tcR/0000755000176200001440000000000013667667012011020 5ustar liggesuserstcR/NAMESPACE0000644000176200001440000000142013667026401012223 0ustar liggesusersuseDynLib(tcR) exportPattern("^[[:alpha:]]+") export('.split.get') importFrom("grDevices", "colorRampPalette", "rainbow") importFrom("graphics", "segments") importFrom("stats", "aggregate", "as.formula", "dist", "na.exclude", "p.adjust", "prcomp", "qnorm", "quantile", "rmultinom", "runif", "sd") importFrom(Rcpp, evalCpp, cppFunction) importFrom(stringdist, stringdist) import(grid) importFrom(gridExtra, grid.arrange, arrangeGrob) import(ggplot2) importFrom(reshape2, melt, dcast, acast) importFrom(data.table, data.table, as.data.table, setnames, setkeyv, setattr) importFrom(dplyr, group_by, group_by_, grouped_df, summarise, summarise_, select, select_) importFrom(gtable, gtable_filter) importFrom("stats", "lm") import(utils) import(igraph) import(scales)tcR/README.md0000644000176200001440000000306513667224201012270 0ustar liggesusers[![Downloads_all](http://cranlogs.r-pkg.org/badges/grand-total/tcR)](http://www.r-pkg.org/pkg/tcR) [![Downloads_week](http://cranlogs.r-pkg.org/badges/last-week/tcR)](http://www.r-pkg.org/pkg/tcR) tcR === **The tcR package is no longer supported and current issues will not be fixed.** **A new package is available that is designed to replace tcR called immunarch: https://immunarch.com/** *We have solved most of the problems tcR package had and improved the overall pipeline, providing functions for painless repertoire file parsing and publication-ready plot making. We will be happy to help you to integrate the new package into your pipelines. Please do not hesitate to contact us via emails on https://immunarch.com/ or via issues on https://github.com/immunomind/immunarch, should any question arise.* *Sincerely, immunarch dev team and Vadim I. Nazarov, lead developer* tcR is a platform designed for TCR and Ig repertoire data analysis in R after preprocessing data with software tools for CDR3 extraction and gene segments aligning (MiTCR, MiXCR, MiGEC, ImmunoSEQ, IMSEQ, etc.). With the power and flexibility of R language and procedures supported by tcR users can perform advanced statistical analysis of TCR and Ig repertoires. 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See "Details" for methods.} \item{.quant}{Which column to use for the quantity of clonotypes: "read.count" for the "Read.count" column, "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, "umi.prop" for the "Umi.proportion" column.} \item{.q}{q-parameter for the Diversity index.} \item{.norm}{If T than compute the normsalised entropy.} \item{.do.norm}{One of the three values - NA, T or F. If NA than check for distrubution (sum(.data) == 1) and normalise it with the given laplace correction value if needed. if T then do normalisation and laplace correction. If F than don't do normalisaton and laplace correction.} \item{.laplace}{Value for Laplace correction.} } \description{ General interface to all cloneset diversity functions. } \details{ You can see a more detailed description for each diversity method at \link{diversity}. Parameter \code{.method} can have one of the following value each corresponding to the specific method: - "div" for the true diversity, or the effective number of types (basic function \code{diversity}). - "inv.simp" for the inverse Simpson index (basic function \code{inverse.simpson}). - "gini" for the Gini coefficient (basic function \code{gini}). - "gini.simp" for the Gini-Simpson index (basic function \code{gini.simpson}). - "chao1" for the Chao1 estimator (basic function \code{chao1}). - "entropy" for the Shannon entropy measure (basic function \code{entropy}). } \examples{ \dontrun{ data(twb) twb.div <- repDiversity(twb, "chao1", "read.count") } } \seealso{ \link{diversity}, \link{entropy} } tcR/man/vis.heatmap.Rd0000644000176200001440000000306513667026455014305 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.heatmap} \alias{vis.heatmap} \title{Heatmap.} \usage{ vis.heatmap( .data, .title = "Number of shared clonotypes", .labs = c("Sample", "Sample"), .legend = "Shared clonotypes", .na.value = NA, .text = T, .scientific = FALSE, .signif.digits = 4, .size.text = 4, .no.legend = F, .no.labs = F ) } \arguments{ \item{.data}{Either a matrix with colnames and rownames specifyed or a data.frame with the first column of strings for row names and other columns stands for values.} \item{.title}{Main title of the plot.} \item{.labs}{Labs names. Character vector of length 2 (for naming x-axis and y-axis).} \item{.legend}{Title for the legend.} \item{.na.value}{Replace NAs with this values.} \item{.text}{if T then print \code{.data} values at tiles.} \item{.scientific}{If T then force show scientific values in the heatmap plot.} \item{.signif.digits}{Number of significant digits to show. Default - 4.} \item{.size.text}{Size for the text in the cells of the heatmap, 4 by default.} \item{.no.legend}{If T than remove the legend from the plot.} \item{.no.labs}{If T than remove x / y labels names from the plot.} } \value{ ggplot object. } \description{ Plot a heatmap from a matrix or a data.frame } \examples{ \dontrun{ # Load your data. load('immdata.rda') # Perform cloneset overlap by amino acid sequences with V-segments. imm.av <- repOverlap(immdata, .seq = 'aa', .vgene = T) # Plot a heatmap. vis.heatmap(imm.av, .title = 'Immdata - (ave)-intersection') } } tcR/man/sample.clones.Rd0000644000176200001440000000115013667026401014611 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{sample.clones} \alias{sample.clones} \title{Get a random subset from a data.frame.} \usage{ sample.clones(.data, .n, .replace = T) } \arguments{ \item{.data}{Data.frame or a list with data.frames} \item{.n}{Sample size if integer. If in bounds [0;1] than percent of rows to extract. "1" is a percent, not one row!} \item{.replace}{if T then choose with replacement, else without.} } \value{ Data.frame of nrow .n or a list with such data.frames. } \description{ Sample rows of the given data frame with replacement. } tcR/man/gc.content.Rd0000644000176200001440000000063313667026401014115 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/strtools.R \name{gc.content} \alias{gc.content} \title{GC-content of a nucleotide sequences.} \usage{ gc.content(.nucseq) } \arguments{ \item{.nucseq}{Character vector of nucletoide sequences.} } \value{ Numeric vector of \code{length(.nucseq)}. } \description{ Compute the GC-content (proportion of G-C nucleotide in a sequence). } tcR/man/find.clonotypes.Rd0000644000176200001440000000430013667026455015175 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stats.R \name{find.clonotypes} \alias{find.clonotypes} \title{Find target clonotypes and get columns' value corresponded to that clonotypes.} \usage{ find.clonotypes( .data, .targets, .method = c("exact", "hamm", "lev"), .col.name = "Read.count", .target.col = "CDR3.amino.acid.sequence", .verbose = T ) } \arguments{ \item{.data}{List with mitcr data.frames or a mitcr data.frame.} \item{.targets}{Target sequences or elements to search. Either character vector or a matrix / data frame (not a data table!) with two columns: first for sequences, second for V-segments.} \item{.method}{Method, which will be used to find clonotypes: - "exact" performs exact matching of targets; - "hamm" finds targets and close sequences using hamming distance <= 1; - "lev" finds targets and close sequences using levenshtein distance <= 1.} \item{.col.name}{Character vector with column names which values should be returned.} \item{.target.col}{Character vector specifying name of columns in which function will search for a targets. Only first column's name will be used for matching by different method, others will match exactly. \code{.targets} should be a two-column character matrix or data frame with second column for V-segments.} \item{.verbose}{if T then print messages about the search process.} } \value{ Data.frame. } \description{ Find the given target clonotypes in the given list of data.frames and get corresponding values of desired columns. } \examples{ \dontrun{ # Get ranks of all given sequences in a list of data frames. immdata <- set.rank(immdata) find.clonotypes(.data = immdata, .targets = head(immdata[[1]]$CDR3.amino.acid.sequence), .method = 'exact', .col.name = "Rank", .target.col = "CDR3.amino.acid.sequence") # Find close by levenhstein distance clonotypes with similar V-segments and return # their values in columns 'Read.count' and 'Total.insertions'. find.clonotypes(.data = twb, .targets = twb[[1]][, c('CDR3.amino.acid.sequence', 'V.gene')], .col.name = c('Read.count', 'Total.insertions'), .method = 'lev', .target.col = c('CDR3.amino.acid.sequence', 'V.gene')) } } tcR/man/assymetry.Rd0000644000176200001440000000153013667026401014110 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/infoanalysis.R \name{assymetry} \alias{assymetry} \title{Normalised log assymetry.} \usage{ assymetry(.alpha, .beta = NULL, .by = "CDR3.nucleotide.sequence") } \arguments{ \item{.alpha}{First mitcr data.frame or a list with data.frames.} \item{.beta}{Second mitcr data.frame or NULL if \code{.alpha} is a list.} \item{.by}{Which column use to merge. See "Details".} } \value{ Value of the normalised log assymetry measure for the given data.frames. } \description{ Compute the value of the normalised log assymetry measure for the given data.frames of the counts of shared clones. } \details{ Merge two data frames by the given column and compute value Sum(Log((Percentage for shared clone S from alpha) / (Percentage for shared clone S from beta))) / (# of shared clones). } tcR/man/mutation.network.Rd0000644000176200001440000000340113667026455015410 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/graph.R \name{mutation.network} \alias{mutation.network} \title{Make mutation network for the given repertoire.} \usage{ mutation.network( .data, .method = c("hamm", "lev"), .max.errors = 1, .label.col = "CDR3.amino.acid.sequence", .seg.col = "V.gene", .prob.col = "Probability" ) } \arguments{ \item{.data}{Either character vector of sequences, data frame with \code{.label.col} or shared repertoire (result from the \code{shared.repertoire} function) constructed based on \code{.label.col}.} \item{.method}{Either "hamm" (for hamming distance) or "lev" (for edit distance). Passed to the \code{find.similar.sequences} function.} \item{.max.errors}{Passed to the \code{find.similar.sequences} function.} \item{.label.col}{Name of the column with CDR3 sequences (vertex labels).} \item{.seg.col}{Name of the column with V gene segments.} \item{.prob.col}{Name of the column with clonotype probability.} } \value{ Mutation network, i.e. igraph object with input sequences as vertices labels, ??? } \description{ Mutation network (or a mutation graph) is a graph with vertices representing nucleotide or in-frame amino acid sequences (out-of-frame amino acid sequences will automatically filtered out) and edges are connecting pairs of sequences with hamming distance or edit distance between them no more than specified in the \code{.max.errors} function parameter. } \examples{ \dontrun{ data(twb) twb.shared <- shared.repertoire(twb) G <- mutation.network(twb.shared) get.people.names(G, 300, T) # "Subj.A|Subj.B" get.people.names(G, 300, F) # list(c("Subj.A", "Subj.B")) } } \seealso{ \link{shared.repertoire}, \link{find.similar.sequences}, \link{set.people.vector}, \link{get.people.names} } tcR/man/dot-column.choice.Rd0000644000176200001440000000057713667026455015405 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{.column.choice} \alias{.column.choice} \title{Choose the right column.} \usage{ .column.choice(x, .verbose = T) } \arguments{ \item{x}{Character vector with column IDs.} \item{.verbose}{If T then print the error mesasge.} } \value{ Character. } \description{ Choose the right column. } tcR/man/vis.clonal.space.Rd0000644000176200001440000000117213667026401015214 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.clonal.space} \alias{vis.clonal.space} \title{Visualise occupied by clones homeostatic space among Samples or groups.} \usage{ vis.clonal.space(.clonal.space.data, .groups = NULL) } \arguments{ \item{.clonal.space.data}{Data from the \code{fclonal.space.homeostasis} function.} \item{.groups}{List of named character vector with names of Samples in \code{.clonal.space.data} for grouping them together.} } \value{ ggplot object. } \description{ Visualise which clones how much space occupy. } \seealso{ \link{clonal.space.homeostasis} } tcR/man/vis.rarefaction.Rd0000644000176200001440000000152213667026401015146 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.rarefaction} \alias{vis.rarefaction} \title{Rarefaction statistics visualisation.} \usage{ vis.rarefaction(.muc.res, .groups = NULL, .log = F, .names = T) } \arguments{ \item{.muc.res}{Output from the \code{muc} function.} \item{.groups}{List with names for groups and names of the group members. If NULL than each member is in the individual group.} \item{.log}{if T then log-scale the y axis.} \item{.names}{If T then print number of samples.} } \description{ Plot a line with mean unique clones. } \examples{ \dontrun{ data(twb) names(twb) # "Subj.A" "Subj.B" "Subj.C" "Subj.D" twb.rar <- rarefaction(twb, .col = "Read.count") vis.rarefaction(twb.rar, list(A = c("Subj.A", "Subj.B"), B = c("Subj.C", "Subj.D"))) } } \seealso{ \link{rarefaction} } tcR/man/shared.repertoire.Rd0000644000176200001440000000734713667026401015511 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shared.R \name{shared.repertoire} \alias{shared.repertoire} \alias{shared.matrix} \title{Shared TCR repertoire managing and analysis} \usage{ shared.repertoire(.datalist, .type = 'avrc', .min.ppl = 1, .head = -1, .clear = T, .verbose = T, .by.col = '', .sum.col = '', .max.ppl = length(.datalist)) shared.matrix(.shared.rep) } \arguments{ \item{.datalist}{List with data frames.} \item{.type}{String of length 4 denotes how to create a shared repertoire. See "Details" for more information. If supplied, than parameters \code{.by.col} and \code{.sum.col} will be ignored. If not supplied, than columns in \code{.by.col} and \code{.sum.col} will be used.} \item{.min.ppl}{At least how many people must have a sequence to leave this sequence in the shared repertoire.} \item{.head}{Parameter for the \code{head} function, applied to all data frames before clearing.} \item{.clear}{if T then remove all sequences which have symbols "~" or "*" (i.e., out-of-frame sequences for amino acid sequences).} \item{.verbose}{if T then output progress.} \item{.by.col}{Character vector with names of columns with sequences and their parameters (like segment) for using for creating a shared repertoire.} \item{.sum.col}{Character vector of length 1 with names of the column with count, percentage or any other numeric chaaracteristic of sequences for using for creating a shared repertoire.} \item{.max.ppl}{At most how many people must have a sequence to leave this sequence in the shared repertoire.} \item{.shared.rep}{Shared repertoire.} } \value{ Data frame for \code{shared.repertoire}, matrix for \code{shared.matrix}. } \description{ Generate a repertoire of shared sequences - sequences presented in more than one subject. If sequence is appeared more than once in the one repertoire, than only the first appeared one will be choosed for a shared repertoire. \code{shared.repertoire} - make a shared repertoire of sequences from the given list of data frames. \code{shared.matrix} - leave columns, which related to the count of sequences in people, and return them as a matrix. I.e., this functions will remove such columns as 'CDR3.amino.acid.sequence', 'V.gene', 'People'. } \details{ Parameter \code{.type} is a string of length 4, where: \enumerate{ \item First character stands either for the letter 'a' for taking the "CDR3.amino.acid.sequence" column or for the letter 'n' for taking the "CDR3.nucleotide.sequence" column. \item Second character stands whether or not take the V.gene column. Possible values are '0' (zero) stands for taking no additional columns, 'v' stands for taking the "V.gene" column. \item Third character stands for using either UMIs or reads in choosing the column with numeric characterisitc (see the next letter). \item Fourth character stands for name of the column to choose as numeric characteristic of sequences. It depends on the third letter. Possible values are "c" for the "Umi.count" (if 3rd character is "u") / "Read.count" column (if 3rd character is "r"), "p" for the "Umi.proportion" / "Read.proportion" column, "r" for the "Rank" column or "i" for the "Index" column. If "Rank" or "Index" isn't in the given repertoire, than it will be created using \code{set.rank} function using "Umi.count" / "Read.count" column. } } \examples{ \dontrun{ # Set "Rank" column in data by "Read.count" column. # This is doing automatically in shared.repertoire() function # if the "Rank" column hasn't been found. immdata <- set.rank(immdata) # Generate shared repertoire using "CDR3.amino.acid.sequence" and # "V.gene" columns and with rank. imm.shared.av <- shared.repertoire(immdata, 'avrc') } } \seealso{ \link{shared.representation}, \link{set.rank} } tcR/man/kmer.table.Rd0000644000176200001440000000323013667026401014073 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/kmers.R \name{kmer.table} \alias{kmer.table} \alias{get.kmer.column} \title{Make and manage the table of the most frequent k-mers.} \usage{ kmer.table(.data, .heads = c(10, 100, 300, 1000, 3000, 10000, 30000), .k = 5, .nrow = 20, .clean = T, .meat = F) get.kmer.column(.kmer.table.list, .head) } \arguments{ \item{.data}{tcR data.frame or a list with tcR data.frames.} \item{.heads}{Vector of parameter for the \code{head()} function, supplied sequentialy to the \code{get.kmers()} function. -1 means all rows.} \item{.k}{Size of the kmer.} \item{.nrow}{How many most frequent k-mers include to the output table.} \item{.clean}{Parameter for the \code{get.kmers()} function.} \item{.meat}{Parameter for the \code{get.kmers()} function.} \item{.kmer.table.list}{Result from the \code{kmer.table} function if \code{.data} supplied as a list.} \item{.head}{Which columns with this head return.} } \value{ \code{kmer.table} - if \code{.data} is a data frame, than data frame with columns like "Kmers.X", "Count.X" where X - element from \code{.heads}. If \code{.data} is a list, than list of such data frames. \code{get.kmer.column} - data frame with first column with kmers and other columns named as a names of data frames, from which \code{.kmer.table.list} was generated. } \description{ \code{kmer.table} - generate table with the most frequent k-mers. \code{get.kmer.column} - get vector of k-mers from the k-mer table from the function \code{kmer.table} } \examples{ \dontrun{ twb.kmers <- kmer.table(twb, .heads = c(5000, 10000), .meat = T) head(get.kmer.column(twb.kmers, 10000)) } } tcR/man/vis.count.len.Rd0000644000176200001440000000215313667026401014557 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.count.len} \alias{vis.count.len} \title{Plot a histogram of lengths.} \usage{ vis.count.len(.data, .ncol = 3, .name = "", .col = "Read.count") } \arguments{ \item{.data}{Data frame with columns 'CDR3.nucleotide.sequence' and 'Read.count' or list with such data frames.} \item{.ncol}{If .data is a list, than number of columns in a grid of histograms for each data frame in \code{.data}. Else not used.} \item{.name}{Title for this plot.} \item{.col}{Name of the column to use in computing the lengths distribution.} } \value{ ggplot object. } \description{ Plot a histogram of distribution of lengths of CDR3 nucleotide sequences. On y-axis are sum of read counts for each length. } \details{ If \code{.data} is a data frame, than one histogram will be plotted. Is \code{.data} is a list, than grid of histograms will be plotted. } \examples{ \dontrun{ load('immdata.rda') # Plot one histogram with main title. vis.count.len(immdata[[1]], 'Main title here') # Plot a grid of histograms with 2 columns. vis.count.len(immdata, 2) } } tcR/man/get.kmers.Rd0000644000176200001440000000225213667026455013762 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/kmers.R \name{get.kmers} \alias{get.kmers} \title{Get kmers from sequences.} \usage{ get.kmers( .data, .head = -1, .k = 5, .clean = T, .meat = F, .verbose = T, .left.shift = 0, .right.shift = 0 ) } \arguments{ \item{.data}{Either character vector or a data.frame.} \item{.head}{Parameter for head function applied to the given data before kmer generation.} \item{.k}{Size of the kmer.} \item{.clean}{if T then remove sequences which contain '~' or '*' symbols. Useful for deleting out-of-frame aminoacid sequnces.} \item{.meat}{if TRUE than .data must be data.frame with columns CDR3.amino.acid.sequence and Read.count.} \item{.verbose}{if T then print progress.} \item{.left.shift}{Cut all \code{.left.shift} symbols from the left side for each sequence.} \item{.right.shift}{Cut all \code{.right.shift} symbols from the right side for each sequence.} } \value{ Data.frame with 2 columns Kmers and Count / Rank / Proportion relatively to the .value param or a list with such data.frames if .data is a list. } \description{ Get vector of kmers from the given character vector or data frame. } tcR/man/contamination.stats.Rd0000644000176200001440000000354313667026401016056 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/filters.R \name{contamination.stats} \alias{contamination.stats} \alias{decontamination} \title{Contamination filtering.} \usage{ contamination.stats(.data1, .data2, .limit = 20, .col = 'Read.count') decontamination(.data1, .data2, .limit = 20, .col = 'Read.count', .symm = T) } \arguments{ \item{.data1}{First data frame with columns 'CDR3.nucleotide.sequence' and 'Read.count'. Will be checked for contamination.} \item{.data2}{Second data frame with such columns. Will be used for checking for sequences which contaminated the first one.} \item{.limit}{Parameter for filtering: all sequences from \code{.data1} which are presented in \code{.data2} and (count of in \code{.data2}) / (count of seq in \code{.data1}) >= \code{.limit} are removed.} \item{.col}{Column's name with clonal count.} \item{.symm}{if T then perform filtering out of sequences in .data1, and then from .data2. Else only from .data1.} } \value{ Filtered \code{.data1} or a list with filtered both \code{.data1} and \code{.data2}. } \description{ Occasionally DNA or RNA libraries are contaminate each other. To address this issue and estimate contamination rate \code{tcR} offers \code{contamination.stats} and \code{decontamination} functions. The \code{decontamination} function received data (either data frame or a list with data frames) and a limit for clonal proportion as arguments. Script searches for a similar clones to the first data frame in the other (or performs pairwise searches if the given data is a list) and removes clones from the first data frame, which has been found in the second one with counts less or equal to 10 * counts of similar clones in the first one. Function \code{contamination.stats} will return the number of clones which will be removed with the \code{contamination.stats} function. } tcR/man/set.rank.Rd0000644000176200001440000000124413667026401013577 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataproc.R \name{set.rank} \alias{set.rank} \alias{set.index} \title{Set new columns "Rank" and "Index".} \usage{ set.rank(.data, .col = "Read.count") } \arguments{ \item{.data}{Data frame or list with data frames.} \item{.col}{Character vector with name of the column to use for ranking or indexing.} } \value{ Data frame with new column "Rank" or "Index" or list with such data frames. } \description{ Set new columns "Rank" and "Index": set.rank <==> .data$Rank = rank(.data[, .col], ties.method = 'average') set.index <==> .data$Index = 1:nrow(.data) in a sorted data frame by \code{.col} } tcR/man/rarefaction.Rd0000644000176200001440000000357613667026455014372 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/diversity.R \name{rarefaction} \alias{rarefaction} \title{Diversity evaluation using rarefaction.} \usage{ rarefaction( .data, .step = NA, .quantile = c(0.025, 0.975), .extrapolation = 2e+05, .col = "Umi.count", .verbose = T ) } \arguments{ \item{.data}{Data frame or a list with data frames.} \item{.step}{Step's size. By default - minimal repertoire size divided by 50.} \item{.quantile}{Numeric vector of length 2 with quantiles for confidence intervals.} \item{.extrapolation}{If N > 0 than perform extrapolation of all samples to the size of the max one +N reads or UMIs. By default - 200000.} \item{.col}{Column's name from which choose frequency of each clone.} \item{.verbose}{if T then print progress bar.} } \value{ Data frame with first column for sizes, second columns for the first quantile, third column for the mean, fourth columns for the second quantile, fifth columns for the name of subject. } \description{ Sequentially resample the given data with growing sample size the given data and compute mean number of unique clones. For more details on the procedure see "Details". } \details{ This subroutine is designed for diversity evaluation of repertoires. On each step it computes a mean unique clones from sample of fixed size using bootstrapping. Unique clones for each sample from bootstrap computed as a number of non-zero elements in a vector from multinomial distribution with input vector of probabilities from the \code{.col} column using function \code{rmultinom} with parameters n = .n, size = i * .step, prob = .data[, .col] (i is an index of current iteration) and choosing for lower and upper bound \code{quantile} bounds of the computed distribution of unique clones. } \examples{ \dontrun{ rarefaction(immdata, .col = "Read.count") } } \seealso{ \link{vis.rarefaction} \link{rmultinom} } tcR/man/vis.shared.clonotypes.Rd0000644000176200001440000000454513667026455016336 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.shared.clonotypes} \alias{vis.shared.clonotypes} \title{Visualisation of shared clonotypes occurrences among repertoires.} \usage{ vis.shared.clonotypes( .shared.rep, .x.rep = NA, .y.rep = NA, .title = NA, .ncol = 3, .point.size.modif = 1, .cut.axes = T, .density = T, .lm = T, .radj.size = 3.5, .plot = T ) } \arguments{ \item{.shared.rep}{Shared repertoires, as from \link{shared.repertoire} function.} \item{.x.rep}{Which repertoire show on x-axis. Either a name or an index of a repertoire in the \code{.shared.rep} or NA to choose all repertoires.} \item{.y.rep}{Which repertoire show on y-axis. Either a name or an index of a repertoire in the \code{.shared.rep} or NA to choose all repertoires.} \item{.title}{Main title of the plot.} \item{.ncol}{Number of columns in the resulting plot.} \item{.point.size.modif}{Modify this to correct sizes of points.} \item{.cut.axes}{If T than cut axes' limits to show only frequencies that exists.} \item{.density}{If T than plot densities of shared and unique clonotypes.} \item{.lm}{If T than fit and plot a linear model to shared clonotypes.} \item{.radj.size}{Size of the text for R^2-adjusted.} \item{.plot}{If F than return grobs instead of plotting.} } \value{ ggplot2 object or plot } \description{ Visualise counts or proportions of shared clonotypes among repertoires. Code adapted from https://www.r-bloggers.com/ggplot2-cheatsheet-for-visualizing-distributions/. } \examples{ \dontrun{ data(twb) # Show shared nucleotide clonotypes of all possible pairs # using the Read.proportion column twb.sh <- shared.repertoire(twb, "n0rp") vis.shared.clonotypes(twb.sh, .ncol = 4) # Show shared amino acid + Vseg clonotypes of pairs # including the Subj.A (the first one) using # the Read.count column. twb.sh <- shared.repertoire(twb, "avrc") vis.shared.clonotypes(twb.sh, 1, NA, .ncol = 4) # same, just another order of axis vis.shared.clonotypes(twb.sh, NA, 1, .ncol = 4) # Show shared nucleotide clonotypes of Subj.A (the first one) # Subj.B (the second one) using the Read.proportion column. twb.sh <- shared.repertoire(twb, "n0rp") vis.shared.clonotypes(twb.sh, 1, 2) # Show the same plot, but with much larget points. vis.shared.clonotypes(twb.sh, 1, 2, .point.size.modif = 3) } } \seealso{ \link{shared.repertoire} } tcR/man/entropy.seg.Rd0000644000176200001440000000420013667026401014322 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/infoanalysis.R \name{entropy.seg} \alias{entropy.seg} \alias{js.div.seg} \title{Repertoires' analysis using information measures applied to V- and J- segment frequencies.} \usage{ entropy.seg(.data, .genes = HUMAN_TRBV, .frame = c('all', 'in', 'out'), .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), .ambig = F) js.div.seg(.data, .genes = HUMAN_TRBV, .frame = c('all', 'in', 'out'), .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), .norm.entropy = T, .ambig = F, .verbose = F, .data2 = NULL) } \arguments{ \item{.data}{Mitcr data.frame or a list with mitcr data.frames.} \item{.genes}{Parameter to the \code{geneUsage} function.} \item{.frame}{Character vector of length 1 specified which *-frames should be used: only in-frame ('in'), out-of-frame ('out') or all sequences ('all').} \item{.quant}{Which column to use for the quantity of clonotypes: NA for computing only number of genes without using clonotype counts, "read.count" for the "Read.count" column, "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, "umi.prop" for the "Umi.proportion" column.} \item{.ambig}{Parameter passed to \code{geneUsage}.} \item{.data2}{NULL if .data is a list, or a second mitcr data.frame.} \item{.norm.entropy}{if T then divide result by mean entropy of 2 segments' frequencies.} \item{.verbose}{If T than output the data processing progress bar.} } \value{ For \code{entropy.seg} - numeric integer with entropy value(s). For \code{js.div.seg} - integer of vector one if \code{.data} and \code{.data2} are provided; esle matrix length(.data) X length(.data) if \code{.data} is a list. } \description{ Information approach to repertoire analysis. Function \code{entropy.seg} applies Shannon entropy to V-usage and hence measures variability of V-usage. Function \code{js.div.seg} applied Jensen-Shannon divergence to V-usage of two or more data frames and hence measures distance among this V-usages. } \seealso{ \link{vis.heatmap}, \link{vis.group.boxplot}, \link{geneUsage} } tcR/man/pca.segments.Rd0000644000176200001440000000213113667026401014435 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/segments.R \name{pca.segments} \alias{pca.segments} \alias{pca.segments.2D} \title{Perform PCA on segments frequency data.} \usage{ pca.segments(.data, .cast.freq.seg = T, ..., .text = T, .do.plot = T) pca.segments.2D(.data, .cast.freq.seg = T, ..., .text = T, .do.plot = T) } \arguments{ \item{.data}{Either data.frame or a list of data.frame or a result obtained from the \code{geneUsage} function.} \item{.cast.freq.seg}{if T then apply code{geneUsage} to the supplied data.} \item{...}{Further arguments passed to \code{prcomp} or \code{geneUsage}.} \item{.text}{If T then plot sample names in the resulting plot.} \item{.do.plot}{if T then plot a graphic, else return a pca object.} } \value{ If .do.plot is T than ggplot object; else pca object. } \description{ Perform PCA on gene segments frequency data for V- and J-segments and either return pca object or plot the results. } \examples{ \dontrun{ # Load the twins data. data(twb) # Plot a plot of results of PCA on V-segments usage. pca.segments(twb, T, scale. = T) } } tcR/man/check.distribution.Rd0000644000176200001440000000226513667026455015662 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{check.distribution} \alias{check.distribution} \title{Check for adequaty of distrubution.} \usage{ check.distribution( .data, .do.norm = NA, .laplace = 1, .na.val = 0, .warn.zero = F, .warn.sum = T ) } \arguments{ \item{.data}{Numeric vector of values.} \item{.do.norm}{One of the three values - NA, T or F. If NA than check for distrubution (sum(.data) == 1) and normalise if needed with the given laplace correction value. if T then do normalisation and laplace correction. If F than don't do normalisaton and laplace correction.} \item{.laplace}{Value for laplace correction.} \item{.na.val}{Replace all NAs with this value.} \item{.warn.zero}{if T then the function checks if in the resulted vector (after normalisation) are any zeros, and print a warning message if there are some.} \item{.warn.sum}{if T then the function checks if the sum of resulted vector (after normalisation) is equal to one, and print a warning message if not.} } \value{ Numeric vector. } \description{ Check if the given .data is a distribution and normalise it if necessary with optional laplace correction. } tcR/man/beta.prob.Rd0000644000176200001440000000401713667026455013740 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/docdata.R \docType{data} \name{beta.prob} \alias{beta.prob} \title{List with assembling probabilities of beta chain TCRs.} \format{ \code{beta.prob} is a list of matrices and data frames. } \description{ \code{beta.prob} is a list with probabilities of TCR assembling taken from \code{Murugan et al. Statistical inference of the generation probability of T-cell receptors from sequence repertoires}. It's a list with the following elements: - P.V - matrix with 1 column and row names stands for V-beta segments. Each element is a probability of choosing corresponding V-beta segment. - P.del.D1 - matrix 17x17 with probabilities of choosing D5-D3 deletions for TRBD1. - P.del.D1 - matrix 17x17 with probabilities of choosing D5-D3 deletions for TRBD2. - P.ins.len - matrix with first columns stands for number of insertions, second and third columns filled with probability values of choosing corresponding number of insertions in VD- and DJ-junctions correspondingly. - P.ins.nucl - data frame with probability of choosing a nucleotide in the insertion on junctions with known previous nucleotide. First column with names of nucleotides, 2-5 columns are probabilities of choosing adjacent nucleotide in VD-junction, 6-9 columns are probabilities of choosing adjacent nucleotide in DJ-junction. - P.del.J - matrix with the first column "P.del.V" with number of deletions, and other columns with names for V-segments and with probabilities of corresponding deletions. - P.del.J - matrix with the first column "P.del.J" with number of deletions, and other columns with names for J-segments and with probabilities of corresponding deletions. - P.J.D - matrix with two columns ("TRBD1" and "TRBD2") and 13 rows with row names stands for J-beta segments. Each element is a mutual probability of choosing J-D segments. } \examples{ \dontrun{ # Generate 10 kmers with adjacent nucleotide probability. generate.kmers.prob(rep.int(10, 10), .probs=beta.prob$P.ins.nucl[,c(1, 2:5)]) } } tcR/man/generate.kmers.Rd0000644000176200001440000000231713667026401014766 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/kmers.R \name{generate.kmers} \alias{generate.kmers} \alias{generate.kmers.prob} \title{Generate k-mers.} \usage{ generate.kmers(.k, .seq = '', .alphabet = c('A', 'C', 'G', 'T')) generate.kmers.prob(.k, .probs, .count = 1, .alphabet = c('A', 'C', 'G', 'T'), .last.nucl = 'X') } \arguments{ \item{.k}{Size of k-mers or either integer or vector with k-s for generate.kmers.prob.} \item{.seq}{Prefix of all generated k-mers.} \item{.alphabet}{Alphabet.} \item{.probs}{Matrix with probabilities for generating adjacent symbol with |alphabet| X |alphabet| size. Order of letters is given in the \code{.alphabet} parameter.} \item{.count}{Number of kmers to be generated.} \item{.last.nucl}{Adjacent nucleotide from which start generation. If 'X' than choose one of the nucleotides with equal probabilities.} } \value{ Vector of all possible k-mers for \code{generate.kmers} or a vector of generated kmers for \code{generate.kmers.prob}. } \description{ Generate all k-mers. starting with the given sequence on the given alphabet Generate k-mers with the given k and probabilities of nucleotides next to each other (markov chain). } tcR/man/vis.top.proportions.Rd0000644000176200001440000000130313667026455016056 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.top.proportions} \alias{vis.top.proportions} \title{Visualisation of top clones proportions.} \usage{ vis.top.proportions( .data, .head = c(10, 100, 1000, 10000, 30000, 1e+05, 3e+05, 1e+06), .col = "Read.count" ) } \arguments{ \item{.data}{Data frame with clones.} \item{.head}{Integer vector of clones for the \code{.head} parameter for the \code{top.proportion} function.} \item{.col}{Parameter \code{.col} for the \code{top.proportion} function.} } \description{ Visualisation of proportion of the top clones. } \examples{ \dontrun{ vis.top.proportions(immdata) } } \seealso{ \code{top.proportion} } tcR/man/gibbs.sampler.Rd0000644000176200001440000000115713667026401014605 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/kmers.R \name{gibbs.sampler} \alias{gibbs.sampler} \title{Gibbs Sampler.} \usage{ gibbs.sampler(.data, .k = 5, .niter = 500) } \arguments{ \item{.data}{Vector of characters or data.frame of characters (1st col) and their numbers (2nd col) if .meat == T.} \item{.k}{Motif's length.} \item{.niter}{Number of iterations.} } \value{ Vector of possible motifs. } \description{ Perform the Gibbs Sampler method for finding frequent motifs in the given vector of strings or data.frame. Each string splitted to kmers with the given length of motif. } tcR/man/generate.tcr.Rd0000644000176200001440000000370013667026455014443 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataproc.R \name{generate.tcr} \alias{generate.tcr} \title{Generate random nucleotide TCR sequences.} \usage{ generate.tcr( .count = 1, .chain = c("beta", "alpha"), .segments, .P.list = if (.chain[1] == "alpha") alpha.prob else beta.prob ) } \arguments{ \item{.count}{Number of TCR sequences to generate.} \item{.chain}{Either "alpha" or "beta" for alpha and beta chain respectively.} \item{.segments}{List of segments (see "Details").} \item{.P.list}{List of probabilities (see "Details").} } \value{ Mitcr data.frame with generated sequences. } \description{ Given the list of probabilities and list of segments (see "Details"), generate a artificial TCR repertoire. } \details{ For the generation of a artifical TCR repertoire user need to provide two objects: the list with segments and the list with probabilities. List with segments is a list of 5 elements with 5 names: "TRAV", "TRAJ", "TRBV", "TRBD", "TRBJ". Each element is a data frame with following columns (order is matters!): "V.allelles" with names for V-segments (for TRAV and TRBV; for others is "J.allelles" or "D.allelles"), "CDR3.position" (the function doesn't use it, but you should provide it, fill it with zeros, for example), "Full.nucleotide.sequence" (the function doesn't use it), "Nucleotide.sequence" (function uses it for getting nucleotide sequences of segments) and "Nucleotide.sequence.P" (the function doesn't use it). List with probabilities is quite complicated, so just call \code{data(beta.prob)} for beta chain probabilities (alpha chain probabilities will be added soon). } \examples{ \dontrun{ # Load list of segments provided along with tcR. data(genesegments) # Load list of probabilities provided along with tcR. data(beta.prob) # Generate repertoire of beta chian with 10000 sequences. artif.rep <- generate.tcR(10000, 'beta') View(artif.rep) } } \seealso{ \link{genesegments} \link{beta.prob} } tcR/man/matrixSubgroups.Rd0000644000176200001440000000165413667026401015275 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{matrixSubgroups} \alias{matrixSubgroups} \title{Get all values from the matrix corresponding to specific groups.} \usage{ matrixSubgroups(.mat, .groups = NA, .symm = T, .diag = F) } \arguments{ \item{.mat}{Input matrix with row and columns names.} \item{.groups}{Named list with character vectors for names of elements for each group.} \item{.symm}{If T than remove symmetrical values from the input matrix.} \item{.diag}{If .symm if T and .diag is F than remove diagonal values.} } \description{ Split all matrix values to groups and return them as a data frame with two columns: for values and for group names. } \examples{ \dontrun{ data(twb) ov <- repOverlap(twb) sb <- matrixSubgroups(ov, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))); vis.group.boxplot(sb) } } \seealso{ \link{repOverlap}, \link{vis.group.boxplot} } tcR/man/apply.symm.Rd0000644000176200001440000000176213667026401014170 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{apply.symm} \alias{apply.symm} \alias{apply.asymm} \title{Apply function to every pair of data frames from a list.} \usage{ apply.symm(.datalist, .fun, ..., .diag = NA, .verbose = T) apply.asymm(.datalist, .fun, ..., .diag = NA, .verbose = T) } \arguments{ \item{.datalist}{List with some data.frames.} \item{.fun}{Function to apply, which return basic class value.} \item{...}{Arguments passsed to .fun.} \item{.diag}{Either NA for NA or something else != NULL for .fun(x,x).} \item{.verbose}{if T then output a progress bar.} } \value{ Matrix with values M[i,j] = fun(datalist[i], datalist[j]) } \description{ Apply the given function to every pair in the given datalist. Function either symmetrical (i.e. fun(x,y) == fun(y,x)) or assymmetrical (i.e. fun(x,y) != fun(y,x)). } \examples{ \dontrun{ # equivalent to intersectClonesets(immdata, 'a0e') apply.symm(immdata, intersectClonesets, .type = 'a0e') } } tcR/man/cosine.sharing.Rd0000644000176200001440000000374213667036420014773 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shared.R \name{cosine.sharing} \alias{cosine.sharing} \alias{shared.representation} \alias{shared.clones.count} \alias{shared.summary} \title{Shared repertoire analysis.} \usage{ cosine.sharing(.shared.rep, .log = T) shared.representation(.shared.rep) shared.clones.count(.shared.rep) shared.summary(.shared.rep, .min.ppl = min(.shared.rep$People), .max.ppl = max(.shared.rep$People)) } \arguments{ \item{.shared.rep}{Shared repertoire, obtained from the function \code{shared.repertoire}.} \item{.log}{if T then apply log to the after adding laplace correction equal to one.} \item{.min.ppl}{Filter: get sequences with # people >= .min.ppl.} \item{.max.ppl}{Filter: get sequences with # people <= .max.ppl.} } \value{ Plot or PCA resulr for the \code{shared.seq.pca} function or a matrix with cosine similarity values for the \code{cosine.sharing} function. } \description{ Functions for computing statistics and analysis of shared repertoire of sequences. \code{cosine.sharing} - apply the cosine similarity measure to the vectors of sequences' counts or indices. \code{shared.representation} - for every repertoire in the shared repetoire get a number of sequences in this repertoire which are in the other repertoires. Row names of the input matrix is the number of people. \code{shared.clones.count} - get the number of shared clones for every number of people. \code{shared.summary} - get a matrix with counts of pairwise shared sequences (like a result from \code{cross} function, applied to a list of data frames). } \examples{ \dontrun{ # Load the twb data. data(twb) # Create shared repertoire on the twins data using CDR3 amino acid sequences with CDR1-2. twb.shared <- shared.repertoire(twb, 'av', .verbose = T) sh.repr <- shared.representation(twb.shared) sh.repr # Get proportion of represented shared sequences. apply(sh.repr, 2, function (col) col / col[1]) } } \seealso{ \link{shared.repertoire} } tcR/man/set.people.vector.Rd0000644000176200001440000000223613667026401015433 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/graph.R \name{set.people.vector} \alias{set.people.vector} \alias{get.people.names} \title{Set and get attributes of a mutation network related to source people.} \usage{ set.people.vector(.G, .shared.rep) get.people.names(.G, .V = V(.G), .paste = T) } \arguments{ \item{.G}{Mutation network.} \item{.shared.rep}{Shared repertoire.} \item{.V}{Indices of vertices.} \item{.paste}{If TRUE than concatenate people names to one string, else get a character vector of names.} } \value{ New graph with 'people' and 'npeople' vertex attributes or character vector of length .V or list of length .V. } \description{ Set vertice attributes 'people' and 'npeople' for every vertex in the given graph. Attribute 'people' is a binary string indicating in which repertoire sequence are found. Attribute 'npeople' is a integer indicating number of repertoires, in which this sequence has been found. } \examples{ \dontrun{ data(twb) twb.shared <- shared.repertoire(twb) G <- mutation.network(twb.shared) get.people.names(G, 300, T) # "Subj.A|Subj.B" get.people.names(G, 300, F) # list(c("Subj.A", "Subj.B")) } } tcR/man/get.all.substrings.Rd0000644000176200001440000000115313667026401015601 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/strtools.R \name{get.all.substrings} \alias{get.all.substrings} \title{Get all substrings for the given sequence.} \usage{ get.all.substrings(.seq, .min.len = 3, .table = T) } \arguments{ \item{.seq}{Sequence for splitting to substrings.} \item{.min.len}{Minimal length of output sequences.} \item{.table}{if T then return data frame with substrings and positions of their ends in the .seq.} } \value{ Character vector or data frame with columns "Substring", "Start" and "End". } \description{ Get all substrings for the given sequence. } tcR/man/barcodes.to.reads.Rd0000644000176200001440000000072213667026401015352 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataproc.R \name{barcodes.to.reads} \alias{barcodes.to.reads} \title{Rearrange columns with counts for clonesets.} \usage{ barcodes.to.reads(.data) } \arguments{ \item{.data}{Data frame with columns "Umi.count" and "Read.count".} } \value{ Data frame with new "Read.count" and "Percentage" columns. } \description{ Replace Read.count with Umi.count, recompute Percentage and sort data. } tcR/man/dot-add.legend.Rd0000644000176200001440000000123413667026455014633 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{.add.legend} \alias{.add.legend} \title{Internal function. Add legend to a grid of plots and remove legend from all plots of a grid.} \usage{ .add.legend(.vis.list, .vis.nrow = 2, .legend.ncol = 1) } \arguments{ \item{.vis.list}{A list with ggplot2 plots.} \item{.vis.nrow}{Number of rows of the resulting grid with plots.} \item{.legend.ncol}{Number of columns in the shared legend.} } \description{ Given a list of ggplot2 plots, remove legend from each of them and return grid of such plots plus legend from the first vis. Suitable for plots with similar legends. } tcR/man/ozScore.Rd0000644000176200001440000000252713667026455013514 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/crosses.R \name{ozScore} \alias{ozScore} \title{Overlap Z-score.} \usage{ ozScore(.mat, .symm = T, .as.matrix = F, .val.col = c("norm", "abs", "oz")) } \arguments{ \item{.mat}{Matrix with overlap values.} \item{.symm}{If T then remove lower triangle matrix from counting. Doesn't work if the matrix has different number of rows and columns.} \item{.as.matrix}{If T then return} \item{.val.col}{If .as.matrix is T then this is a name of the column to build matrix upon: either "oz" for the OZ-score column, "abs" for the absolute OZ-score column, or "norm" for the normalised absolute OZ-score column.} } \description{ Compute OZ-scores ("overlap Z scores") for values in the given matrix of overlaps, i.e.,. for each value compute the number of standart deviations from the mean of the matrix. } \examples{ \dontrun{ data(twb) mat <- repOverlap(twb) ozScore(mat) # Take 3x3 matrix ozScore(mat[1:3, 1:3]) # Return as matrix with OZ scores ozmat <- ozScore(mat, T, T, 'oz') # Return as matrix with normalised absolute OZ scores oznorm <- ozScore(mat, T, T, 'norm') # Plot it as boxplots sb <- matrixSubgroups(oznorm, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))); vis.group.boxplot(sb) } } \seealso{ \link{repOverlap}, \link{intersectClonesets}, \link{permutDistTest} } tcR/man/top.fun.Rd0000644000176200001440000000272713667026401013452 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{top.fun} \alias{top.fun} \alias{slice_fun} \title{Get samples from a repertoire slice-by-slice or top-by-top and apply function to them.} \usage{ top.fun(.data, .n, .fun, ..., .simplify = T) slice_fun(.data, .size, .n, .fun, ..., .simplify = T) } \arguments{ \item{.data}{Data.frame, matrix, vector or any enumerated type or a list of this types.} \item{.n}{Vector of values passed to head function for top.fun or the number of slices for slice_fun.} \item{.fun}{Funtions to apply to every sample subset. First input argument is a data.frame, others are passed as \code{...}.} \item{...}{Additional parameters passed to the .fun.} \item{.simplify}{if T then try to simplify result to a vector or to a matrix if .data is a list.} \item{.size}{Size of the slice for sampling for slice_fun.} } \value{ List of length length(.n) for top.fun or .n for slice_fun. } \description{ Functions for getting samples from data frames either by consequently applying head functions (\code{top.fun}) or by getting equal number of rows in the moving window (\code{slice_fun}) and applying specified function to this samples. } \examples{ \dontrun{ # Get entropy of V-usage for the first 1000, 2000, 3000, ... clones. res <- top.fun(immdata[[1]], 1000, entropy.seg) # Get entropy of V-usage for the interval of clones with indices [1,1000], [1001,2000], ... res <- top.fun(immdata[[1]], 1000, entropy.seg) } } tcR/man/column.summary.Rd0000644000176200001440000000330613667026401015044 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stats.R \name{column.summary} \alias{column.summary} \alias{insertion.stats} \title{Columns statistics.} \usage{ column.summary(.data, .factor.col, .target.col, .alphabet = NA, .remove.neg = T) insertion.stats(.data) } \arguments{ \item{.data}{Data frame with columns \code{.factor.col} and \code{target.col}} \item{.factor.col}{Columns with factors by which the data will be divided to subsets.} \item{.target.col}{Column with numeric values for computing summaries after dividing the data to subsets.} \item{.alphabet}{Character vector of factor levels. If NA than use all possible elements frim the \code{.factor.col} column.} \item{.remove.neg}{Remove all elements which >-1 from the \code{.target.col} column.} } \value{ Data.frame with first column with levels of factors and 5 columns with output from the \code{summary} function. } \description{ \code{column.summary} - general function for computing summary statistics (using the \code{summary} function) for columns of the given mitcr data.frame: divide \code{.factor.column} by factors from \code{.alphabet} and compute statistics of correspondingly divided \code{.target.column}. \code{insertion.stats} - compute statistics of insertions for the given mitcr data.frame. } \examples{ \dontrun{ # Compute summary statistics of VD insertions # for each V-segment using all V-segments in the given data frame. column.summary(immdata[[1]], 'V.gene', 'Total.insertions') # Compute summary statistics of VD insertions for each V-segment using only V-segments # from the HUMAN_TRBV_MITCR column.summary(immdata[[1]], 'V.gene', 'Total.insertions', HUMAN_TRBV_MITCR) } } \seealso{ \link{summary} } tcR/man/loglikelihood.Rd0000644000176200001440000000117113667026401014676 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/measures.R \name{loglikelihood} \alias{loglikelihood} \title{Log-likelihood.} \usage{ loglikelihood(.data, .base = 2, .do.norm = NA, .laplace = 1e-12) } \arguments{ \item{.data}{Vector for distribution or counts.} \item{.base}{Logarightm's base for the loglikelihood.} \item{.do.norm}{Parameter to the \code{check.distribution} function.} \item{.laplace}{Laplace correction, Parameter to the \code{check.distribution} function.} } \value{ Loglikelihood value. } \description{ Compute the log-likelihood of the given distribution or vector of counts. } tcR/man/set.group.vector.Rd0000644000176200001440000000263713667026401015310 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/graph.R \name{set.group.vector} \alias{set.group.vector} \alias{get.group.names} \title{Set group attribute for vertices of a mutation network} \usage{ set.group.vector(.G, .attr.name, .groups) get.group.names(.G, .attr.name, .V = V(.G), .paste = T) } \arguments{ \item{.G}{Mutation network.} \item{.attr.name}{Name of the new vertex attribute.} \item{.groups}{List with integer vector with indices of subjects for each group.} \item{.V}{Indices of vertices.} \item{.paste}{if T then return character string with concatenated group names, else return list with character vectors with group names.} } \value{ igraph object with new vertex attribute \code{.attr.name} with binary strings for \code{set.group.vector}. Return character vector for \code{get.group.names}. } \description{ asdasd } \examples{ \dontrun{ data(twb) twb.shared <- shared.repertoire(twb) G <- mutation.network(twb.shared) G <- set.group.vector(G, "twins", list(A = c(1,2), B = c(3,4))) # <= refactor this get.group.names(G, "twins", 1) # "A|B" get.group.names(G, "twins", 300) # "A" get.group.names(G, "twins", 1, F) # list(c("A", "B")) get.group.names(G, "twins", 300, F) # list(c("A")) # Because we have only two groups, we can assign more readable attribute. V(G)$twin.names <- get.group.names(G, "twins") V(G)$twin.names[1] # "A|B" V(G)$twin.names[300] # "A" } } tcR/man/top.cross.Rd0000644000176200001440000000462713667026401014014 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stats.R \name{top.cross} \alias{top.cross} \alias{top.cross.vec} \alias{top.cross.plot} \title{Perform sequential cross starting from the top of a data frame.} \usage{ top.cross(.data, .n = NA, .data2 = NULL, .type = 'ave', .norm = F, .verbose = T) top.cross.vec(.top.cross.res, .i, .j) top.cross.plot(.top.cross.res, .xlab = 'Top X clonotypes', .ylab = 'Normalised number of shared clonotypes', .nrow = 2, .legend.ncol = 1, .logx = T, .logy = T) } \arguments{ \item{.data}{Either list of data.frames or a data.frame.} \item{.n}{Integer vector of parameter appled to the head function; same as .n in the top.fun function. See "Details" for more information.} \item{.data2}{Second data.frame or NULL if .data is a list.} \item{.type}{Parameter .type to the \code{tcR::intersect} function.} \item{.norm}{Parameter .norm to the \code{tcR::intersect} function.} \item{.verbose}{if T then plot a progress bar.} \item{.top.cross.res}{Result from the \code{top.cross} function.} \item{.i, .j}{Coordinate of a cell in each matrix.} \item{.xlab}{Name for a x-lab.} \item{.ylab}{Name for a y-lab.} \item{.nrow}{Number of rows of sub-plots in the output plot.} \item{.legend.ncol}{Number of columns in the output legend.} \item{.logx}{if T then transform x-axis to log-scale.} \item{.logy}{if T then transform y-axis to log-scale.} } \value{ \code{top.cross} - return list for each element in \code{.n} with intersection matrix (from \code{tcR::intersectClonesets}). \code{top.cross.vec} - vector of length \code{.n} with \code{.i}:\code{.j} elements of each matrix. \code{top.cross.plot} - grid / ggplot object. } \description{ \code{top.cross} - get top crosses of the given type between each pair of the given data.frames with \code{top.cross} function. \code{top.cross.vec} - get vector of cross values for each top with the \code{top.cross.vec} function. \code{top.cross.plot} - plot a plots with result with the \code{top.cross.plot} function. } \details{ Parameter \code{.n} can have two possible values. It could be either integer vector of numbers (same as in the \code{top.fun} function) or NA and then it will be replaced internally by the value \code{.n <- seq(5000, min(sapply(.data, nrow)), 5000)}. } \examples{ \dontrun{ immdata.top <- top.cross(immdata) top.cross.plot(immdata.top) } } \seealso{ \code{\link{intersect}} } tcR/man/repSave.Rd0000644000176200001440000000145713667026401013465 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/io.R \name{repSave} \alias{repSave} \title{Save tcR data frames to disk as text files or gzipped text files.} \usage{ repSave(.data, .format = c("txt", "gz"), .names = "", .folder = "./") } \arguments{ \item{.data}{Either tcR data frame or a list of tcR data frames.} \item{.format}{"txt" for simple tab-delimited text tables, "gz" for compressed (gzipped) tables.} \item{.names}{Names of output files. By default it's an empty string so names will be taken from names of the input list.} \item{.folder}{Path to the folder with output files.} } \description{ Save repertoire files to either text files or gzipped text files. You can read them later by \code{repLoad} function with \code{.format = "tcr"}. } \seealso{ \link{repLoad} } tcR/man/entropy.Rd0000644000176200001440000000374413667026401013561 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/measures.R \name{entropy} \alias{entropy} \alias{js.div} \alias{kl.div} \title{Information measures.} \usage{ entropy(.data, .norm = F, .do.norm = NA, .laplace = 1e-12) kl.div(.alpha, .beta, .do.norm = NA, .laplace = 1e-12) js.div(.alpha, .beta, .do.norm = NA, .laplace = 1e-12, .norm.entropy = F) } \arguments{ \item{.data, .alpha, .beta}{Vector of values.} \item{.norm}{if T then compute normalised entropy (H / Hmax).} \item{.do.norm}{One of the three values - NA, T or F. If NA than check for distrubution \code{(sum(.data) == 1)}. and normalise if needed with the given laplace correction value. if T then do normalisation and laplace correction. If F than don't do normalisaton and laplace correction.} \item{.laplace}{Value for Laplace correction which will be added to every value in the .data.} \item{.norm.entropy}{if T then normalise JS-divergence by entropy.} } \value{ Shannon entropy, Jensen-Shannon divergence or Kullback-Leibler divergence values. } \description{ Functions for information measures of and between distributions of values. Warning! Functions will check if \code{.data} if a distribution of random variable (sum == 1) or not. To force normalisation and / or to prevent this, set \code{.do.norm} to TRUE (do normalisation) or FALSE (don't do normalisation). For \code{js.div} and \code{kl.div} vectors of values must have equal length. Functions: - The Shannon entropy quantifies the uncertainty (entropy or degree of surprise) associated with this prediction. - Kullback-Leibler divergence (information gain, information divergence, relative entropy, KLIC) is a non-symmetric measure of the difference between two probability distributions P and Q (measure of information lost when Q is used to approximate P). - Jensen-Shannon divergence is a symmetric version of KLIC. Square root of this is a metric often referred to as Jensen-Shannon distance. } \seealso{ \link{similarity}, \link{diversity} } tcR/man/cloneset.stats.Rd0000644000176200001440000000160313667026401015022 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stats.R \name{cloneset.stats} \alias{cloneset.stats} \alias{repseq.stats} \title{MiTCR data frame basic statistics.} \usage{ cloneset.stats(.data, .head = 0) repseq.stats(.data, .head = 0) } \arguments{ \item{.data}{tcR data frames or a list with tcR data frames.} \item{.head}{How many top clones use to comput summary.} } \value{ if \code{.data} is a data frame, than numeric vector with statistics. If \code{.data} is a list with data frames, than matrix with statistics for each data frame. } \description{ Compute basic statistics of TCR repertoires: number of clones, number of clonotypes, number of in-frame and out-of-frame sequences, summary of "Read.count", "Umi.count" and other. } \examples{ \dontrun{ # Compute basic statistics of list with data frames. cloneset.stats(immdata) repseq.stats(immdata) } } tcR/man/vis.pca.Rd0000644000176200001440000000134013667026401013412 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.pca} \alias{vis.pca} \title{PCA result visualisation} \usage{ vis.pca(.data, .groups = NA, .text = T) } \arguments{ \item{.data}{Result from prcomp() function or a data frame with two columns 'First' and 'Second' stands for the first PC and the second PC.} \item{.groups}{List with names for groups and indices of the group members. If NA than each member is in the individual group.} \item{.text}{If T than print the names of the subjects.} } \value{ ggplot object. } \description{ Plot the given pca results with colour divided by the given groups. } \examples{ \dontrun{ data(twb) tmp = geneUsage(twb) vis.pca(prcomp(t(tmp[,-1]))) } } tcR/man/group.clonotypes.Rd0000644000176200001440000000315113667026455015414 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{group.clonotypes} \alias{group.clonotypes} \title{Get all unique clonotypes.} \usage{ group.clonotypes( .data, .gene.col = "V.gene", .count.col = "Read.count", .prop.col = "Read.proportion", .seq.col = "CDR3.amino.acid.sequence" ) } \arguments{ \item{.data}{Either tcR data frame or a list with data frames.} \item{.gene.col}{Either name of the column with gene segments used to compare clonotypes or NA if you don't need comparing using gene segments.} \item{.count.col}{Name of the column with counts for each clonotype.} \item{.prop.col}{Name of the column with proportions for each clonotype.} \item{.seq.col}{Name of the column with clonotypes' CDR3 sequences.} } \value{ Data frame or a list with data frames with updated counts and proportion columns and rows with unique clonotypes only. } \description{ Get all unique clonotypes with merged counts. Unique clonotypes are those with either equal CDR3 sequence or with equal CDR3 sequence and equal gene segments. Counts of equal clonotypes will be summed up. } \examples{ \dontrun{ tmp <- data.frame(A = c('a','a','b','c', 'a') B = c('V1', 'V1','V1','V2', 'V3') C = c(10,20,30,40,50), stringsAsFactors = F) tmp # A B C # 1 a V1 10 # 2 a V1 20 # 3 b V1 30 # 4 c V2 40 # 5 a V3 50 group.clonotypes(tmp, 'B', 'C', 'A') # A B C # 1 a V1 30 # 3 b V1 50 # 4 c V2 30 # 5 a V3 40 group.clonotypes(tmp, NA, 'C', 'A') # A B C # 1 a V1 80 # 3 b V1 30 # 4 c V2 40 # For tcR data frame: data(twb) twb1.gr <- group.clonotypes(twb[[1]]) twb.gr <- group.clonotypes(twb) } } tcR/man/segments.list.Rd0000644000176200001440000000172313667026455014664 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/docdata.R \docType{data} \name{segments.list} \alias{segments.list} \alias{genesegments} \title{Segment data.} \format{ \code{genesegments} is a list with data frames. } \description{ \code{segments} is a list with 5 data frames with data of human alpha-beta chain segments. Elements names as "TRAV", "TRAJ", "TRBV", "TRVJ", "TRVD". Each data frame consists of 5 columns: - V.allelles / J.allelles / D.allelles - character column with names of V/D/J-segments. - CDR3.position - position in the full nucleotide segment sequence where CDR3 starts. - Full.nucleotide.sequence - character column with segment CDR1-2-3 sequence. - Nucleotide.sequence - character column with segment CDR3 sequences. - Nucleotide.sequence.P - character column with segment CDR3 sequences with P-insertions. } \examples{ \dontrun{ data(genesegments) genesegments$Nucleotide.sequence[segments$TRBV[,1] == "TRBV10-1"] } } tcR/man/segments.alphabets.Rd0000644000176200001440000000364313667026455015657 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/docdata.R \docType{data} \name{segments.alphabets} \alias{segments.alphabets} \alias{HUMAN_TRAV} \alias{genealphabets} \alias{HUMAN_TRAJ} \alias{HUMAN_TRBV} \alias{HUMAN_TRBD} \alias{HUMAN_TRBJ} \alias{HUMAN_TRBV_MITCR} \alias{HUMAN_TRGV} \alias{HUMAN_TRGJ} \alias{HUMAN_TRDV} \alias{HUMAN_TRDD} \alias{HUMAN_TRDJ} \alias{HUMAN_IGHV} \alias{HUMAN_IGHD} \alias{HUMAN_IGHJ} \alias{HUMAN_IGKV} \alias{HUMAN_IGKJ} \alias{HUMAN_IGLJ} \alias{HUMAN_IGLV} \alias{HUMAN_TRBV_ALS} \alias{HUMAN_TRBV_FAM} \alias{HUMAN_TRBV_GEN} \alias{MACMUL_TRBJ} \alias{MACMUL_TRBV} \alias{MOUSE_TRBJ} \alias{MOUSE_TRBV} \alias{MOUSE_TRAV} \alias{MOUSE_TRAJ} \alias{MOUSE_IGKV} \alias{MOUSE_IGKJ} \alias{MOUSE_IGHV} \alias{MOUSE_IGHD} \alias{MOUSE_IGHJ} \alias{MOUSE_TRDD} \alias{MOUSE_TRDV} \alias{MOUSE_TRDJ} \alias{MOUSE_TRGV} \alias{MOUSE_TRGJ} \alias{MOUSE_IGLJ} \alias{MOUSE_IGLV} \title{Alphabets of TCR and Ig gene segments.} \format{ Each \code{_} is a character vector. is an identifier of species, is concatenated three identifiers of cell type ("TR**" for TCR, "IG**" for Ig), chain (e.g., "**A*" for alpha chains) and gene segment ("***V" for V(ariable) gene segment, "***J" for J(oining) gene segment, "***D" for D(iversity) gene segment). } \usage{ HUMAN_TRAV HUMAN_TRAJ HUMAN_TRBV HUMAN_TRBD HUMAN_TRBJ HUMAN_TRBV_MITCR HUMAN_TRBV_ALS HUMAN_TRGV HUMAN_TRGJ HUMAN_TRDV HUMAN_TRDD HUMAN_TRDJ MOUSE_TRBV MOUSE_TRBJ MOUSE_TRAV MOUSE_TRAJ MOUSE_IGKV MOUSE_IGKJ MOUSE_IGHV MOUSE_IGHD MOUSE_IGHJ MACMUL_TRBV MACMUL_TRBJ HUMAN_IGHV HUMAN_IGHD HUMAN_IGHJ HUMAN_IGLV HUMAN_IGLJ MOUSE_IGLJ MOUSE_IGLV } \description{ Character vector with names for segments. With \code{tcR} we provided alphabets for all alpha, beta, gamma and delta chains gene segments. } \examples{ \dontrun{ HUMAN_TRBV[1] # => "TRBV10-1" } } \keyword{datasets} tcR/man/permutedf.Rd0000644000176200001440000000106313667026401014044 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{permutedf} \alias{permutedf} \alias{unpermutedf} \title{Shuffling data frames.} \usage{ permutedf(.data) unpermutedf(.data) } \arguments{ \item{.data}{MiTCR data.frame or list of such data frames.} } \value{ Shuffled data.frame or un-shuffled data frame if \code{.data} is a data frame, else list of such data frames. } \description{ Shuffle the given data.frame and order it by the Read.count column or un-shuffle a data frame and return it to the initial order. } tcR/man/vis.gene.usage.Rd0000644000176200001440000000323013667026455014701 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.gene.usage} \alias{vis.gene.usage} \alias{vis.V.usage} \alias{vis.J.usage} \title{Histogram of segments usage.} \usage{ vis.gene.usage( .data, .genes = NA, .main = "Gene usage", .ncol = 3, .coord.flip = F, .dodge = F, .labs = c("Gene", "Frequency"), ... ) } \arguments{ \item{.data}{Mitcr data frame or a list with mitcr data frames.} \item{.genes}{Gene alphabet passed to \link{geneUsage}.} \item{.main}{Main title of the plot.} \item{.ncol}{Number of columns in a grid of histograms if \code{.data} is a list and \code{.dodge} is F.} \item{.coord.flip}{if T then flip coordinates.} \item{.dodge}{If \code{.data} is a list, than if this is T plot V-usage for all data frames to the one histogram.} \item{.labs}{Character vector of length 2 with names for x-axis and y-axis.} \item{...}{Parameter passed to \code{geneUsage}. By default the function compute V-usage or J-usage for beta chains w/o using read counts and w/ "Other" segments.} } \value{ ggplot object. } \description{ Plot a histogram or a grid of histograms of V- / J-usage. } \examples{ \dontrun{ # Load your data. load('immdata.rda') # Compute V-usage statistics. imm1.vs <- geneUsage(immdata[[1]], HUMAN_TRBV) vis.V.usage(immdata, HUMAN_TRBV, .main = 'Immdata V-usage [1]', .dodge = T) # Plot a histogram for one data frame using all gene segment data from V.gene column. vis.V.usage(imm1.vs, NA, .main = 'Immdata V-usage [1]') # Plot a grid of histograms - one histogram for V-usage for each data frame in .data. vis.V.usage(immdata, HUMAN_TRBV, .main = 'Immdata V-usage', .dodge = F, .other = F) } } tcR/man/tailbound.proportion.Rd0000644000176200001440000000372013667026401016246 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataproc.R \name{tailbound.proportion} \alias{tailbound.proportion} \alias{clonal.proportion} \alias{top.proportion} \title{Proportions of specifyed subsets of clones.} \usage{ tailbound.proportion(.data, .bound = 2, .col = 'Read.count') top.proportion(.data, .head = 10, .col = 'Read.count') clonal.proportion(.data, .perc = 10, .col = 'Read.count') } \arguments{ \item{.data}{Data frame or a list with data frames.} \item{.bound}{Subset the \code{.data} by \code{.col} <= \code{.bound}.} \item{.col}{Column's name with counts of sequences.} \item{.head}{How many top values to choose - parameter to the \code{.head} function.} \item{.perc}{Percentage (0 - 100).} } \value{ For \code{tailbound.proportion} - numeric vector of percentage. For \code{top.proportion} - numeric vector of percentage for top clones. For \code{clonal.proportion} - vector or matrix with values for number of clones, occupied percentage and proportion of the chosen clones to the overall count of clones. } \description{ Get a specifyed subset of the given repertiure and compute which proportion in counts it has comparing to the overall count. \code{tailbound.proportion} - subset by the count; \code{top.proportion} - subset by rank (top N clones); \code{clonal.proportion} - subset by a summary percentage (top N clones which in sum has the given percentage). } \examples{ \dontrun{ # How many clones fill up approximately clonal.proportion(immdata, 25) # the 25\% of the sum of values in 'Read.count'? # What proportion of the top-10 clones' reads vis.top.proportions(immdata) # Plot this proportions. # What proportion of sequences which # has 'Read.count' <= 100 to the tailbound.proportion(immdata, 100) # overall number of reads? } } \seealso{ \link{vis.top.proportions}, \link{prop.sample} } tcR/man/set.pb.Rd0000644000176200001440000000112413667026401013242 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{set.pb} \alias{set.pb} \alias{add.pb} \title{Simple functions for manipulating progress bars.} \usage{ set.pb(.max) add.pb(.pb, .value = 1) } \arguments{ \item{.max}{Length of the progress bar.} \item{.pb}{Progress bar object.} \item{.value}{Value to add to the progress bar.} } \value{ Progress bar (for set.pb) or length-one numeric vector giving the previous value (for add.pb). } \description{ Set the progress bar with the given length (from zero) or add value to the created progress bar. } tcR/man/find.similar.sequences.Rd0000644000176200001440000000272113667026401016424 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/strtools.R \name{find.similar.sequences} \alias{find.similar.sequences} \alias{exact.match} \alias{hamming.match} \alias{levenshtein.match} \title{Find similar sequences.} \usage{ find.similar.sequences(.data, .patterns = c(), .method = c('exact', 'hamm', 'lev'), .max.errors = 1, .verbose = T, .clear = F) exact.match(.data, .patterns = c(), .verbose = T) hamming.match(.data, .patterns = c(), .max.errors = 1, .verbose = T) levenshtein.match(.data, .patterns = c(), .max.errors = 1, .verbose = T) } \arguments{ \item{.data}{Vector of strings.} \item{.patterns}{Character vector of sequences, which will be used for searching for neighbours.} \item{.method}{Which method use: 'exact' for exact matching, 'hamm' for Hamming Distance, 'lev' for Levenshtein distance.} \item{.max.errors}{Max Hamming or Levenshtein distance between strings. Doesn't use in 'exact' setting.} \item{.verbose}{Should function print progress or not. // DON'T USE IT} \item{.clear}{if T then remove all sequences with character "*" or "~".} } \value{ Matrix with two columns [i,j], dist(data(i), data(j)) <= .max.errors. } \description{ Return matrix M with two columns. For each element in row i and column j M[i,j] => distance between pattern(i) and data(j) sequences equal to or less than .max.errors. This function will uppercase .data and remove all strings, which have anything than A-Z letters. } tcR/man/clonal.space.homeostasis.Rd0000644000176200001440000000235413667026455016765 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stats.R \name{clonal.space.homeostasis} \alias{clonal.space.homeostasis} \title{Clonal space homeostasis.} \usage{ clonal.space.homeostasis( .data, .clone.types = c(Rare = 1e-05, Small = 1e-04, Medium = 0.001, Large = 0.01, Hyperexpanded = 1), .prop.col = "Read.proportion" ) } \arguments{ \item{.data}{Cloneset data frame or list with such data frames.} \item{.clone.types}{Named numeric vector.} \item{.prop.col}{Which column to use for counting proportions.} } \description{ Compute clonal space homeostatsis - statistics of how many space occupied by clones with specific proportions. } \examples{ \dontrun{ data(twb) # Compute summary space of clones, that occupy # [0, .05) and [.05, 1] proportion. clonal.space.homeostasis(twb, c(Low = .05, High = 1))) # Low (0 < X <= 0.05) High (0.05 < X <= 1) # Subj.A 0.9421980 0.05780198 # Subj.B 0.9239454 0.07605463 # Subj.C 0.8279270 0.17207296 # Subj.D 1.0000000 0.00000000 # I.e., for Subj.D sum of all read proportions for clones # which have read proportion between 0 and .05 is equal to 1. } } \seealso{ \link{vis.clonal.space} } tcR/man/sample2D.Rd0000644000176200001440000000106713667026401013524 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{sample2D} \alias{sample2D} \title{Get a sample from matrix with probabilities.} \usage{ sample2D(.table, .count = 1) } \arguments{ \item{.table}{Numeric matrix or data frame with probabilities and columns and rows names.} \item{.count}{Number of sample to fetch.} } \value{ Character matrix with nrow == .count and 2 columns. row[1] in row.names(.table), row[2] in colnames(.table). } \description{ Get a sample from matrix or data frame with pair-wise probabilities. } tcR/man/vis.clonal.dynamics.Rd0000644000176200001440000000157213667026401015734 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.clonal.dynamics} \alias{vis.clonal.dynamics} \title{Visualise clonal dynamics among time points.} \usage{ vis.clonal.dynamics(.changed, .lower, .upper, .log = T) } \arguments{ \item{.changed}{Result from the \code{find.clonotypes} function, i.e. data frame with first columns with sequences (nucleotide or amino acid) and other columns are columns with frequency / count for each time point for each clone.} \item{.lower}{Similar to .changed but values are lower bound for clonal count / frequency.} \item{.upper}{Similar to .changed but values are upper bound for clonal count / frequency.} \item{.log}{if T then log-scale y-axis.} } \value{ ggplot object. } \description{ Visualise clonal dynamics (i.e., changes in frequency or count) with error bars of given clones among time points. } tcR/man/vis.logo.Rd0000644000176200001440000000154413667026455013626 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.logo} \alias{vis.logo} \title{Logo - plots for amino acid and nucletide profiles.} \usage{ vis.logo( .data, .replace.zero.with.na = T, .jitter.width = 0.01, .jitter.height = 0.01, .dodge.width = 0.15 ) } \arguments{ \item{.data}{Output from the \code{kmer.profile} function.} \item{.replace.zero.with.na}{if T then replace all zeros with NAs, therefore letters with zero frequency wont appear at the plot.} \item{.jitter.width, .jitter.height, .dodge.width}{Parameters to \code{position_jitterdodge} for aligning text labels of letters.} } \value{ ggplot2 object } \description{ Plot logo-like graphs for visualising of nucleotide or amino acid motif sequences / profiles. } \examples{ \dontrun{ d <- kmer_profile(c('CASLL', 'CASSQ', 'CASGL')) vis.logo(d) } } tcR/man/vis.kmer.histogram.Rd0000644000176200001440000000206613667026455015620 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.kmer.histogram} \alias{vis.kmer.histogram} \title{Plot of the most frequent kmers.} \usage{ vis.kmer.histogram( .kmers, .head = 100, .position = c("stack", "dodge", "fill") ) } \arguments{ \item{.kmers}{Data frame with two columns "Kmers" and "Count" or a list with such data frames. See Examples.} \item{.head}{Number of the most frequent kmers to choose for plotting from each data frame.} \item{.position}{Character vector of length 1. Position of bars for each kmers. Value for the \code{ggplot2} argument \code{position}.} } \description{ Plot a distribution (bar plot) of the most frequent kmers in a data. } \examples{ \dontrun{ # Load necessary data and package. library(gridExtra) load('immdata.rda') # Get 5-mers. imm.km <- get.kmers(immdata) # Plots for kmer proportions in each data frame in immdata. p1 <- vis.kmer.histogran(imm.km, .position = 'stack') p2 <- vis.kmer.histogran(imm.km, .position = 'fill') grid.arrange(p1, p2) } } \seealso{ \code{get.kmers} } tcR/man/parse.cloneset.Rd0000644000176200001440000000424113667026455015010 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/parsing.R \name{parse.cloneset} \alias{parse.cloneset} \title{Parse input table files with the immune receptor repertoire data.} \usage{ parse.cloneset( .filename, .nuc.seq, .aa.seq, .reads, .barcodes, .vgenes, .jgenes, .dgenes, .vend, .jstart, .dalignments, .vd.insertions, .dj.insertions, .total.insertions, .skip = 0, .sep = "\\t" ) } \arguments{ \item{.filename}{Path to the input file with cloneset data.} \item{.nuc.seq}{Name of the column with CDR3 nucleotide sequences.} \item{.aa.seq}{Name of the column with CDR3 amino acid sequences.} \item{.reads}{Name of the column with counts of reads for each clonotype.} \item{.barcodes}{Name of the column with counts of barcodes (UMI, events) for each clonotype.} \item{.vgenes}{Name of the column with names of aligned Variable gene segments.} \item{.jgenes}{Name of the column with names of aligned Joining gene segments.} \item{.dgenes}{Name of the column with names of aligned Diversity gene segments.} \item{.vend}{Name of the column with last positions of aligned V gene segments.} \item{.jstart}{Name of the column with first positions of aligned J gene segments.} \item{.dalignments}{Character vector of length two that names columns with D5' and D3' end positions.} \item{.vd.insertions}{Name of the column with VD insertions for each clonotype.} \item{.dj.insertions}{Name of the column with DJ insertions for each clonotype.} \item{.total.insertions}{Name of the column with total number of insertions for each clonotype.} \item{.skip}{How many lines from beginning to skip.} \item{.sep}{Separator character.} } \value{ Data frame with immune receptor repertoire data. See \link{parse.file} for more details. } \description{ General parser for cloneset table files. Each column name has specific purpose (e.g., column for CDR3 nucleotide sequence or aligned gene segments), so you need to supply column names which has this purpose in your input data. } \examples{ \dontrun{ # Parse file in "~/mitcr/immdata1.txt" as a MiTCR file. immdata1 <- parse.file("~/mitcr/immdata1.txt", 'mitcr') } } \seealso{ \link{parse.file} } tcR/man/get.inframes.Rd0000644000176200001440000000246513667026401014442 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stats.R \name{get.inframes} \alias{get.inframes} \alias{get.outframes} \alias{count.inframes} \alias{count.outframes} \alias{get.frames} \alias{count.frames} \alias{clonotypescount} \title{In-frame / out-of-frame sequences filter.} \usage{ get.inframes(.data, .head = 0, .coding = T) get.outframes(.data, .head = 0) count.inframes(.data, .head = 0, .coding = T) count.outframes(.data, .head = 0) get.frames(.data, .frame = c('in', 'out', 'all'), .head = 0, .coding = T) count.frames(.data, .frame = c('in', 'out', 'all'), .head = 0, .coding = T) } \arguments{ \item{.data}{MiTCR data.frame or a list with mitcr data.frames.} \item{.head}{Parameter to the head() function. Supply 0 to get all elements. \code{head} applied before subsetting, i.e. if .head == 500, you will get in-frames from the top 500 clonotypes.} \item{.coding}{if T then return only coding sequences, i.e. without stop-codon.} \item{.frame}{Which *-frames to choose.} } \value{ Filtered data.frame or a list with such data.frames. } \description{ Return the given data frame with in-frame or out-of-frame sequences only. Nucleotide sequences in a column "CDR3.nucleotide.sequence" are checked if they length are divisible by 3 (len mod 3 == 0 => in-frame, else out-of-frame) } tcR/man/repOverlap.Rd0000644000176200001440000000557413667026455014214 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/repoverlap.R \name{repOverlap} \alias{repOverlap} \title{General function for the repertoire overlap evaluation.} \usage{ repOverlap( .data, .method = c("exact", "hamm", "lev", "jaccard", "morisita", "tversky", "overlap", "horn"), .seq = c("nuc", "aa"), .quant = c("read.count", "umi.count", "read.prop", "umi.prop"), .vgene = F, .norm = T, .a = 0.5, .b = 0.5, .do.unique = T, .verbose = T ) } \arguments{ \item{.data}{List of clonesets.} \item{.method}{Which method to use for the overlap evaluation. See "Details" for methods.} \item{.seq}{Which clonotype sequences to use for the overlap: "nuc" for "CDR3.nucleotide.sequence", "aa" for "CDR3.amino.acid.sequence".} \item{.quant}{Which column to use for the quantity of clonotypes: "read.count" for the "Read.count" column, "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, "umi.prop" for the "Umi.proportion" column. Used in "morisita" and "horn".} \item{.vgene}{If T than use V genes in computing shared or similar clonotypes. Used in all methods.} \item{.norm}{If T than compute the normalised number of shared clonotypes. Used in "exact".} \item{.a, .b}{Alpha and beta parameters for "tversky". Default values gives the Jaccard index measure.} \item{.do.unique}{If T than remove duplicates from the input data, but add their quantities to their clones.} \item{.verbose}{If T than output the data processing progress bar.} } \description{ General interface to all cloneset overlap functions. } \details{ You can see a more detailed description for each overlap method at \link{intersectClonesets} and \link{similarity}. Parameter \code{.method} can have one of the following value each corresponding to the specific method: - "exact" for the shared number of clonotypes (basic function \code{intersectClonesets(..., .type = "..e")}). - "hamm" for the number of similar clonotypes by the Hamming distance (basic function \code{intersectClonesets(..., .type = "..h")}). - "lev" for the number of similar clonotypes by the Levenshtein distance (basic function \code{intersectClonesets(..., .type = "..l")}). - "jaccard" for the Jaccard index (basic function \code{jaccard.index}). - "morisita" for the Morisita's overlap index (basic function \code{morisita.index}). - "tversky" for the Tversky index (basic function \code{tversky.index}). - "overlap" for the overlap coefficient (basic function \code{overlap.coef}). - "horn" for the Horn's index (basic function \code{horn.index}). } \examples{ \dontrun{ data(twb) repOverlap(twb, "exact", .seq = "nuc", .vgene = F) repOverlap(twb, "morisita", .seq = "aa", .vgene = T, .quant = "umi.count") ov <- repOverlap(twb) ov[is.na(ov)] <- 0 vis.pca(prcomp(ov, scale. = T), list(A = c(1, 2), B = c(3, 4))) } } \seealso{ \link{intersectClonesets}, \link{similarity}, \link{repDiversity} } tcR/man/matrixdiagcopy.Rd0000644000176200001440000000056013667026401015076 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{matrixdiagcopy} \alias{matrixdiagcopy} \title{Copy the up-triangle matrix values to low-triangle.} \usage{ matrixdiagcopy(mat) } \arguments{ \item{mat}{Given up-triangle matrix.} } \value{ Full matrix. } \description{ Copy the up-triangle matrix values to low-triangle. } tcR/man/vis.group.boxplot.Rd0000644000176200001440000000312713667026455015507 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.group.boxplot} \alias{vis.group.boxplot} \title{Boxplot for groups of observations.} \usage{ vis.group.boxplot( .data, .groups = NA, .labs = c("V genes", "Frequency"), .title = "", .rotate.x = T, .violin = T, .notch = F, ... ) } \arguments{ \item{.data}{Either a matrix with colnames and rownames specifyed or a data frame with the first column of strings for row names and other columns stands for values.} \item{.groups}{Named list with character vectors for names of elements for each group. If NA than each member is in the individual group.} \item{.labs}{Labs names. Character vector of length 1 (for naming both axis with same name) or 2 (first elements stands for x-axis).} \item{.title}{Main title of the plot.} \item{.rotate.x}{if T then rotate x-axis.} \item{.violin}{If T then plot a violin plot.} \item{.notch}{"notch" parameter to the \code{geom_boxplot} ggplo2 function.} \item{...}{Parameters passed to \code{melt}, applied to \code{.data} before plotting in \code{vis.group.boxplot}.} } \value{ ggplot object. } \description{ Plot boxplots for each group. } \examples{ \dontrun{ names(immdata) # "A1" "A2" "B1" "B2" "C1" "C2" # Plot a boxplot for V-usage for each plot # three boxplots for each group. vis.group.boxplot(freq.Vb(immdata), list(A = c('A1', 'A2'), B = c('B1', 'B2'), C = c('C1', 'C2')), c('V segments', 'Frequency')) data(twb) ov <- repOverlap(twb) sb <- matrixSubgroups(ov, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))); vis.group.boxplot(sb) } } tcR/man/fix.alleles.Rd0000644000176200001440000000060613667026401014261 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{fix.alleles} \alias{fix.alleles} \alias{fix.genes} \title{Fix alleles / genes by removing allele information / unnecessary colons.} \usage{ fix.alleles(.data) } \arguments{ \item{.data}{tcR data frame.} } \description{ Fix alleles / genes by removing allele information / unnecessary colons. } tcR/man/inverse.simpson.Rd0000644000176200001440000000600213667026401015211 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/diversity.R \name{inverse.simpson} \alias{inverse.simpson} \alias{diversity} \alias{gini} \alias{chao1} \alias{gini.simpson} \title{Distribution evaluation.} \usage{ inverse.simpson(.data, .do.norm = NA, .laplace = 0) diversity(.data, .q = 5, .do.norm = NA, .laplace = 0) gini(.data, .do.norm = NA, .laplace = 0) gini.simpson(.data, .do.norm = NA, .laplace = 0) chao1(.data) } \arguments{ \item{.data}{Numeric vector of values for proportions or for numbers of individuals.} \item{.do.norm}{One of the three values - NA, T or F. If NA than check for distrubution (sum(.data) == 1) and normalise if needed with the given laplace correction value. if T then do normalisation and laplace correction. If F than don't do normalisaton and laplace correction.} \item{.laplace}{Value for Laplace correction which will be added to every value in the .data.} \item{.q}{q-parameter for the Diversity index.} } \value{ Numeric vector of length 1 with value for all functions except \code{chao1}, which returns 4 values: estimated number of species, standart deviation of this number and two 95% confidence intervals for the species number. } \description{ Functions for evaluating the diversity of species or objects in the given distribution. See the \code{repOverlap} function for working with clonesets and a general interface to all of this functions. Warning! Functions will check if .data is a distribution of a random variable (sum == 1) or not. To force normalisation and / or to prevent this, set .do.norm to TRUE (do normalisation) or FALSE (don't do normalisation), respectively. - True diversity, or the effective number of types, refers to the number of equally-abundant types needed for the average proportional abundance of the types to equal that observed in the dataset of interest where all types may not be equally abundant. - Inverse Simpson index is the effective number of types that is obtained when the weighted arithmetic mean is used to quantify average proportional abundance of types in the dataset of interest. - The Gini coefficient measures the inequality among values of a frequency distribution (for example levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini coefficient of one (or 100 percents ) expresses maximal inequality among values (for example where only one person has all the income). - The Gini-Simpson index is the probability of interspecific encounter, i.e., probability that two entities represent different types. - Chao1 estimator is a nonparameteric asymptotic estimator of species richness (number of species in a population). } \examples{ data(twb) # Next two are equal calls: stopifnot(gini(twb[[1]]$Read.count, TRUE, 0) - 0.7609971 < 1e-07) stopifnot(gini(twb[[1]]$Read.proportion, FALSE) - 0.7609971 < 1e-07) stopifnot(chao1(twb[[1]]$Read.count)[1] == 1e+04) } \seealso{ \link{repOverlap}, \link{entropy}, \link{similarity} } tcR/man/kmer.profile.Rd0000644000176200001440000000174213667026401014452 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/kmers.R \name{kmer.profile} \alias{kmer.profile} \alias{kmers.profile} \title{Profile of sequences of equal length.} \usage{ kmer.profile(.data, .names = rep('Noname', times=length(.data)), .verbose = F) } \arguments{ \item{.data}{Either list with elements of one of the allowed classes or object with one of the class: data.frame with first column with character sequences and second column with number of sequences or character vector.} \item{.names}{Names for each sequence / row in the \code{.data}.} \item{.verbose}{if T then print processing output.} } \value{ Return data frame with first column "Symbol" with all possible symbols in the given sequences and other columns with names "1", "2", ... for each position with percentage for each symbol. } \description{ Return profile for the given character vector or a data frame with sequences of equal length or list with them. } \seealso{ \link{vis.logo} } tcR/man/convergence.index.Rd0000644000176200001440000000161413667026455015470 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/crosses.R \name{convergence.index} \alias{convergence.index} \title{Compute convergence characteristics of repertoires.} \usage{ convergence.index( .alpha, .beta, .col.nuc = "CDR3.nucleotide.sequence", .col.aa = "CDR3.amino.acid.sequence" ) } \arguments{ \item{.alpha}{Either data frame with columns \code{.col.nuc} and \code{.col.aa} or list with such data frames.} \item{.beta}{Either data frame or none.} \item{.col.nuc}{Name of the column with nucleotide sequences.} \item{.col.aa}{Name of the columnw ith aminoacid sequences.} } \value{ If \code{.alpha} is data frame, than integer vector of length 2 with . If \code{.alpha} is a list than matrix M with M[i,j] = convergence.index(.alpha[[i]], .alpha[[j]]). } \description{ Get a number of rows with similar aminoacid sequence but different nucleotide sequence. } tcR/man/revcomp.Rd0000644000176200001440000000116313667026455013536 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/strtools.R \name{revcomp} \alias{revcomp} \alias{bunch.translate} \title{DNA reverse complementing and translation.} \usage{ revcomp(.seq) bunch.translate(.seq, .two.way = T) revcomp(.seq) bunch.translate(.seq, .two.way = T) } \arguments{ \item{.seq}{Vector of nucleotide sequences.} \item{.two.way}{if T then translate sequences from both ends (output differes for out-of-frame sequences).} } \value{ Vector of corresponding revese complemented or aminoacid sequences. } \description{ Functions for DNA reverse complementing and translation. } tcR/man/codon.variants.Rd0000644000176200001440000000326613667026401015010 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/strtools.R \name{codon.variants} \alias{codon.variants} \alias{translated.nucl.sequences} \alias{reverse.translation} \title{Functions for working with aminoacid sequences.} \usage{ codon.variants(.aaseq, .nucseq = sapply(1:length(.aaseq), function (i) paste0(rep('XXX', times = nchar(.aaseq[i])), collapse = ''))) translated.nucl.sequences(.aaseq, .nucseq = sapply(1:length(.aaseq), function (i) paste0(rep('XXX', times = nchar(.aaseq[i])), collapse = ''))) reverse.translation(.aaseq, .nucseq = paste0(rep('XXX', times = nchar(.aaseq)), collapse = '')) } \arguments{ \item{.aaseq}{Amino acid sequence.} \item{.nucseq}{Nucleotide sequence with 'X' letter at non-fixed positions. Other positions will be fixed.} } \value{ List with all possible variants for every aminoacid in .aaseq, number of sequences or character vector of candidate sequences. } \description{ \code{codon.variants} - get all codon variants for the given nucleotide sequence with known corresponding aminoacid sequence. \code{translated.nucl.variants} - get number of nucleotide sequences which can be translated to the given aminoacid sequence. \code{reverse.translation} - get all nucleotide sequences, which can be traslated to the given aminoacid sequence. } \examples{ codon.variants('ACT') translated.nucl.sequences(c('ACT', 'CASSLQ')) reverse.translation('T') # -> "ACA" "ACC" "ACG" "ACT" reverse.translation('T', 'XXT') # -> "ACT" translated.nucl.sequences('ACT', 'XXXXXXXC') codon.variants('ACT', 'XXXXXXXC') reverse.translation('ACT', 'XXXXXXXC') } tcR/man/startmitcr.Rd0000644000176200001440000000232113667026455014254 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mitcr.R \name{startmitcr} \alias{startmitcr} \title{Start MiTCR directly from the package.} \usage{ startmitcr( .input = "", .output = "", ..., .file.path = "", .mitcr.path = "~/programs/", .mem = "4g" ) } \arguments{ \item{.input, .output}{Input and output files.} \item{...}{Specify input and output files and arguments of the MITCR without first '-' to run it.} \item{.file.path}{Path prepending to \code{.input} and \code{.output}. If input and output is empty, but .file.path is specified, than process all files from the folder \code{.file.path}} \item{.mitcr.path}{Path to MiTCR .jar file.} \item{.mem}{Volume of memory available to MiTCR.} } \description{ Start the MiTCR tools directly from the package with given settings. } \details{ Don't use spaces in paths! You should have insalled JDK 1.7 to make it works. } \examples{ \dontrun{ # Equal to # java -Xmx8g -jar ~/programs/mitcr.jar -pset flex # -level 2 ~/data/raw/TwA1_B.fastq.gz ~/data/mitcr/TwA1_B.txt startmitcr('raw/TwA1_B.fastq.gz', 'mitcr/TwA1_B.txt', .file.path = '~/data/', pset = 'flex', level = 1, 'debug', .mitcr.path = '~/programs/', .mem = '8g') } } tcR/man/spectratype.Rd0000644000176200001440000000211713667026455014426 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/spectrum.R \name{spectratype} \alias{spectratype} \title{Spectratype} \usage{ spectratype( .data, .quant = c("read.count", "umi.count", "id"), .gene = "V", .plot = T, .main = "Spectratype", .legend = "Gene segment", .labs = c("CDR3 length", NA) ) } \arguments{ \item{.data}{tcR data frame.} \item{.quant}{Either "read.count" or "umi.count" for choosing the corresponding columns, or "id" to compute avoid using counts.} \item{.gene}{Either NA for not using genes, "V" or "J" for corresponding genes.} \item{.plot}{If T than plot the spectratype plot, otherwise return a table with data for lengths and counts.} \item{.main}{Main title.} \item{.legend}{Legend title.} \item{.labs}{Character vector of length 2 for x-lab and y-lab.} } \description{ Plot a spectratype plot - a histogram of read counts / umi counts by CDR3 length. } \examples{ \dontrun{ data(twb) tmp = twb[[1]] spectratype(tmp) spectratype(tmp, .quant = "id", .plot = T, .gene = 'V') spectratype(tmp, .quant = "read.count", .plot = F) } } tcR/man/geneUsage.Rd0000644000176200001440000000463013667026455013770 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/segments.R \name{geneUsage} \alias{geneUsage} \title{Gene usage.} \usage{ geneUsage( .data, .genes = HUMAN_TRBV_MITCR, .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), .norm = F, .ambig = F ) } \arguments{ \item{.data}{Cloneset data frame or a list with clonesets.} \item{.genes}{Either one of the gene alphabet (e.g., HUMAN_TRBV, \link{genealphabets}) or list with two gene alphabets for computing joint distribution.} \item{.quant}{Which column to use for the quantity of clonotypes: NA for computing only number of genes without using clonotype counts, "read.count" for the "Read.count" column, "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, "umi.prop" for the "Umi.proportion" column.} \item{.norm}{If T then return proportions of resulting counting of genes.} \item{.ambig}{If F than remove from counting genes which are not presented in the given gene alphabet(s).} } \value{ If \code{.data} is a cloneset and \code{.genes} is NOT a list than return a data frame with first column "Gene" with genes and second with counts / proportions. If \code{.data} is a list with clonesets and \code{.genes} is NOT a list than return a data frame with first column "Gene" with genes and other columns with counts / proportions for each cloneset in the input list. If \code{.data} is a cloneset and \code{.genes} IS a list than return a matrix with gene segments for the first gene in \code{.genes} and column names for the second gene in \code{.genes}. See "Examples". If \code{.data} is a list with clonesets and \code{.genes} IS a list than return a list with matrices like in the previous case. } \description{ Compute frequencies or counts of gene segments ("V / J - usage"). } \examples{ \dontrun{ # Load your data data(twb) # compute V-segments frequencies of human TCR beta. seg <- geneUsage(twb, HUMAN_TRBV, .norm = T) # plot V-segments frequencies as a heatmap vis.heatmap(seg, .labs = c("Sample", "V gene")) # plot V-segments frequencies directly from clonesets vis.gene.usage(twb, HUMAN_TRBV) # plot V-segments frequencies from the gene frequencies vis.gene.usage(seg, NA) # Compute V-J joint usage. geneUsage(twb, list(HUMAN_TRBV, HUMAN_TRBJ)) # for future: # geneUsage(twb, "human", "trbv") } } \seealso{ \code{\link{genealphabets}}, \code{\link{vis.gene.usage}}, \code{\link{pca.segments}} } tcR/man/reverse.string.Rd0000644000176200001440000000101713667026401015030 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/strtools.R \name{reverse.string} \alias{reverse.string} \title{Reverse given character vector by the given n-plets.} \usage{ reverse.string(.seq, .n = 1) } \arguments{ \item{.seq}{Sequences.} \item{.n}{By which n-plets we should reverse the given strings.} } \value{ Reversed strings. } \description{ Reverse given character vector by the given n-plets. } \examples{ reverse.string('abcde') # => "edcba" reverse.string('abcde', 2) # => "debca" } tcR/man/parse.folder.Rd0000644000176200001440000001062513667026401014441 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/parsing.R \name{parse.folder} \alias{parse.folder} \alias{parse.file.list} \alias{parse.file} \alias{parse.mitcr} \alias{parse.mitcrbc} \alias{parse.migec} \alias{parse.vdjtools} \alias{parse.immunoseq} \alias{parse.immunoseq2} \alias{parse.immunoseq3} \alias{parse.tcr} \alias{parse.mixcr} \alias{parse.imseq} \alias{parse.migmap} \title{Parse input table files with immune receptor repertoire data.} \usage{ parse.file(.filename, .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', 'mixcr', 'imseq', 'tcr'), ...) parse.file.list(.filenames, .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', 'mixcr', 'imseq', 'tcr'), .namelist = NA) parse.folder(.folderpath, .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', 'mixcr', 'imseq', 'tcr'), ...) parse.mitcr(.filename) parse.mitcrbc(.filename) parse.migec(.filename) parse.vdjtools(.filename) parse.immunoseq(.filename) parse.immunoseq2(.filename) parse.immunoseq3(.filename) parse.mixcr(.filename) parse.imseq(.filename) parse.tcr(.filename) parse.migmap(.filename) } \arguments{ \item{.folderpath}{Path to the folder with text cloneset files.} \item{.format}{String that specifies the input format.} \item{...}{Parameters passed to \code{parse.cloneset}.} \item{.filename}{Path to the input file with cloneset data.} \item{.filenames}{Vector or list with paths to files with cloneset data.} \item{.namelist}{Either NA or character vector of length \code{.filenames} with names for output data frames.} } \value{ Data frame with immune receptor repertoire data. Each row in this data frame corresponds to a clonotype. The data frame has following columns: - "Umi.count" - number of barcodes (events, UMIs); - "Umi.proportion" - proportion of barcodes (events, UMIs); - "Read.count" - number of reads; - "Read.proportion" - proportion of reads; - "CDR3.nucleotide.sequence" - CDR3 nucleotide sequence; - "CDR3.amino.acid.sequence" - CDR3 amino acid sequence; - "V.gene" - names of aligned Variable gene segments; - "J.gene" - names of aligned Joining gene segments; - "D.gene" - names of aligned Diversity gene segments; - "V.end" - last positions of aligned V gene segments (1-based); - "J.start" - first positions of aligned J gene segments (1-based); - "D5.end" - positions of D'5 end of aligned D gene segments (1-based); - "D3.end" - positions of D'3 end of aligned D gene segments (1-based); - "VD.insertions" - number of inserted nucleotides (N-nucleotides) at V-D junction (-1 for receptors with VJ recombination); - "DJ.insertions" - number of inserted nucleotides (N-nucleotides) at D-J junction (-1 for receptors with VJ recombination); - "Total.insertions" - total number of inserted nucleotides (number of N-nucleotides at V-J junction for receptors with VJ recombination). } \description{ Load the TCR data from the file with the given filename to a data frame or load all files from the given folder to a list of data frames. The folder must contain onky files with the specified format. Input files could be either text files or archived with gzip ("filename.txt.gz") or bzip2 ("filename.txt.bz2"). For a general parser see \code{\link{parse.cloneset}}. Parsers are available for: MiTCR ("mitcr"), MiTCR w/ UMIs ("mitcrbc"), MiGEC ("migec"), VDJtools ("vdjtools"), ImmunoSEQ ("immunoseq" or 'immunoseq2' for old and new formats respectively), MiXCR ("mixcr"), IMSEQ ("imseq") and tcR ("tcr", data frames saved with the `repSave()` function). Output of MiXCR should contain either all hits or best hits for each gene segment. Output of IMSEQ should be generated with parameter "-on". In this case there will be no positions of aligned gene segments in the output data frame due to restrictions of IMSEQ output. tcR's data frames should be saved with the `repSave()` function. } \examples{ \dontrun{ # Parse file in "~/mitcr/immdata1.txt" as a MiTCR file. immdata1 <- parse.file("~/mitcr/immdata1.txt", 'mitcr') # Parse VDJtools file archive as .gz file. immdata1 <- parse.file("~/mitcr/immdata3.txt.gz", 'vdjtools') # Parse files "~/data/immdata1.txt" and "~/data/immdat2.txt" as MiGEC files. immdata12 <- parse.file.list(c("~/data/immdata1.txt", "~/data/immdata2.txt"), 'migec') # Parse all files in "~/data/" as MiGEC files. immdata <- parse.folder("~/data/", 'migec') } } \seealso{ \link{parse.cloneset}, \link{repSave}, \link{repLoad} } tcR/man/mutated.neighbours.Rd0000644000176200001440000000226413667026401015664 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/graph.R \name{mutated.neighbours} \alias{mutated.neighbours} \title{Get vertex neighbours.} \usage{ mutated.neighbours(.G, .V, .order = 1) } \arguments{ \item{.G}{Mutation network.} \item{.V}{Indices of vertices for which return neighbours.} \item{.order}{Neighbours of which order return.} } \value{ List of length \code{.V} with data frames with vertex properties. First row in each data frame is the vertex for which neighbours was returned. } \description{ Get all properties of neighbour vertices in a mutation network of specific vertices. } \examples{ \dontrun{ data(twb) twb.shared <- shared.repertoire(twb) G <- mutation.network(twb.shared) head(mutated.neighbours(G, 1)[[1]]) # label vseg repind prob people npeople # 1 CASSDRDTGELFF TRBV6-4 1 -1 1111 4 # 2 CASSDSDTGELFF TRBV6-4 69 -1 1100 2 # 3 CASSYRDTGELFF TRBV6-3, TRBV6-2 315 -1 1001 2 # 4 CASKDRDTGELFF TRBV6-3, TRBV6-2 2584 -1 0100 1 # 5 CASSDGDTGELFF TRBV6-4 5653 -1 0010 1 # 6 CASSDRETGELFF TRBV6-4 5950 -1 0100 1 } } tcR/man/resample.Rd0000644000176200001440000000377713667026401013677 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataproc.R \name{resample} \alias{resample} \alias{downsample} \alias{prop.sample} \title{Resample data frame using values from the column with number of clonesets.} \usage{ resample(.data, .n = -1, .col = c("read.count", "umi.count")) downsample(.data, .n, .col = c("read.count", "umi.count")) prop.sample(.data, .perc = 50, .col = c("read.count", "umi.count")) } \arguments{ \item{.data}{Data frame with the column \code{.col} or list of such data frames.} \item{.n}{Number of values / reads / UMIs to choose.} \item{.col}{Which column choose to represent quanitites of clonotypes. See "Details".} \item{.perc}{Percentage (0 - 100). See "Details" for more info.} } \value{ Subsampled data frame. } \description{ Resample data frame using values from the column with number of clonesets. Number of clonestes (i.e., rows of a MiTCR data frame) are reads (usually the "Read.count" column) or UMIs (i.e., barcodes, usually the "Umi.count" column). } \details{ \code{resample}. Using multinomial distribution, compute the number of occurences for each cloneset, than remove zero-number clonotypes and return resulting data frame. Probabilities for \code{rmultinom} for each cloneset is a percentage of this cloneset in the \code{.col} column. It's a some sort of simulation of how clonotypes are chosen from the organisms. For now it's not working very well, so use \code{downsample} instead. \code{downsample}. Choose \code{.n} clones (not clonotypes!) from the input repertoires without any probabilistic simulation, but exactly computing each choosed clones. Its output is same as for \code{resample} (repertoires), but is more consistent and biologically pleasant. \code{prop.sample}. Choose the first N clonotypes which occupies \code{.perc} percents of overall UMIs / reads. } \examples{ \dontrun{ # Get 100K reads (not clones!). immdata.1.100k <- resample(immdata[[1]], 100000, .col = "read.count") } } \seealso{ \link{rmultinom}, \link{clonal.proportion} } tcR/man/dot-verbose.msg.Rd0000644000176200001440000000071013667026455015076 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{.verbose.msg} \alias{.verbose.msg} \title{Print the given message if second parameter is a TRUE.} \usage{ .verbose.msg(.message, .verbose = T) } \arguments{ \item{.message}{Character vector standing for a message.} \item{.verbose}{If T then print the given mesasge.} } \value{ Nothing. } \description{ Print the given message if second parameter is a TRUE. } tcR/man/cosine.similarity.Rd0000644000176200001440000001025313667026401015517 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/measures.R \name{cosine.similarity} \alias{cosine.similarity} \alias{similarity} \alias{tversky.index} \alias{overlap.coef} \alias{morisitas.index} \alias{jaccard.index} \alias{horn.index} \title{Set and vector similarity measures.} \usage{ cosine.similarity(.alpha, .beta, .do.norm = NA, .laplace = 0) tversky.index(x, y, .a = 0.5, .b = 0.5) overlap.coef(.alpha, .beta) jaccard.index(.alpha, .beta, .intersection.number = NA) morisitas.index(.alpha, .beta, .do.unique = T) horn.index(.alpha, .beta, .do.unique = T) } \arguments{ \item{.alpha, .beta, x, y}{Vector of numeric values for cosine similarity, vector of any values (like characters) for \code{tversky.index} and \code{overlap.coef}, matrix or data.frame with 2 columns for \code{morisitas.index} and \code{horn.index}, either two sets or two numbers of elements in sets for \code{jaccard.index}.} \item{.do.norm}{One of the three values - NA, T or F. If NA than check for distrubution (sum(.data) == 1) and normalise if needed with the given laplace correction value. if T then do normalisation and laplace correction. If F than don't do normalisaton and laplace correction.} \item{.laplace}{Value for Laplace correction.} \item{.a, .b}{Alpha and beta parameters for Tversky Index. Default values gives the Jaccard index measure.} \item{.do.unique}{if T then call unique on the first columns of the given data.frame or matrix.} \item{.intersection.number}{Number of intersected elements between two sets. See "Details" for more information.} } \value{ Value of similarity between the given sets or vectors. } \description{ Functions for computing similarity between two vectors or sets. See "Details" for exact formulas. - Cosine similarity is a measure of similarity between two vectors of an inner product space that measures the cosine of the angle between them. - Tversky index is an asymmetric similarity measure on sets that compares a variant to a prototype. - Overlap cofficient is a similarity measure related to the Jaccard index that measures the overlap between two sets, and is defined as the size of the intersection divided by the smaller of the size of the two sets. - Jaccard index is a statistic used for comparing the similarity and diversity of sample sets. - Morisita's overlap index is a statistical measure of dispersion of individuals in a population. It is used to compare overlap among samples (Morisita 1959). This formula is based on the assumption that increasing the size of the samples will increase the diversity because it will include different habitats (i.e. different faunas). - Horn's overlap index based on Shannon's entropy. Use the \link{repOverlap} function for computing similarities of clonesets. } \details{ For \code{morisitas.index} input data are matrices or data.frames with two columns: first column is elements (species or individuals), second is a number of elements (species or individuals) in a population. Formulas: Cosine similarity: \code{cos(a, b) = a * b / (||a|| * ||b||)} Tversky index: \code{S(X, Y) = |X and Y| / (|X and Y| + a*|X - Y| + b*|Y - X|)} Overlap coefficient: \code{overlap(X, Y) = |X and Y| / min(|X|, |Y|)} Jaccard index: \code{J(A, B) = |A and B| / |A U B|} For Jaccard index user can provide |A and B| in \code{.intersection.number} otherwise it will be computed using \code{base::intersect} function. In this case \code{.alpha} and \code{.beta} expected to be vectors of elements. If \code{.intersection.number} is provided than \code{.alpha} and \code{.beta} are exptected to be numbers of elements. Formula for Morisita's overlap index is quite complicated and can't be easily shown here, so just look at this webpage: http://en.wikipedia.org/wiki/Morisita%27s_overlap_index } \examples{ \dontrun{ jaccard.index(1:10, 2:20) a <- length(unique(immdata[[1]][, c('CDR3.amino.acid.sequence', 'V.gene')])) b <- length(unique(immdata[[2]][, c('CDR3.amino.acid.sequence', 'V.gene')])) # Next jaccard.index(a, b, repOverlap(immdata[1:2], .seq = 'aa', .vgene = T)) # is equal to repOverlap(immdata[1:2], 'jaccard', seq = 'aa', .vgene = T) } } \seealso{ \link{repOverlap}, \link{intersectClonesets}, \link{entropy}, \link{diversity} } tcR/man/repLoad.Rd0000644000176200001440000000324113667026401013437 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/io.R \name{repLoad} \alias{repLoad} \title{Parse input files or folders with immune receptor repertoire data.} \usage{ repLoad(.path, .format = c("mitcr", "migec")) } \arguments{ \item{.path}{Character vector with path to files and / or folders.} \item{.format}{String that specifies the input format.} } \description{ Load the immune receptor repertoire data from the given input: either a file name, a list of file names, a name of the folder with repertoire files, or a list of folders with repertoire files. The folder / folders must contain only files with the specified format. Input files could be either text files or archived with gzip ("filename.txt.gz") or bzip2 ("filename.txt.bz2"). For a general parser of table files with cloneset data see \code{\link{parse.cloneset}}. Parsers are available for: MiTCR ("mitcr"), MiTCR w/ UMIs ("mitcrbc"), MiGEC ("migec"), VDJtools ("vdjtools"), ImmunoSEQ ("immunoseq" or 'immunoseq2' for old and new formats respectively), MiXCR ("mixcr"), IMSEQ ("imseq") and tcR ("tcr", data frames saved with the `repSave()` function). Output of MiXCR should contain either all hits or best hits for each gene segment. Output of IMSEQ should be generated with parameter "-on". In this case there will be no positions of aligned gene segments in the output data frame due to restrictions of IMSEQ output. tcR's data frames should be saved with the `repSave()` function. For details on the tcR data frame format see \link{parse.file}. } \examples{ \dontrun{ datalist <- repLoad(c("file1.txt", "folder_with_files1", "another_folder"), "mixcr") } } \seealso{ \link{parse.file} } tcR/man/vis.radarlike.Rd0000644000176200001440000000161313667026401014610 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.radarlike} \alias{vis.radarlike} \title{Radar-like / spider-like plots.} \usage{ vis.radarlike(.data, .ncol = 3, .which = NA, .expand = c(0.25, 0)) } \arguments{ \item{.data}{Square data frame or matrix with row names and col names stands for objects and values for distances.} \item{.ncol}{Number of columns in the grid.} \item{.which}{Character vector, which datasets to show.} \item{.expand}{Integer vector of length 2, for \code{scale_y_continous(expand = .expand)} function.} } \description{ Plot a grid of radar(-like) plots for visualising a distance among objects. } \examples{ \dontrun{ load('immdata.rda') # Compute Jensen-Shannon divergence among V-usage of repertoires. imm.js <- js.div.seg(immdata, .verbose = F) # Plot it. vis.radarlike(imm.js) } } \seealso{ \link{repOverlap}, \link{js.div} } tcR/man/bootstrap.tcr.Rd0000644000176200001440000000351213667026455014667 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stats.R \name{bootstrap.tcr} \alias{bootstrap.tcr} \title{Bootstrap for data frames in package tcR.} \usage{ bootstrap.tcr( .data, .fun = entropy.seg, .n = 1000, .size = nrow(.data), .sim = c("uniform", "percentage"), .postfun = function(x) { unlist(x) }, .verbose = T, .prop.col = "Read.proportion", ... ) } \arguments{ \item{.data}{Data frame.} \item{.fun}{Function applied to each sample.} \item{.n}{Number of iterations (i.e., size of a resulting distribution).} \item{.size}{Size of samples. For \code{.sim} == "uniform" stands for number of rows to take. For \code{.sim} == "percentage" stands for number of UMIs / read counts to take.} \item{.sim}{A character string indicating the type of simulation required. Possible values are "uniform" or "percentage". See "Details" for more details of type of simulation.} \item{.postfun}{Function applied to the resulting list: list of results from each processed sample.} \item{.verbose}{if T then show progress bar.} \item{.prop.col}{Column with proportions for each clonotype.} \item{...}{Further values passed to \code{.fun}.} } \value{ Either result from \code{.postfun} or list of length \code{.n} with values of \code{.fun}. } \description{ Resample rows (i.e., clones) in the given data frame and apply the given function to them. } \details{ Argument \code{.sim} can take two possible values: "uniform" (for uniform distribution), when each row can be taken with equal probability, and "perccentage" when each row can be taken with probability equal to its "Read.proportion" column. } \examples{ \dontrun{ # Apply entropy.seg function to samples of size 20000 from immdata$B data frame for 100 iterations. bootstrap.tcr(immdata[[2]], .fun = entropy.seg, .n = 100, .size = 20000, .sim = 'uniform') } } tcR/man/get.deletions.alpha.Rd0000644000176200001440000000257213667026401015707 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataproc.R \name{get.deletions.alpha} \alias{get.deletions.alpha} \alias{get.deletions.beta} \title{Compute the number of deletions in MiTCR data frames.} \usage{ get.deletions.alpha(.data, .Vs = segments$TRAV, .Js = segments$TRAJ) get.deletions.beta(.data, .Vs = segments$TRBV, .Js = segments$TRBJ, .Ds = segments$TRBD) } \arguments{ \item{.data}{Mitcr data.frame.} \item{.Vs}{Table of V segments; must have 'V.segment' and 'Nucleotide.sequence' columns.} \item{.Js}{Table of J segments; must have 'J.segment' and 'Nucleotide.sequence' columns.} \item{.Ds}{Table of D segments; must have 'D.segment' and 'Nucleotide.sequence' columns.} } \value{ Mitcr data.frame with 3 (for alpha chains) or 5 (for beta chains) new columns for deletions. } \description{ Get deletions for VD, DJ, 5'D and 3'D ends and two columns with total deletions for VD/DJ and 5'D/3'D deletions for the given mitcr data.frame with 0-indexes columns. Cases, in which deletions cannot be determined, will have -1 in their cell. } \details{ By default, \code{*.table} parameters are taken from the \code{segments} data frame which can be loaded to your R environment with data(segments). Data for segments has been taken from IMGT. } \examples{ \dontrun{ data(segments) immdata <- get.deletions.beta(.data) immdata.prob <- tcr.prob.df(immdata) } } tcR/man/dot-fix.listnames.Rd0000644000176200001440000000050313667026455015430 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{.fix.listnames} \alias{.fix.listnames} \title{Fix names in lists.} \usage{ .fix.listnames(.datalist) } \arguments{ \item{.datalist}{List with data frames.} } \value{ List with fixed names. } \description{ Fix names in lists. } tcR/man/AA_TABLE.Rd0000644000176200001440000000127013667026455013252 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/docdata.R \docType{data} \name{AA_TABLE} \alias{AA_TABLE} \alias{AA_TABLE_REVERSED} \title{Tables with genetic code.} \format{ AA_TABLE: \code{Class 'table' Named chr [1:65] "K" "N" "K" "N" ... ..- attr(*, "names")= chr [1:65] "AAA" "AAC" "AAG" "AAT" ...} AA_TABLE_REVERSED: \code{List of 22 $ *: chr [1:3] "TAA" "TAG" "TGA" $ A: chr [1:4] "GCA" "GCC" "GCG" "GCT" $ C: chr [1:2] "TGC" "TGT" $ D: chr [1:2] "GAC" "GAT" ... } } \usage{ AA_TABLE } \description{ Tables with genetic code. } \examples{ \dontrun{ AA_TABLE['ATG'] # => "M" AA_TABLE_REVERSED['K'] # => list(K = c("AAA", "AAG")) } } \keyword{datasets} tcR/man/intersectClonesets.Rd0000644000176200001440000001252713667026401015740 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/crosses.R \name{intersectClonesets} \alias{intersectClonesets} \alias{intersectCount} \alias{intersectLogic} \alias{intersectIndices} \title{Intersection between sets of sequences or any elements.} \usage{ intersectClonesets(.alpha = NULL, .beta = NULL, .type = "n0e", .head = -1, .norm = F, .verbose = F) intersectCount(.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) intersectIndices(.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) intersectLogic(.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) } \arguments{ \item{.alpha}{Either first vector or data.frame or list with data.frames.} \item{.beta}{Second vector or data.frame or type of intersection procedure (see the \code{.type} parameter) if \code{.alpha} is a list.} \item{.type}{Types of intersection procedure if \code{.alpha} and \code{.beta} is data frames. String with 3 characters (see 'Details' for more information).} \item{.head}{Parameter for the \code{head} function, applied before intersecting.} \item{.norm}{If TRUE than normalise result by product of length or nrows of the given data.} \item{.verbose}{if T then produce output of processing the data.} \item{.method}{Method to use for intersecting string elements: 'exact' for exact matching, 'hamm' for matching strings which have <= 1 hamming distance, 'lev' for matching strings which have <= 1 levenshtein (edit) distance between them.} \item{.col}{Which columns use for fetching values to intersect. First supplied column matched with \code{.method}, others as exact values.} } \value{ \code{intersectClonesets} returns (normalised) number of similar elements or matrix with numbers of elements. \code{intersectCount} returns number of similar elements. \code{intersectIndices} returns 2-row matrix with the first column stands for an index of an element in the given \code{x}, and the second column stands for an index of an element of \code{y} which is similar to a relative element in \code{x}; \code{intersectLogic} returns logical vector of \code{length(x)} or \code{nrow(x)}, where TRUE at position \code{i} means that element with index {i} has been found in the \code{y} } \description{ Functions for the intersection of data frames with TCR / Ig data. See the \code{repOverlap} function for a general interface to all overlap analysis functions. \code{intersectClonesets} - returns number of similar elements in the given two clonesets / data frames or matrix with counts of similar elements among each pair of objects in the given list. \code{intersectCount} - similar to \code{tcR::intersectClonesets}, but with fewer parameters and only for two objects. \code{intersectIndices} - returns matrix M with two columns, where element with index M[i, 1] in the first given object is similar to an element with index M[i, 2] in the second given object. \code{intersectLogic} - returns logic vector with TRUE values in positions, where element in the first given data frame is found in the second given data frame. } \details{ Parameter \code{.type} of the \code{intersectClonesets} function is a string of length 3 [0an][0vja][ehl], where: \enumerate{ \item First character defines which elements intersect ("a" for elements from the column "CDR3.amino.acid.sequence", "n" for elements from the column "CDR3.nucleotide.sequence", other characters - intersect elements as specified); \item Second character defines which columns additionaly script should use ('0' for cross with no additional columns, 'v' for cross using the "V.gene" column, 'j' for cross using "J.gene" column, 'a' for cross using both "V.gene" and "J.gene" columns); \item Third character defines a method of search for similar sequences is use: "e" stands for the exact match of sequnces, "h" for match elements which have the Hamming distance between them equal to or less than 1, "l" for match elements which have the Levenshtein distance between tham equal to or less than 1. } } \examples{ \dontrun{ data(twb) # Equivalent to intersectClonesets(twb[[1]]$CDR3.nucleotide.sequence, # twb[[2]]$CDR3.nucleotide.sequence) # or intersectCount(twb[[1]]$CDR3.nucleotide.sequence, # twb[[2]]$CDR3.nucleotide.sequence) # First "n" stands for a "CDR3.nucleotide.sequence" column, "e" for exact match. twb.12.n0e <- intersectClonesets(twb[[1]], twb[[2]], 'n0e') stopifnot(twb.12.n0e == 46) # First "a" stands for "CDR3.amino.acid.sequence" column. # Second "v" means that intersect should also use the "V.gene" column. intersectClonesets(twb[[1]], twb[[2]], 'ave') # Works also on lists, performs all possible pairwise intersections. intersectClonesets(twb, 'ave') # Plot results. vis.heatmap(intersectClonesets(twb, 'ave'), .title = 'twb - (ave)-intersection', .labs = '') # Get elements which are in both twb[[1]] and twb[[2]]. # Elements are tuples of CDR3 nucleotide sequence and corresponding V-segment imm.1.2 <- intersectLogic(twb[[1]], twb[[2]], .col = c('CDR3.amino.acid.sequence', 'V.gene')) head(twb[[1]][imm.1.2, c('CDR3.amino.acid.sequence', 'V.gene')]) data(twb) ov <- repOverlap(twb) sb <- matrixSubgroups(ov, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))); vis.group.boxplot(sb) } } \seealso{ \link{repOverlap}, \link{vis.heatmap}, \link{ozScore}, \link{permutDistTest}, \link{vis.group.boxplot} } tcR/man/vis.number.count.Rd0000644000176200001440000000207313667026455015303 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{vis.number.count} \alias{vis.number.count} \title{Plot a histogram of counts.} \usage{ vis.number.count( .data, .ncol = 3, .name = "Histogram of clonotypes read counts", .col = "Read.count" ) } \arguments{ \item{.data}{Cloneset data frame or a list of clonesets.} \item{.ncol}{If .data is a list, than number of columns in a grid of histograms for each data frame in \code{.data}. Else not used.} \item{.name}{Title for this plot.} \item{.col}{Name of the column with counts.} } \value{ ggplot object. } \description{ Plot a histogram of distribution of counts of CDR3 nucleotide sequences. On y-axis are number of counts. } \details{ If \code{.data} is a data frame, than one histogram will be plotted. Is \code{.data} is a list, than grid of histograms will be plotted. } \examples{ \dontrun{ load('immdata.rda') # Plot one histogram with main title. vis.number.count(immdata[[1]], 'Main title here') # Plot a grid of histograms with 2 columns. vis.number.count(immdata, 2) } } tcR/man/pca2euclid.Rd0000644000176200001440000000167213667026401014072 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{pca2euclid} \alias{pca2euclid} \title{Compute the Euclidean distance among principal components.} \usage{ pca2euclid(.pcaobj, .num.comps = 2) } \arguments{ \item{.pcaobj}{An object returned by \code{prcomp}.} \item{.num.comps}{On how many principal components compute the distance.} } \value{ Matrix of distances. } \description{ Compute the Euclidean distance among principal components. } \examples{ \dontrun{ mat.ov <- repOverlap(AS_DATA, .norm = T) mat.gen.pca <- pca.segments(AS_DATA, T, .genes = HUMAN_TRBV) mat.ov.pca <- prcomp(mat.ov, scale. = T) mat.gen.pca.dist <- pca2euclid(mat.gen.pca) mat.ov.pca.dist <- pca2euclid(mat.ov.pca) permutDistTest(mat.gen.pca.dist, list()) permutDistTest(mat.ov.pca.dist, list()) } } \seealso{ \link{prcomp}, \link{pca.segments}, \link{repOverlap}, \link{permutDistTest} } tcR/man/twinsdata.Rd0000644000176200001440000000103613667026455014060 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/docdata.R \docType{data} \name{twinsdata} \alias{twinsdata} \alias{twa} \alias{twb} \title{Twins alpha-beta chain data} \format{ \code{twa} and \code{twb} are lists of 4 data frames with 10000 row in each. } \description{ \code{twa.rda}, \code{twb.rda} - data frames with downsampled to the 10000 most abundant clonesets and 4 samples data of twins data (alpha and beta chains). Link: http://labcfg.ibch.ru/tcr.html } \examples{ \dontrun{ data(twa) data(twb) } } tcR/man/permutDistTest.Rd0000644000176200001440000000320613667026455015063 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/crosses.R \name{permutDistTest} \alias{permutDistTest} \title{Monte Carlo permutation test for pairwise and one-vs-all-wise within- and inter-group differences in a set of repertoires.} \usage{ permutDistTest( .mat, .groups, .n = 1000, .fun = mean, .signif = 0.05, .plot = T, .xlab = "Values", .title = "Monte Carlo permutation testing of overlaps", .hjust = -0.1, .vjust = -4 ) } \arguments{ \item{.mat}{Symmetric matrix of repertoire distances.} \item{.groups}{Named list with names of repertoires in groups.} \item{.n}{Number of permutations for each pair of group.} \item{.fun}{A function to apply to distances.} \item{.signif}{Significance level. Below this value hypotheses counts as significant.} \item{.plot}{If T than plot the output results. Else return them as a data frame.} \item{.xlab}{X lab label.} \item{.title}{Main title of the plot.} \item{.hjust}{Value for adjusting the x coordinate of p-value labels on plots.} \item{.vjust}{Value for adjusting the y coordinate of p-value labels on plots.} } \description{ WARNING: this is an experimental procedure, work is still in progress. Perform permutation tests of distances among groups for the given groups of samples and matrix of distances among all samples. } \examples{ \dontrun{ data(twb) mat <- repOverlap(twb) permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))) permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D')), .fun = median) } } \seealso{ \link{repOverlap}, \link{intersectClonesets}, \link{ozScore}, \link{pca2euclid} } tcR/man/has.class.Rd0000644000176200001440000000055413667026401013734 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datatools.R \name{has.class} \alias{has.class} \title{Check if a given object has a given class.} \usage{ has.class(.data, .class) } \arguments{ \item{.data}{Object.} \item{.class}{String naming a class.} } \value{ Logical. } \description{ Check if a given object has a given class. } tcR/DESCRIPTION0000644000176200001440000000206613667667012012532 0ustar liggesusersPackage: tcR Type: Package Title: Advanced Data Analysis of Immune Receptor Repertoires Version: 2.3.2 Author: Vadim Nazarov Maintainer: Vadim Nazarov Description: Platform for the advanced analysis of T cell receptor and Immunoglobulin repertoires data and visualisation of the analysis results. Publication: Nazarov et al. (2015) . License: Apache License 2.0 Depends: R (>= 3.0.0), ggplot2 (>= 1.0.0), dplyr (>= 0.4.0), gridExtra (>= 0.9.0), reshape2 (>= 1.2.0), igraph (>= 0.7.1) Imports: utils (>= 3.1.0), Rcpp (>= 0.11.1), grid (>= 3.0.0), data.table (>= 1.9.0), gtable (>= 0.1.2), stringdist (>= 0.7.3), scales (>= 0.3.0) Suggests: knitr (>= 1.8), roxygen2 (>= 3.0.0), rmarkdown (>= 1.0) LinkingTo: Rcpp URL: https://immunarch.com/, http://imminfo.github.io/tcr/ BugReports: https://github.com/immunomind/immunarch/issues VignetteBuilder: knitr RoxygenNote: 7.1.0 NeedsCompilation: yes Packaged: 2020-06-09 00:07:49 UTC; vdn Repository: CRAN Date/Publication: 2020-06-09 11:10:02 UTC tcR/src/0000755000176200001440000000000013667551325011606 5ustar liggesuserstcR/src/neighbour.search.cpp0000644000176200001440000001734213667026402015540 0ustar liggesusers#include #include #include #include #include #include #include #include using namespace std; using namespace Rcpp; //----------------------------------------------- // Exact Match //----------------------------------------------- // if patterns is emptry, than use input sequences in trie for search. // [[Rcpp::export(".exact_search")]] std::vector exact_search(const std::vector& vec, const std::vector& patterns, int max_error = 1, bool verbose = true) { std::vector res; res.reserve(patterns.size() * 4); unordered_multimap string_set; for (int i = 0; i < vec.size(); i++) { string_set.insert(std::pair(vec[i], i)); } for (int j = 0; j < patterns.size(); j++) { std::pair::iterator, unordered_map::iterator> i = string_set.equal_range(patterns[j]); if (i.first != string_set.end()) { for (unordered_map::iterator k = i.first; k != i.second; k++) { res.push_back((*k).second + 1); res.push_back(j + 1); } } } return res; } // [[Rcpp::export(".exact_search_list")]] List exact_search_list(const std::vector& vec, const List patterns_list, int max_error = 1, bool verbose = true) { List out = List(); for (int list_ind = 0; list_ind < patterns_list.length(); list_ind++) { std::vector res; std::vector patterns = as >(out[list_ind]); res.reserve(patterns.size() * 4); unordered_multimap string_set; for (int i = 0; i < vec.size(); i++) { string_set.insert(std::pair(vec[i], i)); } for (int j = 0; j < patterns.size(); j++) { std::pair::iterator, unordered_map::iterator> i = string_set.equal_range(patterns[j]); if (i.first != string_set.end()) { for (unordered_map::iterator k = i.first; k != i.second; k++) { res.push_back((*k).second + 1); res.push_back(j + 1); } } } out[list_ind] = 0; } return out; } //----------------------------------------------- // Hamming Distance //----------------------------------------------- bool hamming_distance_check(const std::string& alpha, const std::string& beta, int max_error = 1) { if (alpha.size() != beta.size()) { return false; } int err = 0; for (int i = 0; i < alpha.size(); i++) { err += alpha[i] != beta[i]; if (err > max_error) { return false; } } return true; } // if patterns is empty, than use input sequences in trie for search. // [[Rcpp::export(".hamming_search")]] std::vector hamming_search(const std::vector& vec, const std::vector& patterns, int max_error = 1, bool verbose = true) { std::vector res; res.reserve(patterns.size() * 4); for (int i = 0; i < vec.size(); i++) { for (int j = 0; j < patterns.size(); j++) { if (hamming_distance_check(vec[i], patterns[j], max_error)) { res.push_back(i + 1); res.push_back(j + 1); } } } return res; } //----------------------------------------------- // Levenshtein Distance //----------------------------------------------- #define MAGIC_NUMBER 27 struct trie { struct nucmap { nucmap() { _data = new trie*[MAGIC_NUMBER]; for (int i = 0; i < MAGIC_NUMBER; i++) { _data[i] = NULL; } } ~nucmap() { for (int i = 0; i < MAGIC_NUMBER; i++) { delete _data[i]; } delete [] _data; } trie* operator[](char letter) { return _data[letter - 'A']; } trie* addTrie(char letter) { _data[letter - 'A'] = new trie(); return _data[letter - 'A']; } trie **_data; }; typedef nucmap next_t; // The set with all the letters which this node is prefix next_t next; //int index; vector index; trie() : next(nucmap()) { //index = 0; index.reserve(2); } ~trie() {} void insert(string w, int w_index = 0) { w = '[' + w; trie* n = this; for (int i = 0; i < w.size(); ++i) { if (!n->next[w[i]]) { n->next.addTrie(w[i]); } n = n->next[w[i]]; } //n->index = w_index; n->index.push_back(w_index); } }; // std::vector search_impl(trie* tree, char ch, int *last_row, int sz, const string& word, int min_cost) { int *current_row = new int[sz + 1]; current_row[0] = last_row[0] + 1; // Calculate the min cost of insertion, deletion, match or substution int insert_or_del, replace; for (int i = 1; i < sz + 1; ++i) { insert_or_del = min(current_row[i-1] + 1, last_row[i] + 1); replace = (word[i-1] == ch) ? last_row[i-1] : (last_row[i-1] + 1); current_row[i] = min(insert_or_del, replace); } // When we find a cost that is less than the min_cost, is because // it is the minimum until the current row, so we update std::vector res; //if ((current_row[sz] < min_cost) && (tree->index)) { if ((current_row[sz] < min_cost) && (tree->index.size() != 0)) { //res.push_back(tree->index); res.insert(res.end(), tree->index.begin(), tree->index.end()); } // If there is an element wich is smaller than the current minimum cost, // we can have another cost smaller than the current minimum cost if (*min_element(current_row, current_row + sz + 1) < min_cost) { for (int i = 'A'; i < 'A' + MAGIC_NUMBER; ++i) { if (tree->next[i]) { std::vector tmp = search_impl(tree->next[i], i, current_row, sz, word, min_cost); if (tmp.size() > 0) { res.insert(res.end(), tmp.begin(), tmp.end()); } } } } delete [] current_row; return res; } std::vector search(string word, int min_cost, trie* tree) { word = '[' + word; int sz = word.size(); int *current_row = new int[sz + 1]; // Naive DP initialization for (int i = 0; i < sz + 1; ++i) current_row[i] = i; std::vector res; // For each letter in the root map wich matches with a // letter in word, we must call the search for (int i = 0 ; i < sz; ++i) { if (tree->next[word[i]]) { std::vector tmp = search_impl(tree->next[word[i]], word[i], current_row, sz, word, min_cost); if (tmp.size() > 0) { res.insert(res.end(), tmp.begin(), tmp.end()); } } } delete [] current_row; return res; } // if patterns is emptry, than use input sequences in trie for search. // [[Rcpp::export(".levenshtein_search")]] std::vector levenshtein_search(const std::vector& vec, const std::vector& patterns, int max_error = 1, bool verbose = true) { // The tree trie tree; // The minimum cost of a given word to be changed to a word of the dictionary int min_cost = max_error + 1; for (int i = 0; i < vec.size(); i++) { tree.insert(vec[i], i + 1); } vector res; res.reserve(patterns.size() * 4); for (int i = 0; i < patterns.size(); i++) { // if (verbose && i % 100000 == 25) { // cout << i << "/" << patterns.size() << endl; // } vector tmp = search(patterns[i], min_cost, &tree); for (int j = 0; j < tmp.size(); j++) { res.push_back(tmp[j]); res.push_back(i + 1); } } return res; } tcR/src/Makevars0000644000176200001440000000002013667026402013263 0ustar liggesusersCXX_STD = CXX11 tcR/src/RcppExports.cpp0000644000176200001440000000743613667026445014615 0ustar liggesusers// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include using namespace Rcpp; // exact_search std::vector exact_search(const std::vector& vec, const std::vector& patterns, int max_error, bool verbose); RcppExport SEXP _tcR_exact_search(SEXP vecSEXP, SEXP patternsSEXP, SEXP max_errorSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< const std::vector& >::type vec(vecSEXP); Rcpp::traits::input_parameter< const std::vector& >::type patterns(patternsSEXP); Rcpp::traits::input_parameter< int >::type max_error(max_errorSEXP); Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); rcpp_result_gen = Rcpp::wrap(exact_search(vec, patterns, max_error, verbose)); return rcpp_result_gen; END_RCPP } // exact_search_list List exact_search_list(const std::vector& vec, const List patterns_list, int max_error, bool verbose); RcppExport SEXP _tcR_exact_search_list(SEXP vecSEXP, SEXP patterns_listSEXP, SEXP max_errorSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< const std::vector& >::type vec(vecSEXP); Rcpp::traits::input_parameter< const List >::type patterns_list(patterns_listSEXP); Rcpp::traits::input_parameter< int >::type max_error(max_errorSEXP); Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); rcpp_result_gen = Rcpp::wrap(exact_search_list(vec, patterns_list, max_error, verbose)); return rcpp_result_gen; END_RCPP } // hamming_search std::vector hamming_search(const std::vector& vec, const std::vector& patterns, int max_error, bool verbose); RcppExport SEXP _tcR_hamming_search(SEXP vecSEXP, SEXP patternsSEXP, SEXP max_errorSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< const std::vector& >::type vec(vecSEXP); Rcpp::traits::input_parameter< const std::vector& >::type patterns(patternsSEXP); Rcpp::traits::input_parameter< int >::type max_error(max_errorSEXP); Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); rcpp_result_gen = Rcpp::wrap(hamming_search(vec, patterns, max_error, verbose)); return rcpp_result_gen; END_RCPP } // levenshtein_search std::vector levenshtein_search(const std::vector& vec, const std::vector& patterns, int max_error, bool verbose); RcppExport SEXP _tcR_levenshtein_search(SEXP vecSEXP, SEXP patternsSEXP, SEXP max_errorSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< const std::vector& >::type vec(vecSEXP); Rcpp::traits::input_parameter< const std::vector& >::type patterns(patternsSEXP); Rcpp::traits::input_parameter< int >::type max_error(max_errorSEXP); Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); rcpp_result_gen = Rcpp::wrap(levenshtein_search(vec, patterns, max_error, verbose)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"_tcR_exact_search", (DL_FUNC) &_tcR_exact_search, 4}, {"_tcR_exact_search_list", (DL_FUNC) &_tcR_exact_search_list, 4}, {"_tcR_hamming_search", (DL_FUNC) &_tcR_hamming_search, 4}, {"_tcR_levenshtein_search", (DL_FUNC) &_tcR_levenshtein_search, 4}, {NULL, NULL, 0} }; RcppExport void R_init_tcR(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } tcR/vignettes/0000755000176200001440000000000013667551325013027 5ustar liggesuserstcR/vignettes/tcrvignette.Rmd0000644000176200001440000010676113667232050016033 0ustar liggesusers--- title: '

tcR: a package for T cell receptor and Immunoglobulin repertoires advanced data analysis

Vadim I. Nazarov

' author:

Laboratory of Comparative and Functional Genomics, IBCH RAS, Moscow, Russia

output: html_document: theme: spacelab toc: yes toc_depth: 4 pdf_document: toc: yes toc_depth: 4 word_document: default --- # IMPORTANT INFORMATION The tcR package WILL SOON BE ORPHANED AND REMOVED FROM CRAN. A new package is available that is designed to replace tcR: immunarch -- http://immunarch.com/ We will be happy to help you to move to the new package. Feel free to contact us: http://github.com/immunomind/immunarch Sincerely, immunarch dev team and Vadim I. Nazarov, lead developer of tcR ## Introduction The *tcR* package designed to help researchers in the immunology field to analyse T cell receptor (`TCR`) and immunoglobulin (`Ig`) repertoires. In this vignette, I will cover procedures for immune receptor repertoire analysis provided with the package. Terms: - Clonotype: a group of T / B cell clones with equal CDR3 nucleotide sequences and equal Variable genes. - Cloneset / repertoire: a set of clonotypes. Represented as a data frame in which each row corresponds to a unique clonotype. - UMI: Unique Molecular Identifier (see this [paper](http://www.nature.com/nmeth/journal/v9/n1/full/nmeth.1778.html) for details) ```{r eval=TRUE,echo=FALSE,warning=FALSE,message=FALSE} library(tcR) data(twa) data(twb) ``` ### Package features - Parsers for outputs of various tools for CDR3 extraction and genes alignment *(currently implemented parsers for MiTCR, MiGEC, VDJtools, ImmunoSEQ, IMSEQ and MiXCR)* - Data manipulation *(in-frame / out-of-frame sequences subsetting, clonotype motif search)* - Descriptive statistics *(number of reads, number of clonotypes, gene segment usage)* - Shared clonotypes statistics *(number of shared clonotypes, using V genes or not; sequential intersection among the most abundant clonotype ("top-cross"))* - Repertoire comparison *(Jaccard index, Morisita's overlap index, Horn's index, Tversky index, overlap coefficient)* - V- and J genes usage and it's analysis *(PCA, Shannon Entropy, Jensen-Shannon Divergence)* - Diversity evaluation *(ecological diversity index, Gini index, inverse Simpson index, rarefaction analysis)* - Artificial repertoire generation (beta chain only, for now) - Spectratyping - Various visualisation procedures - Mutation networks *(graphs, in which vertices represent CDR3 nucleotide / amino acid sequences and edges are connecting similar sequences with low hamming or edit distance between them)* ### Data in the package There are two datasets provided with the package - twins data and V(D)J recombination genes data. #### Downsampled twins data `twa.rda`, `twb.rda` - two lists with 4 data frames in each list. Every data frame is a sample downsampled to the 10000 most abundant clonotypes of twins data (alpha and beta chains). Full data is available here: [Twins TCR data at Laboratory of Comparative and Functional Genomics](http://labcfg.ibch.ru/tcr.html) Explore the data: ```{r eval=FALSE,echo=TRUE} # Load the package and load the data. library(tcR) data(twa) # "twa" - list of length 4 data(twb) # "twb" - list of length 4 # Explore the data. head(twa[[1]]) head(twb[[1]]) ``` #### Gene alphabets Gene alphabets - character vectors with names of genes for TCR and Ig. ```{r eval=FALSE,echo=TRUE} # Run help to see available alphabets. ?genealphabets ?genesegments data(genesegments) ``` ### Quick start / automatic report generation For the exploratory analysis of a single repertoire, use the RMarkdown report file at `"/inst/library.report.Rmd"` Analysis in the file include statistics and visualisation of number of clones, clonotypes, in- and out-of-frame sequences, unique amino acid CDR3 sequences, V- and J-usage, most frequent k-mers, rarefaction analysis. For the analysis of a group of repertoires ("cross-analysis"), use the RMarkdown report file at: `"/inst/crossanalysis.report.Rmd}"` Analysis in this file include statistics and visualisation of number of shared clones and clonotypes, V-usage for individuals and groups, J-usage for individuals, Jensen-Shannon divergence among V-usages of repertoires and top-cross. You need the *knitr* package installed in order to generate reports from default pipelines. In RStudio you can run a pipeline file as follows: `Run RStudio -> load the pipeline .Rmd files -> press the knitr button` ### Input parsing Currently in *tcR* there are implemented parser for the next software: - MiTCR - `parse.mitcr`; - MiTCR w/ UMIs - `parse.mitcrbc`; - MiGEC - `parse.migec`; - VDJtools - `parse.vdjtools`; - ImmunoSEQ - `parse.immunoseq`; - MiXCR - `parse.mixcr`; - IMSEQ - `parse.imseq`. Also a general parser `parse.cloneset` for a text table files is implemented. General wrapper for parsers is `parse.file`. User can also parse a list of files or the entire folder. Run `?parse.folder` to see a help on parsing input files and a list of functions for parsing a specific input format. ```{r eval=FALSE,echo=TRUE} # Parse file in "~/mitcr/immdata1.txt" as a MiTCR file. immdata1 <- parse.file("~/mitcr_data/immdata1.txt", 'mitcr') # equivalent to immdata1.eq <- parse.mitcr("~/mitcr_data/immdata1.txt") # Parse folder with MiGEC files. immdata <- parse.folder("~/migec_data/", 'migec') ``` ### Cloneset representation Clonesets represented in *tcR* as data frames with each row corresponding to the one nucleotide clonotype and with specific column names: - *Umi.count* - number of UMIs; - *Umi.proportion* - proportion of UMIs; - *Read.count* - number of reads; - *Read.proportion* - proportion of reads; - *CDR3.nucleotide.sequence* - CDR3 nucleotide sequence; - *CDR3.amino.acid.sequence* - CDR3 amino acid sequence; - *V.gene* - names of aligned Variable genes; - *J.gene* - names of aligned Joining genes; - *D.gene* - names of aligned Diversity genes; - *V.end* - last positions of aligned V genes (1-based); - *J.start* - first positions of aligned J genes (1-based); - *D5.end* - positions of D'5 end of aligned D genes (1-based); - *D3.end* - positions of D'3 end of aligned D genes (1-based); - *VD.insertions* - number of inserted nucleotides (N-nucleotides) at V-D junction (-1 for receptors with VJ recombination); - *DJ.insertions* - number of inserted nucleotides (N-nucleotides) at D-J junction (-1 for receptors with VJ recombination); - *Total.insertions* - total number of inserted nucleotides (number of N-nucleotides at V-J junction for receptors with VJ recombination). Any data frame with this columns and of this class is suitable for processing with the package, hence user can generate their own table files and load them for the further analysis using `read.csv`, `read.table` and other `base` R functions. Please note that *tcR* internally expects all strings to be of class "character", not "factor". Therefore you should use R parsing functions with parameter *stringsAsFactors=FALSE*. ```{r eval=TRUE, echo=TRUE} # No D genes is available here hence "" at "D.genes" and "-1" at positions. str(twa[[1]]) str(twb[[1]]) ``` ## Repertoire descriptive statistics For the exploratory analysis *tcR* provides various functions for computing descriptive statistics. ### Cloneset summary To get a general view of a subject's repertoire (overall count of sequences, in- and out-of-frames numbers and proportions) use the `cloneset.stats` function. It returns a `summary` of counts of nucleotide and amino acid clonotypes, as well as summary of read counts: ```{r eval=TRUE,echo=TRUE} cloneset.stats(twb) ``` For characterisation of a library use the `repseq.stats` function: ```{r eval=TRUE,echo=TRUE} repseq.stats(twb) ``` ### Most abundant clonotypes statistics Function `clonal.proportion` is used to get the number of most abundant by the count of reads clonotypes. E.g., compute number of clonotypes which fill up (approx.) the 25% from total repertoire's "Read.count": ```{r eval=TRUE,echo=TRUE} # How many clonotypes fill up approximately clonal.proportion(twb, 25) # the 25% of the sum of values in 'Read.count'? ``` To get a proportion of the most abundant clonotypes' sum of reads to the overall number of reads in a repertoire, use `top.proportion`, i.e. get ($\sum$ reads of top clonotypes)$/$($\sum$ reads for all clonotypes). E.g., get a proportion of the top-10 clonotypes' reads to the overall number of reads: ```{r echo=TRUE, eval=TRUE, fig=TRUE, fig.height=4, fig.width=5.5, message=FALSE, fig.align='center'} # What accounts a proportion of the top-10 clonotypes' reads top.proportion(twb, 10) # to the overall number of reads? vis.top.proportions(twb) # Plot this proportions. ``` Function `tailbound.proportion` with two arguments *.col* and *.bound* gets subset of the given data frame with clonotypes which have column *.col* with value $\leq$ *.bound* and computes the ratio of sums of count reads of such subset to the overall data frame. E.g., get proportion of sum of reads of sequences which has "Read.count" <= 100 to the overall number of reads: ```{r eval=TRUE,echo=TRUE} # What is a proportion of sequences which # have 'Read.count' <= 100 to the tailbound.proportion(twb, 100) # overall number of reads? ``` ### Clonal space homeostasis Clonal space homeostasis is a useful statistics of how many space occupied by clonotypes with specific proportions. ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=6.5, fig.align='center'} # data(twb) # Compute summary space of clones, that occupy # [0, .05) and [.05, 1] proportion. clonal.space.homeostasis(twb, c(Low = .05, High = 1)) # Use default arguments: clonal.space.homeostasis(twb[[1]]) twb.space <- clonal.space.homeostasis(twb) vis.clonal.space(twb.space) ``` ### In-frame and out-of-frame sequences Functions for performing subsetting and counting number of in-frame and out-of-frame clonotypes are: `count.inframes`, `count.outframes`, `get.inframes`, `get.outframes`. Parameter *.head* for this functions is a parameter to the *.head* function, that applied to the input data frame or an input list of data frames before subsetting. Functions accept both data frames and list of data frames as parameters. E.g., get data frame with only in-frame sequences and count out-of-frame sequences in the first 5000 rows for this data frame: ```{r eval=TRUE,echo=TRUE} imm.in <- get.inframes(twb) # Return all in-frame sequences from the 'twb'. # Count the number of out-of-frame sequences count.outframes(twb, 5000) # from the first 5000 sequences. ``` General functions with parameter stands for 'all' (all sequences), 'in' (only in-frame sequences) or 'out' (only out-of-frame sequences) are *get.frames* and *count.frames*: ```{r eval=TRUE,echo=TRUE} imm.in <- get.frames(twb, 'in') # Similar to 'get.inframes(twb)'. count.frames(twb[[1]], 'all') # Just return number of rows. flag <- 'out' count.frames(twb, flag, 5000) # Similar to 'count.outframes(twb, 5000)'. ``` ### Search for a target CDR3 sequences For exact or fuzzy search of sequences the package employed a function `find.clonotypes`. Input arguments for this function are a data frame or a list of data frames, targets (a character vector or data frame having one column with sequences and additional columns with, e.g., V genes), a value of which column or columns to return, a method to be used to compare sequences among each other (either "exact" for exact matching, "hamm" for matching sequences by Hamming distance (two sequences are matched if H $\leq$ 1) or "lev" for matching sequences by Levenshtein distance (two sequences are matched if L $\leq$ 1)), and column name from which sequences for matching are obtained. Sounds very complex, but in practice it's very easy, therefore let's go to examples. Suppose we want to search for some CDR3 sequences in a number of repertoires: ```{r eval=TRUE,echo=TRUE} cmv <- data.frame(CDR3.amino.acid.sequence = c('CASSSANYGYTF', 'CSVGRAQNEQFF', 'CASSLTGNTEAFF', 'CASSALGGAGTGELFF', 'CASSLIGVSSYNEQFF'), V.genes = c('TRBV4-1', 'TRBV4-1', 'TRBV4-1', 'TRBV4-1', 'TRBV4-1'), stringsAsFactors = F) cmv ``` We will search for them using all methods of matching (exact, hamming or levenshtein) and with and without matching by V-segment. Also, for the first case (exact matching and without V gene) we return "Total.insertions" column along with the "Read.count" column, and for the second case output will be a "Rank" - rank (generated by `set.rank`) of a clone or a clonotype in a data frame. ```{r eval=TRUE,echo=TRUE} twb <- set.rank(twb) # Case 1. cmv.imm.ex <- find.clonotypes(.data = twb[1:2], .targets = cmv[,1], .method = 'exact', .col.name = c('Read.count', 'Total.insertions'), .verbose = F) head(cmv.imm.ex) # Case 2. # Search for CDR3 sequences with hamming distance <= 1 # to the one of the cmv$CDR3.amino.acid.sequence with # matching V genes. Return ranks of found sequences. cmv.imm.hamm.v <- find.clonotypes(twb[1:3], cmv, 'hamm', 'Rank', .target.col = c('CDR3.amino.acid.sequence', 'V.gene'), .verbose = F) head(cmv.imm.hamm.v) # Case 3. # Similar to the previous example, except # using levenshtein distance and the "Read.count" column. cmv.imm.lev.v <- find.clonotypes(twb[1:3], cmv, 'lev', .target.col = c('CDR3.amino.acid.sequence', 'V.gene'), .verbose = F) head(cmv.imm.lev.v) ``` ## Gene usage Variable and Joining gene usage (V-usage and J-usage) are important characteristics of repertoires. To access and compare them among repertoires *tcR* provides a few useful functions. ### Gene usage computing To access V- and J-usage of a repertoire *tcR* provides functions `geneUsage`. Function `geneUsage`, depending on parameters, computes frequencies or counts of the given elements (e.g., V genes) of the input data frame or the input list of data frames. V and J gene names for humans for TCR and Ig are stored in the .rda file `genesegments.rda` (they are identical to those form IMGT: \href{http://www.imgt.org/IMGTrepertoire/index.php?section=LocusGenes&repertoire=nomenclatures&species=human&group=TRBV}{link to beta genes (red ones)} and \href{http://www.imgt.org/IMGTrepertoire/index.php?section=LocusGenes&repertoire=nomenclatures&species=human&group=TRAV}{link to alpha genes (red ones)}). All of the mentioned functions are accept data frames as well as list of data frames. Output for those functions are data frames with the first column stands for a gene and the other for frequencies. ```{r eval=TRUE,echo=TRUE} imm1.vs <- geneUsage(twb[[1]], HUMAN_TRBV) head(imm1.vs) imm.vs.all <- geneUsage(twb, HUMAN_TRBV) imm.vs.all[1:10, 1:4] ``` You can also directly visualise gene usage with the function `vis.gene.usage` (if you pass the gene alphabet as a second argument): ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} # Put ".dodge = F" to get distinct plot for every data frame in the given list. vis.gene.usage(twb, HUMAN_TRBJ, .main = 'twb J-usage dodge', .dodge = T) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=6, fig.width=9} vis.gene.usage(twb, HUMAN_TRBJ, .main = 'twb J-usage column', .dodge = F, .ncol = 2) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} vis.gene.usage(imm1.vs, NA, .main = 'twb[[1]] V-usage', .coord.flip = F) ``` ### Gene usage comparing To evaluate V- and J genes usage of repertoires, the package implements subroutines for two approaches to the analysis: measures from the information theory and PCA (Principal Component Analysis). #### Shannon entropy and Jensen-Shannon divergence To assess the diversity of genes usage user can use the `entropy` function. Kullback-Leibler assymetric measure (function `kl.div`) and Jensen-Shannon symmetric measure (functions `js.div` for computing JS-divergence between the given distributions and `js.div.seg` for computing JS-divergence between genes distributions of two clonesets or a list with data frames) are provided to estimate distance among gene usage of different repertoires. To visualise distances *tcR* employed the `vis.radarlike` function, see Section "Plots" for more detailed information. ```{r eval=T, echo=TRUE, fig.align='center'} # Transform "0:100" to distribution with Laplace correction entropy(0:100, .laplace = 1) # (i.e., add "1" to every value before transformation). entropy.seg(twb, HUMAN_TRBV) # Compute entropy of V-segment usage for each data frame. js.div.seg(twb[1:2], HUMAN_TRBV, .verbose = F) imm.js <- js.div.seg(twb, HUMAN_TRBV, .verbose = F) vis.radarlike(imm.js, .ncol = 2) ``` #### Principal Component Analysis (PCA) Principal component analysis (PCA) is a statistical procedure for transforming a set of observations to a set of special values for analysis. In *tcR* implemented functions `pca.segments` for performing PCA on V- or J-usage, and `pca.segments.2D` for performing PCA on VJ-usage. For plotting the PCA results see the `vis.pca` function. ```{r eval=TRUE, echo=TRUE, fig.align='center', fig.height=4.5, fig.width=6} pca.segments(twb, .genes = HUMAN_TRBV) # Plot PCA results of V-segment usage. # Return object of class "prcomp" class(pca.segments(twb, .do.plot = F, .genes = HUMAN_TRBV)) ``` ## Repertoire overlap analysis *tcR* provides a number of functions for evaluating similarity of clonesets based on shared among clonesets clonotypes and working with data frames with shared clonotypes. ### Overlap quantification The general interface to all functions for computing cloneset overlap coefficients is the `repOverlap` function. #### Number of shared clonotypes The most straightforward yet a quite effective way to evaluate similarity of two clonesets is compute the number of shared clonotypes. *tcR* adds the new function `intersectClonesets` (`repOverlap(your_data, 'exact')`) which is by default computes the number of shared clonotypes using the "CDR3.nucleotide.sequence" columns of the given data frames, but user can change target columns by using arguments *.type* or *.col*. As in the `find.clonotypes`, user can choose which method apply to the elements: exact match of elements, match by Hamming distance or match by Levenshtein distance. Logical argument *.norm* is used to perform normalisation of the number of shared clonotypes by dividing this number by multiplication of clonesets' sizes (**strongly** recommended otherwise your results will be correlating with clonesets' sizes). ```{r eval=TRUE, echo=T, fig.align='center', warning=FALSE} # Equivalent to intersect(twb[[1]]$CDR3.nucleotide.sequence, # twb[[2]]$CDR3.nucleotide.sequence) repOverlap(twb[1:2], 'exact', 'nuc', .verbose = F) # Equivalent to intersectClonesets(twb, "n0e", .norm = T) repOverlap(twb, 'exact', 'nuc', .norm = T, .verbose = F) # Intersect by amino acid clonotypes + V genes repOverlap(twb, 'exact', 'aa', .vgene = T, .verbose = F) # Plot a heatmap of the number of shared clonotypes. vis.heatmap(repOverlap(twb, 'exact', 'aa', .vgene = T, .verbose = F), .title = 'twb - (ave)-intersection', .labs = '') ``` See the `vis.heatmap` function in the Section "Visualisation" for the visualisation of the intersection results. Functions `intersectCount`, `intersectLogic` and `intersectIndices` are more flexible in terms of choosing which columns to match. They all have parameter *.col* that specifies names of columns which will used in computing intersection. Function `intersectCount` returns number of similar elements; `intersectIndices(x, y)` returns 2-column matrix with the first column stands for an index of an element in the given *x*, and the second column stands for an index of that element of *y* which is similar to a relative element in *x*; `intersectLogic(x, y)` returns a logical vector of *length(x)* or *nrow(x)*, where TRUE at position *i* means that element with index {i} has been found in the *y*. ```{r eval=TRUE, echo=TRUE} # Get logic vector of shared elements, where # elements are tuples of CDR3 nucleotide sequence and corresponding V-segment imm.1.2 <- intersectLogic(twb[[1]], twb[[2]], .col = c('CDR3.amino.acid.sequence', 'V.gene')) # Get elements which are in both twb[[1]] and twb[[2]]. head(twb[[1]][imm.1.2, c('CDR3.amino.acid.sequence', 'V.gene')]) ``` #### "Top cross" Number of shared clonotypes among the most abundant clonotypes may differ signigicantly from those with lesses count. To support research *tcR* offers the `top.cross` function, that apply `tcR::intersectClonesets`, e.g., to the first 1000 clonotypes, 2000, 3000 and so on up to the first 100000 clones, if supplied `.n == seq(1000, 100000, 1000)`. ```{r eval=TRUE, echo=T, fig.align='center', fig.height=6.5, fig.width=10, warning=FALSE} twb.top <- top.cross(.data = twb, .n = seq(500, 10000, 500), .verbose = F, .norm = T) top.cross.plot(twb.top) ``` #### More complex similarity measures *tcR* also provides more complex similarity measures for evaluating the similarity of sets. - Tversky index (`repOverlap(your_data, 'tversky')` for clonesets or `tversky.index` for vectors) is an asymmetric similarity measure on sets that compares a variant to a prototype. If using default arguments, it's similar to Dice's coefficient. - Overlap coefficient (`repOverlap(your_data, 'overlap')` for clonesets or `overlap.coef` for vectors) is a similarity measure that measures the overlap between two sets, and is defined as the size of the intersection divided by the smaller of the size of the two sets. - Jaccard index (`repOverlap(your_data, 'jaccard')` for clonesets or `jaccard.index` for vectors) is a statistic used for comparing the similarity and diversity of sample sets. - Morisita's overlap index (`repOverlap(your_data, 'morisita')` for clonesets or `morisitas.index` for other data) is a statistical measure of dispersion of individuals in a population and is used to compare overlap among samples. The formula is based on the assumption that increasing the size of the samples will increase the diversity because it will include different habitats (i.e. different faunas) (Morisita, 1959). To visualise similarity among repertoires the `vis.heatmap` function is appropriate. ```{r eval=TRUE, echo=TRUE, results='hold'} # Apply the Morisitas overlap index to the each pair of repertoires. # Use information about V genes (i.e. one CDR3 clonotype is equal to another # if and only if their CDR3 aa sequences are equal and their V genes are equal) repOverlap(twb, 'morisita', 'aa', 'read.count', .vgene = T, .verbose = F) ``` ### Overlap statistics and tests #### Overlap Z-score (OZ-score) - a measure for ???abnormality??? in overlaps `ozScore` #### Monte Carlo permutation test for pairwise and one-vs-all-wise within- and inter-group differences in a set of repertoires `permutDistTest` `pca2euclid` ### Shared repertoire To investigate a shared among a several repertoires clonotypes ("shared repertoire") the package provided the `shared.repertoire` function along with functions for computing the shared repertoire statistics. The `shared.representation` function computes the number of shared clonotypes for each repertoire for each degree of sharing (i.e., number of people, in which indicated amount of clones have been found). The function `shared.summary` is equivalent to `repOverlap(, 'exact')` but applies to the shared repertoire data frame. Measuring distances among repertoires using the cosine similarity on vector of counts of shared sequences is also possible with the `cosine.sharing` function. ```{r eval=TRUE, echo=TRUE} # Compute shared repertoire of amino acid CDR3 sequences and V genes # which has been found in two or more people and return the Read.count column # of such clonotypes from each data frame in the input list. imm.shared <- shared.repertoire(.data = twb, .type = 'avrc', .min.ppl = 2, .verbose = F) head(imm.shared) shared.representation(imm.shared) # Number of shared sequences. ``` ## Diversity evaluation For assessing the distribution of clonotypes in the given repertoire, *tcR* provides functions for evaluating the diversity (functions `diversity` and `inverse.simpson`) and the skewness of the clonal distribution (functions `gini` and `gini.simpson`), and a general interface to all of this functions `repDiversity`, which user should use to estimate the diversity of clonesets. Function `diversity` (`repDiversity(your_clonesets, "div")`) computes the ecological diversity index (with parameter `.q` for penalties for clones with large count). Function `inverse.simpson` (`repDiversity(your_clonesets, "inv.simp")`) computes the Inverse Simpson Index (i.e., inverse probability of choosing two similar clonotypes). Function `gini` (`repDiversity(your_clonesets, "gini")`) computes the economical Gini index of clonal distribution. Function `gini.simpson` (`repDiversity(your_clonesets, "gini.simp")`) computes the Gini-Simpson index. Function `chao1` (`repDiversity(your_clonesets, "chao1")`) computes the Chao1 index, its SD and two 95 perc CI. Function `repDiversity` accepts single clonesets as well as a list of clonesets. Parameter `.quant` specifies which column to use for computing the diversity (print `?repDiversity` to see more information about input arguments). ```{r eval=TRUE, echo=TRUE, results='hold'} # Evaluate the diversity of clones by the ecological diversity index. repDiversity(twb, 'div', 'read.count') sapply(twb, function (x) diversity(x$Read.count)) ``` ```{r eval=TRUE, echo=TRUE, results='hold'} # Compute the diversity as the inverse probability of choosing two similar clonotypes. repDiversity(twb, 'inv.simp', 'read.prop') sapply(twb, function (x) inverse.simpson(x$Read.proportion)) ``` ```{r eval=TRUE, echo=TRUE, results='hold'} # Evaluate the skewness of clonal distribution. repDiversity(twb, 'gini.simp', 'read.prop') sapply(twb, function (x) gini.simpson(x$Read.proportion)) ``` ```{r eval=TRUE, echo=TRUE, results='hold'} # Compute diversity of repertoire using Chao index. repDiversity(twb, 'chao1', 'read.count') sapply(twb, function (x) chao1(x$Read.count)) ``` ## Visualisation ### CDR3 length and read count distributions plot Plots of the distribution of CDR3 nucleotide sequences length (function `vis.count.len`) and the histogram of counts (function `vis.number.count`). Input data is either a data frame or a list with data frames. Argument *.col* specifies column's name with clonotype counts. Argument *.ncol* specifies a number of columns in a plot with multiple distribution, i.e., if the input data is a list with data frames. ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=5.5, fig.align='center'} vis.count.len(twb[[1]], .name = "twb[[1]] CDR3 lengths", .col = "Read.count") ``` ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=5.5, fig.align='center', warning=FALSE, message=FALSE} # I comment this to avoid a strange bug in ggplot2. Will uncomment later. # vis.number.count(twb[[1]], .name = "twb[[1]] count distribution") ``` ### Top proportions bar plot For the visualisation of proportions of the most abundant clonotypes in a repertoire *tcR* offers the `vis.top.proportions` function. As input the function receives either data frame or a list with data frames (argument *.data*), an integer vector with number of clonotypes for computing proportions of count for this clonotypes (argument *.head*), and a column's name with clonotype counts (argument *.col*). ```{r echo=TRUE, eval=TRUE, fig.height=4, fig.width=5.5, message=FALSE, fig.align='center'} vis.top.proportions(twb, c(10, 500, 3000, 10000), .col = "Read.count") ``` ### Clonal space homeostasis bar plot For the visualisation of how much space occupied each group of clonotypes, divided into groups by their proportions in the data, use the `vis.clonal.space` function. As an input it receives the output of the `clonal.space.homeostasis` function. ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=6.5, fig.align='center'} twb.space <- clonal.space.homeostasis(twb) vis.clonal.space(twb.space) ``` ### Heat map Pairwise distances or similarity of repertoires can be represented as qudratic matrices, in which each row and column represented a cloneset, and each value in every cell (i, j) is a distance between repertoires with indices i and j. One way to visalise such matrices is using "heatmaps". For plotting heatmaps in *tcR* implemented the `vis.heatmap` function. With changing input arguments user can change names of labs, title and legend. ```{r eval=TRUE, echo=TRUE, fig.align='center', warning=FALSE, message=FALSE} twb.shared <- repOverlap(twb, "exact", .norm = F, .verbose = F) vis.heatmap(twb.shared, .title = "Twins shared nuc clonotypes", .labs = c("Sample in x", "Sample in y"), .legend = "# clonotypes") ``` ### Radar-like plot Another way to repsent distances among objects is "radar-like" plots (because this plots is not exactly radar plots) realised in *tcR* throught the `vis.radarlike` function. Argument *.ncol* specifies a number of columns of radar-like plots in a viewport. ```{r eval=T, echo=TRUE, fig.align='center'} twb.js <- js.div.seg(twb, HUMAN_TRBV, .verbose = F) vis.radarlike(twb.js, .ncol = 2) ``` ### Gene usage histogram For the visualisation of gene usage *tcR* employes subroutines for making classical histograms using the `vis.gene.usage` function. The function accept clonesets, lists of clonesets or output from the `geneUsage` function. If input is a cloneset(s), then user should specify a gene alphabet (e.g., `HUMAN_TRBV`) in order to compute the gene usage. Using a parameter \code{.dodge}, user can change type of the output between an output as histograms for each cloneset in the input list (`.dodge = F`) or an output as an one histogram for all data, which is very useful for comparing distribution of genes (`.dodge = T`). If `.dodge=F` and input are lists of clonesets or a gene usage of a few clonesets, than user with argument `.ncol` can specify how many columns of histograms will be outputted. With `.coord.flip` user can flip coordinates so genes will be at the left side of the plot. ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} vis.gene.usage(twb[[1]], HUMAN_TRBV, .main = 'Sample I V-usage') ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=7, fig.width=5} vis.gene.usage(twb[[2]], HUMAN_TRBV, .main = 'Sample II V-usage', .coord.flip = T) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} twb.jusage <- geneUsage(twb, HUMAN_TRBJ) vis.gene.usage(twb.jusage, .main = 'Twins J-usage', .dodge = T) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=6, fig.width=9} vis.gene.usage(twb, HUMAN_TRBJ, .main = 'Twins J-usage', .dodge = F, .ncol = 2) ``` ### PCA visualisation For the visualisation of results from the `prcomp` function (i.e., objects of class `prcomp`), *tcR* provides the `vis.pca` function. Input arguments for the function are an object of class `prcomp` and a (if needed) list with groups (vectors of indices of samples) for colouring points in the plot. ```{r eval=TRUE, echo=TRUE, fig.align='center', fig.height=4.5, fig.width=6} twb.pca <- pca.segments(twb, .do.plot = F) vis.pca(pca.segments(twb, .do.plot = F, .genes = HUMAN_TRBV), .groups = list(GroupA = c(1,2), GroupB = c(3,4))) ``` ### Logo-like plot Logo-like graphs for visualisation of nucleotide or amino acid motif sequences / profiles. ```{r eval=TRUE, echo=TRUE, fig.align='center', fig.width=6, fig.height=5.5, warning=FALSE, message=FALSE} km <- get.kmers(twb[[1]]$CDR3.amino.acid.sequence, .head = 100, .k = 7, .verbose = F) d <- kmer.profile(km) vis.logo(d) ``` ## Mutation networks Mutation network (or a mutation graph) is a graph with vertices representing nucleotide or in-frame amino acid sequences (out-of-frame amino acid sequences will be automatically filtered out by *tcR* functions for mutation network creating) and edges which connecting pairs of sequences with hamming distance (parameter *.method* = 'hamm') or edit distance (parameter *.method* = 'lev') between them no more than specified in the *.max.errors* function parameter of the `mutation.network` function. To create a mutation network first what you need is to make a shared repertoires and then apply the `mutation.network` function to this shared repertoire: ```{r eval=TRUE, echo=TRUE} # data(twb) twb.shared <- shared.repertoire(twb, .head = 1000, .verbose = F) G <- mutation.network(twb.shared) G ``` To manipulate vertex attributes functions \code{set.group.vector} and \code{get.group.names} are provided. ```{r eval=TRUE, echo=TRUE} # data(twb) # twb.shared <- shared.repertoire(twb, .head = 1000) # G <- mutation.network(twb.shared) G <- set.group.vector(G, "twins", list(A = c(1,2), B = c(3,4))) # <= refactor this get.group.names(G, "twins", 1) get.group.names(G, "twins", 300) get.group.names(G, "twins", c(1,2,3), F) get.group.names(G, "twins", 300, F) # Because we have only two groups, we can assign more readable attribute. V(G)$twin.names <- get.group.names(G, "twins") V(G)$twin.names[1] V(G)$twin.names[300] ``` To access neighbour vertices of vertices ("ego-network") use the \code{mutation.neighbours} function: ```{r eval=TRUE, echo=TRUE} # data(twb) # twb.shared <- shared.repertoire(twb, .head = 1000) # G <- mutation.network(twb.shared) head(mutated.neighbours(G, 1)[[1]]) ``` ## Conclusion Feel free to contact me for the package-related or immunoinformatics research-related questions. ## Appendix A: Kmers manipulation and processing In the package implemented functions for working with k-mers. Function `get.kmers` generates k-mers from the given chatacter vector or a data frame with columns for sequences and a count for each sequence. ```{r eval=TRUE, echo=TRUE} head(get.kmers(twb[[1]]$CDR3.amino.acid.sequence, 100, .meat = F, .verbose = F)) head(get.kmers(twb[[1]], .meat = T, .verbose = F)) ``` ## Appendix B: Nucleotide and amino acid sequences manipulation The package also provides a several number of functions for performing classic bioinformatics tasks on strings. For more powerful subroutines see the Bioconductor's *Biostrings* package. ### Nucleotide sequence manipulation Functions for basic nucleotide sequences manipulations: reverse-complement, translation and GC-content computation. All functions are vectorised. ```{r eval=TRUE, echo=TRUE} revcomp(c('AAATTT', 'ACGTTTGGA')) cbind(bunch.translate(twb[[1]]$CDR3.nucleotide.sequence[1:10]), twb[[1]]$CDR3.amino.acid.sequence[1:10]) gc.content(twb[[1]]$CDR3.nucleotide.sequence[1:10]) ``` ### Reverse translation subroutines Function `codon.variants` returns a list of vectors of nucleotide codons for each letter for each input amino acid sequence. Function `translated.nucl.sequences` returns the number of nucleotide sequences, which, when translated, will result in the given amino acid sequence(s). Function `reverse.translation` return all nucleotide sequences, which is translated to the given amino acid sequences. Optional argument `.nucseq` for each of this function provides restriction for nucleotides, which cannot be changed. All functions are vectorised. ```{r eval=TRUE, echo=TRUE} codon.variants('LQ') translated.nucl.sequences(c('LQ', 'CASSLQ')) reverse.translation('LQ') translated.nucl.sequences('LQ', 'XXXXXG') codon.variants('LQ', 'XXXXXG') reverse.translation('LQ', 'XXXXXG') ``` tcR/NEWS0000644000176200001440000000001313667026401011500 0ustar liggesusersVERSION 1.3tcR/R/0000755000176200001440000000000013667551225011217 5ustar liggesuserstcR/R/kmers.R0000644000176200001440000003314413667033467012473 0ustar liggesusers########## Statistics and analysis of k-mers usage ########## #' Get kmers from sequences. #' #' @aliases get.kmers #' #' @description #' Get vector of kmers from the given character vector or data frame. #' #' @param .data Either character vector or a data.frame. #' @param .head Parameter for head function applied to the given data before kmer generation. #' @param .k Size of the kmer. #' @param .clean if T then remove sequences which contain '~' or '*' symbols. Useful for deleting out-of-frame aminoacid sequnces. #' @param .meat if TRUE than .data must be data.frame with columns CDR3.amino.acid.sequence and Read.count. #' @param .verbose if T then print progress. #' @param .left.shift Cut all \code{.left.shift} symbols from the left side for each sequence. #' @param .right.shift Cut all \code{.right.shift} symbols from the right side for each sequence. #' #' @return Data.frame with 2 columns Kmers and Count / Rank / Proportion relatively to the .value param #' or a list with such data.frames if .data is a list. get.kmers <- function (.data, .head = -1, .k = 5, .clean = T, .meat = F, .verbose = T, .left.shift = 0, .right.shift = 0) { if (class(.data) == 'list') { ngrams <- lapply(.data, get.kmers, .head = .head, .k = .k, .clean = .clean, .meat = .meat, .left.shift = .left.shift, .right.shift = .right.shift, .verbose = .verbose) res <- ngrams[[1]] if (.verbose) cat('Merging all data.frames together...\n') for (i in 2:length(.data)) { cat(i, '/', length(.data), '\n') res <- merge(res, ngrams[[i]], by = 'Kmers', all = T) names(res) <- c('Kmers', names(.data)[1:i]) } if (.verbose) cat('Done.\n') res[is.na(res)] <- 0 return(res) } .n <- .k if (.head == -1) { .head <- dim(.data)[1] if (is.null(.head)) { .head <- length(.data) } } .data <- head(.data, .head) read.count <- rep.int(1, .head) if (.meat) { read.count <- .data$Read.count } if (class(.data) == 'data.frame') { .data <- .data$CDR3.amino.acid.sequence } if (.clean) { if (.verbose) cat('Cleaning bad sequences...\t') .data <- .data[grep('[*, ~]', .data, invert = T)] if (.verbose) cat('Sequences after cleaning:', length(.data),'\n') } if (.verbose) cat('Calculating space...\n') .data <- substr(.data, 1 + .left.shift, nchar(.data) - .right.shift) non.nchar <- nchar(.data) >= .n .data <- .data[non.nchar] read.count <- read.count[non.nchar] space <- sum(nchar(.data) -.n + 1) if (.verbose) cat('Number of k-mers:', space,'\n') if (space > 0) { if (.verbose) { cat('Generating k-mers...\n') pb <- set.pb(space) } res <- rep(x='', times=space) meat <- rep(1, times = space) j <- 1 for (i in 1:length(.data)) { ngrams <- sapply(1:(nchar(.data[i]) - .n + 1), function(j) substr(.data[i], j, j + .n - 1), USE.NAMES=F) kmer.meat <- read.count[i] for (ngram in ngrams) { res[j] <- ngram meat[j] <- kmer.meat j <- j + 1 if (.verbose) add.pb(pb) } } if (.verbose) close(pb) meat <- meat[order(res)] res <- sort(res) dup <- duplicated(res) res.unique <- res[!dup] res.dup <- res[dup] # res now is a vector for storing number of strings, i.e. res[i] == # of res.unique[i] res <- meat[!dup] meat <- meat[dup] # res <- rep.int(x=1, times=length(res.unique)) j <- 1 if (.verbose) cat('Unique k-mers:', length(res.unique), '\n') if (.verbose) cat('Merging k-mers...\n') for (i in 1:length(res.unique)) { while (res.dup[j] == res.unique[i] && j <= length(res.dup)) { # res[i] <- res[i] + 1 res[i] <- res[i] + meat[j] j <- j + 1 } } if (.verbose) cat('Done.\n') res <- data.frame(Kmers = res.unique, Count = res, stringsAsFactors=F) res <- res[order(res$Count, decreasing = T),] row.names(res) <- NULL res } else { data.frame(Kmers = NA, Count = NA) } } #' Make and manage the table of the most frequent k-mers. #' #' @aliases kmer.table get.kmer.column #' #' @description #' \code{kmer.table} - generate table with the most frequent k-mers. #' #' \code{get.kmer.column} - get vector of k-mers from the k-mer table from the function \code{kmer.table} #' #' @usage #' kmer.table(.data, .heads = c(10, 100, 300, 1000, 3000, 10000, 30000), .k = 5, .nrow = 20, #' .clean = T, .meat = F) #' #' get.kmer.column(.kmer.table.list, .head) #' #' @param .data tcR data.frame or a list with tcR data.frames. #' @param .heads Vector of parameter for the \code{head()} function, supplied sequentialy to the \code{get.kmers()} function. -1 means all rows. #' @param .k Size of the kmer. #' @param .nrow How many most frequent k-mers include to the output table. #' @param .clean Parameter for the \code{get.kmers()} function. #' @param .meat Parameter for the \code{get.kmers()} function. #' @param .kmer.table.list Result from the \code{kmer.table} function if \code{.data} supplied as a list. #' @param .head Which columns with this head return. #' #' @return #' \code{kmer.table} - if \code{.data} is a data frame, than data frame with columns like "Kmers.X", "Count.X" where X - element from \code{.heads}. #' If \code{.data} is a list, than list of such data frames. #' #' \code{get.kmer.column} - data frame with first column with kmers and other columns named as a names of data frames, from which \code{.kmer.table.list} #' was generated. #' #' @examples #' \dontrun{ #' twb.kmers <- kmer.table(twb, .heads = c(5000, 10000), .meat = T) #' head(get.kmer.column(twb.kmers, 10000)) #' } kmer.table <- function (.data, .heads = c(10, 100, 300, 1000, 3000, 10000, 30000), .k = 5, .nrow = 20, .clean=T, .meat = F) { if (class(.data) == 'list') { return(lapply(.data, kmer.table, .k = .k, .heads = .heads, .nrow = .nrow, .clean = .clean, .meat = .meat)) } res <- do.call(cbind, lapply(.heads, function (h) { head(get.kmers(.data=.data, .k = .k, .head=h, .clean=.clean, .meat = .meat), .nrow) })) names(res) <- sapply(1:length(names(res)), function (i) { if (.heads[(1+i)%/%2] == -1) { paste0(names(res)[i], '.', 'all', collapse = '') } else { paste0(names(res)[i], '.', .heads[(1+i)%/%2], collapse = '') } }) res } get.kmer.column <- function (.kmer.table.list, .head) { name.col <- strsplit(colnames(.kmer.table.list[[1]])[2], '.', fixed=T, useBytes=T)[[1]][1] if (.head == -1) { table.names <- paste0(c('Kmers', name.col), '.', 'all') } else { table.names <- paste0(c('Kmers', name.col), '.', .head) } kmers <- unique(unlist(lapply(.kmer.table.list, function (x) {x[table.names[1]]}))) res <- sapply(.kmer.table.list, function (tab) { indices <- match(tab[[table.names[1]]], kmers) tmp <- rep.int(0, times=length(kmers)) tmp[indices] <- tab[[table.names[2]]] tmp }) df <- data.frame(kmers, res, stringsAsFactors=F) names(df) <- c(table.names[1], names(.kmer.table.list)) df <- df[order(df[, table.names[1]]),] row.names(df) <- NULL df } #' Generate k-mers. #' #' @aliases generate.kmers generate.kmers.prob #' #' @description #' Generate all k-mers. starting with the given sequence on the given alphabet #' Generate k-mers with the given k and probabilities of nucleotides next to each other (markov chain). #' #' @usage #' generate.kmers(.k, .seq = '', .alphabet = c('A', 'C', 'G', 'T')) #' #' generate.kmers.prob(.k, .probs, .count = 1, .alphabet = c('A', 'C', 'G', 'T'), #' .last.nucl = 'X') #' #' @param .k Size of k-mers or either integer or vector with k-s for generate.kmers.prob. #' @param .seq Prefix of all generated k-mers. #' @param .probs Matrix with probabilities for generating adjacent symbol with |alphabet| X |alphabet| size. Order of letters is #' given in the \code{.alphabet} parameter. #' @param .count Number of kmers to be generated. #' @param .alphabet Alphabet. #' @param .last.nucl Adjacent nucleotide from which start generation. If 'X' than choose one of the nucleotides with equal probabilities. #' #' @return Vector of all possible k-mers for \code{generate.kmers} #' or a vector of generated kmers for \code{generate.kmers.prob}. generate.kmers <- function (.k, .seq = '', .alphabet = c('A', 'C', 'G', 'T')) { kmers.list <- .seq for (k in 1:.k) { kmers.list <- unlist(lapply(kmers.list, function (kmer) { paste0(kmer, .alphabet) })) } kmers.list } generate.kmers.prob <- function (.k, .probs, .count = 1, .alphabet = c('A', 'C', 'G', 'T'), .last.nucl = 'X') { if (length(.k) == 1) { .k <- rep.int(.k, .count) } if (length(.last.nucl) == 1) { .last.nucl <- rep(.last.nucl, times=.count) } nucls <- rep('', times = length(.k)) k.more0 <- .k > 0 k.more1 <- .k > 1 nucls[k.more0] <- sapply(.last.nucl[k.more0], function (ln) { if (ln == 'X' || ln == '') { sample(x=.alphabet, size=1) } else { sample(x=.alphabet, size=1, prob=.probs[,match(ln, .alphabet)+1], replace = T) } }) nucls[k.more1] <- sapply(which(k.more1), function (i) { nucl <- rep(nucls[i], times = .k[i]) for (j in 2:.k[i]) { nucl[j] <- sample(x=.alphabet, size=1, prob=.probs[,match(nucl[j-1], .alphabet)+1]) } paste0(nucl, collapse='') } ) unlist(nucls) } #' Profile of sequences of equal length. #' #' @aliases kmers.profile #' #' @description #' Return profile for the given character vector or a data frame with sequences #' of equal length or list with them. #' #' @usage #' kmer.profile(.data, .names = rep('Noname', times=length(.data)), .verbose = F) #' #' @param .data Either list with elements of one of the allowed classes or object with one of the class: #' data.frame with first column with character sequences and second column with number of sequences or character vector. #' @param .names Names for each sequence / row in the \code{.data}. #' @param .verbose if T then print processing output. #' #' @return Return data frame with first column "Symbol" with all possible symbols in the given sequences #' and other columns with names "1", "2", ... for each position with percentage for each symbol. #' #' @seealso \link{vis.logo} kmer.profile <- function (.data, .names = rep('Noname', times=length(.data)), .verbose = F) { .get.nth.letter.stats <- function (.data, .n) { res <- summarise(grouped_df(data.frame(Letter = substr(.data[, 1], .n, .n), Count = .data[,2]), "Letter"), Sum.count = sum(Count)) res$Sum.count <- res$Sum.count / sum(res$Sum.count) names(res) <- c("Sequence", as.character(.n)) res } if (class(.data) == 'list') { res <- kmer.profile(.data[[1]], .verbose = .verbose) names(res) <- c('Var1', paste0(.names[[1]], c(1,2,3,4,5))) for (i in 2:length(.data)) { old.names <- names(res) res <- merge(res, kmer.profile(.data[[i]]), by = 'Var1', all = T) names(res) <- c(old.names, paste0(.names[[i]], c(1,2,3,4,5))) } return(res) } if (class(.data) == 'character') { .data <- data.frame(Sequence = .data, Read.count = 1, stringsAsFactors=F) } tmp <- .get.nth.letter.stats(.data, 1) aa.profiles <- tmp # aa.profiles <- as.data.frame(prop.table(table(substr(kmers[,1], i, i))), stringsAsFactors=F) for (i in 2:nchar(.data[1,1])) { if (.verbose) { cat(i, '/', nchar(.data[1,1]), '\n') } tmp <- .get.nth.letter.stats(.data, i) aa.profiles <- merge(aa.profiles, tmp, all=T, by = 'Sequence') # aa.profiles <- merge(aa.profiles, as.data.frame(prop.table(table(substr(kmers[,1], i, i))), stringsAsFactors=F), all=T, by = 'Var1') names(aa.profiles) <- c('Sequence', 1:i) } aa.profiles <- as.data.frame(aa.profiles) names(aa.profiles)[1] <- 'Symbol' aa.profiles[is.na(aa.profiles)] <- 0 aa.profiles <- aa.profiles[order(aa.profiles[,1]),] row.names(aa.profiles) <- aa.profiles[,1] aa.profiles } #' Gibbs Sampler. #' #' @aliases gibbs.sampler #' #' @description #' Perform the Gibbs Sampler method for finding frequent motifs in the given vector of strings or data.frame. #' Each string splitted to kmers with the given length of motif. #' #' @param .data Vector of characters or data.frame of characters (1st col) and their numbers (2nd col) if .meat == T. #' @param .k Motif's length. #' @param .niter Number of iterations. #' #' @return Vector of possible motifs. gibbs.sampler <- function (.data, .k = 5, .niter = 500) { .n <- .k .get.best.motif <- function (.seq, .profile) { nc <- nchar(.seq) res <- rep(x='aaaaa', times=nc - .n + 1) for (i in 1:(nc - .n + 1)) { res[i] <- substr(.seq, i, i + .n - 1) } max.p <- 0 max.kmer <- res[1] for (km in res) { p <- 1 for (i in 1:.n) { tmp <- .profile[match(substr(km, i, i), .profile[,1]),i+1] if (!is.na(tmp)) { p <- p * tmp } else { p <- 0 break } } if (p > max.p) { max.p <- p max.kmer <- km } } max.kmer } .score <- function (.kmers) { sc <- rep.int(0, .n) for (i in 1:.n) { tab <- table(substr(.kmers, i, i)) sc[i] <- sum(tab) - max(tab) } sum(sc) } .data <- .data[nchar(.data) >= .n] tmp <- trunc(runif(length(.data), 1, nchar(.data) - .n + 1 + 0.999 )) kmers <- substr(.data, tmp, tmp + .n - 1) best.kmers <- kmers cat('Performing gibbs sampling...\n') pb <- set.pb(.niter) for (i in 1:.niter) { unused.motif.index <- sample(1:length(kmers), 1) prof <- kmer.profile(kmers[-unused.motif.index]) kmers[unused.motif.index] <- .get.best.motif(.data[unused.motif.index], prof) if (.score(kmers) < .score(best.kmers)) { best.kmers <- kmers } if (i %% 500 == 0) { save(best.kmers, file = paste0('gibbs.', i, date(), '.rda')) } add.pb(pb) } close(pb) unique(best.kmers) }tcR/R/datatools.R0000644000176200001440000004432413667026401013334 0ustar liggesusers########## Support functions for managing the data ########## #' Fix alleles / genes by removing allele information / unnecessary colons. #' #' @aliases fix.alleles fix.genes #' #' @description #' Fix alleles / genes by removing allele information / unnecessary colons. #' #' @param .data tcR data frame. fix.alleles <- function (.data) { if (has.class(.data, "list")) { return(lapply(.data, fix.alleles)) } .data$V.gene <- gsub("[*][[:digit:]]*", "", .data$V.gene) .data$D.gene <- gsub("[*][[:digit:]]*", "", .data$D.gene) .data$J.gene <- gsub("[*][[:digit:]]*", "", .data$J.gene) .data } fix.genes <- function (.data) { if (has.class(.data, "list")) { return(lapply(.data, fix.genes)) } .fix <- function (.col) { # it's not a mistake .col <- gsub(", ", ",", .col, fixed = T, useBytes = T) .col <- gsub(",", ", ", .col, fixed = T, useBytes = T) .col } .data$V.gene <- .fix(.data$V.gene) .data$D.gene <- .fix(.data$D.gene) .data$J.gene <- .fix(.data$J.gene) .data } #' Print the given message if second parameter is a TRUE. #' #' @param .message Character vector standing for a message. #' @param .verbose If T then print the given mesasge. #' @return Nothing. .verbose.msg <- function (.message, .verbose = T) { if (.verbose) cat(.message) } #' Choose the right column. #' #' @param x Character vector with column IDs. #' @param .verbose If T then print the error mesasge. #' @return Character. .column.choice <- function (x, .verbose = T) { x <- switch(x[1], read.count = "Read.count", umi.count = "Umi.count", read.prop = "Read.proportion", umi.prop = "Umi.proportion", { .verbose.msg("You have specified an invalid column identifier. Choosed column: Read.count\n", .verbose); "Read.count" }) x } #' Fix names in lists. #' #' @param .datalist List with data frames. #' @return List with fixed names. .fix.listnames <- function (.datalist) { if (is.null(names(.datalist))) { names(.datalist) <- paste0("Sample.", 1:length(.datalist)) } else { for (i in 1:length(.datalist)) { if (names(.datalist)[i] == "" || is.na(names(.datalist)[i])) { names(.datalist)[i] <- paste0("Sample.", i) } } } .datalist } #' Get all unique clonotypes. #' #' @description #' Get all unique clonotypes with merged counts. Unique clonotypes are those with #' either equal CDR3 sequence or with equal CDR3 sequence and equal gene segments. #' Counts of equal clonotypes will be summed up. #' #' @param .data Either tcR data frame or a list with data frames. #' @param .gene.col Either name of the column with gene segments used to compare clonotypes #' or NA if you don't need comparing using gene segments. #' @param .count.col Name of the column with counts for each clonotype. #' @param .prop.col Name of the column with proportions for each clonotype. #' @param .seq.col Name of the column with clonotypes' CDR3 sequences. #' #' @return Data frame or a list with data frames with updated counts and proportion columns #' and rows with unique clonotypes only. #' #' @examples #' \dontrun{ #' tmp <- data.frame(A = c('a','a','b','c', 'a') #' B = c('V1', 'V1','V1','V2', 'V3') #' C = c(10,20,30,40,50), stringsAsFactors = F) #' tmp #' # A B C #' # 1 a V1 10 #' # 2 a V1 20 #' # 3 b V1 30 #' # 4 c V2 40 #' # 5 a V3 50 #' group.clonotypes(tmp, 'B', 'C', 'A') #' # A B C #' # 1 a V1 30 #' # 3 b V1 50 #' # 4 c V2 30 #' # 5 a V3 40 #' group.clonotypes(tmp, NA, 'C', 'A') #' # A B C #' # 1 a V1 80 #' # 3 b V1 30 #' # 4 c V2 40 #' # For tcR data frame: #' data(twb) #' twb1.gr <- group.clonotypes(twb[[1]]) #' twb.gr <- group.clonotypes(twb) #' } group.clonotypes<-function (.data, .gene.col = "V.gene", .count.col = "Read.count", .prop.col = "Read.proportion", .seq.col = "CDR3.amino.acid.sequence") { if (has.class(.data, "list")) { return(lapply(.data, group.clonotypes, .gene.col = .gene.col, .count.col = .count.col, .seq.col = .seq.col)) } namesvec <- c(.seq.col) if (!is.na(.gene.col)) { namesvec <- c(namesvec, .gene.col) } val <- dplyr::summarise_(dplyr::grouped_df(.data, lapply(namesvec, as.name)), value = paste0("sum(", .count.col, ")", sep = "", collapse = ""))$value .data <- .data[!duplicated(.data[, namesvec]), ] .data <- .data[order(.data$CDR3.amino.acid.sequence),] .data[, .count.col] <- val .data[, .prop.col] <- val / sum(val) permutedf(.data) } #' Shuffling data frames. #' #' @aliases permutedf unpermutedf #' #' @description #' Shuffle the given data.frame and order it by the Read.count column or un-shuffle #' a data frame and return it to the initial order. #' #' @usage #' permutedf(.data) #' #' unpermutedf(.data) #' #' @param .data MiTCR data.frame or list of such data frames. #' #' @return Shuffled data.frame or un-shuffled data frame if \code{.data} is a data frame, else list of such data frames. permutedf <- function (.data) { if (has.class(.data, 'list')) { return(lapply(.data, permutedf)) } shuffle<-.data[sample(nrow(.data)),] shuffle[order(shuffle$Read.count, decreasing=T),] } unpermutedf <- function (.data) { if (has.class(.data, 'list')) { return(lapply(.data, unpermutedf)) } .data[do.call(order, .data),] } #' Get a random subset from a data.frame. #' #' @description #' Sample rows of the given data frame with replacement. #' #' @param .data Data.frame or a list with data.frames #' @param .n Sample size if integer. If in bounds [0;1] than percent of rows to extract. "1" is a percent, not one row! #' @param .replace if T then choose with replacement, else without. #' #' @return Data.frame of nrow .n or a list with such data.frames. sample.clones <- function (.data, .n, .replace = T) { if (has.class(.data, 'list')) { return(lapply(.data, sample.clones, .n = .n)) } .data[sample(1:nrow(.data), if (.n > 1) .n else round(nrow(.data) * .n), replace = .replace), ] } #' Check if a given object has a given class. #' #' @param .data Object. #' @param .class String naming a class. #' #' @return Logical. has.class <- function (.data, .class) { .class %in% class(.data) } #' Copy the up-triangle matrix values to low-triangle. #' #' @param mat Given up-triangle matrix. #' #' @return Full matrix. matrixdiagcopy<-function(mat){ for (i in 1:ncol(mat)) for (j in 1:nrow(mat)) if (i)) #' permutDistTest(mat.ov.pca.dist, list()) #' } pca2euclid <- function (.pcaobj, .num.comps = 2) { mat <- .pcaobj$x if (.num.comps > 0) { mat <- mat[,1:.num.comps] } as.matrix(dist(mat)) }tcR/R/strtools.R0000644000176200001440000002604713667026401013235 0ustar liggesusers########## Support functions for managing sequences ########## # Private function. Split the given string by ', ' to a list and return # a first element of the list. .split.get <- function (.str, .alphabet) { if (has.class(.str, 'list')) { return(lapply(.str, .split.get, .alphabet = .alphabet)) } if (has.class(.str, 'data.frame')) { res <- .str res$V.gene <- sapply(res$V.gene, .split.get, .alphabet = HUMAN_TRBV_MITCR) res$D.gene <- sapply(res$D.gene, .split.get, .alphabet = c('TRBD1', 'TRBD2')) res$J.gene <- sapply(res$J.gene, .split.get, .alphabet = HUMAN_TRBJ) return(res) } .alphabet2 <- sub(' ', '', .alphabet, fixed = T) if (.str %in% .alphabet || .str %in% .alphabet2) { .str } else { #strsplit(.str, ', ', fixed=T, useBytes=T)[[.get]][1] strsplit(.str, split="[,][ ]*")[[1]][1] } } .fix.segments <- function (.data) { if (has.class(.data, 'list')) { return(lapply(.data, .fix.segments)) } .data$V.gene <- sapply(strsplit(.data$V.gene, ',', fixed=T, useBytes=T), paste0, collapse = ', ') .data$D.gene <- sapply(strsplit(.data$D.gene, ',', fixed=T, useBytes=T), paste0, collapse = ', ') .data$J.gene <- sapply(strsplit(.data$J.gene, ',', fixed=T, useBytes=T), paste0, collapse = ', ') .data } #' Reverse given character vector by the given n-plets. #' #' @param .seq Sequences. #' @param .n By which n-plets we should reverse the given strings. #' #' @return Reversed strings. #' #' @examples #' reverse.string('abcde') # => "edcba" #' reverse.string('abcde', 2) # => "debca" reverse.string <- function (.seq, .n = 1) { lens <- nchar(.seq) sapply(1:length(.seq), function (i) { tmp <- substring(.seq[i], 1, ) paste0(c(substring(.seq[i], seq(lens[i] - .n + 1, 1, -.n), seq(lens[i], .n, -.n)), substr(.seq[i], 1, lens[i] %% .n)), collapse = '') }, USE.NAMES=F) } #' Get all substrings for the given sequence. #' #' @param .seq Sequence for splitting to substrings. #' @param .min.len Minimal length of output sequences. #' @param .table if T then return data frame with substrings and positions of their ends in the .seq. #' #' @return Character vector or data frame with columns "Substring", "Start" and "End". get.all.substrings <- function (.seq, .min.len = 3, .table = T) { nc <- nchar(.seq) if (.table) { seqs <- unlist(sapply(1:(nc - .min.len + 1), function (i) { substring(.seq, i, (i + .min.len - 1):nc) })) inds <- do.call(rbind, sapply(1:(nc - .min.len + 1), function (i) { cbind(i, (i + .min.len - 1):nc) })) data.frame(Substring = seqs, Start = inds[,1], End = inds[,2], stringsAsFactors=F) } else { unique(unlist(sapply(1:(nc - .min.len + 1), function (i) { substring(.seq, i, (i + .min.len - 1):nc) }))) } } #' DNA reverse complementing and translation. #' #' @aliases revcomp bunch.translate #' #' @usage #' revcomp(.seq) #' #' bunch.translate(.seq, .two.way = T) #' #' @description #' Functions for DNA reverse complementing and translation. #' #' @usage #' revcomp(.seq) #' #' bunch.translate(.seq, .two.way = T) #' #' @param .seq Vector of nucleotide sequences. #' @param .two.way if T then translate sequences from both ends (output differes for #' out-of-frame sequences). #' #' @return Vector of corresponding revese complemented or aminoacid sequences. revcomp <- function (.seq) { rc.table <- c(A = 'T', T = 'A', C = 'G', G = 'C') sapply(strsplit(toupper(.seq), '', T, F, T), function (l) paste0(rc.table[l][length(l):1], collapse = ''), USE.NAMES = F) } bunch.translate <- function(.seq, .two.way = T) { sapply(toupper(.seq), function (y) { ny <- nchar(y) ny3 <- ny %/% 3 tmp <- '' if (.two.way) { if (ny %% 3 != 0) { tmp <- paste0(rep('N', times = 3), collapse = '') } y <- paste0(substr(y, 1, 3*((ny3 %/% 2) + (ny %% 2))), tmp, substr(y, 3*((ny3 %/% 2) + (ny3 %% 2)) + (ny %% 3) + 1, ny), collapse = '') } else { y <- substring(y, seq(1, nchar(y) - 2, 3), seq(3, nchar(y), 3)) } paste0(AA_TABLE[unlist(strsplit(gsub("(...)", "\\1_", y), "_"))],collapse="") }, USE.NAMES = F) } #' Functions for working with aminoacid sequences. #' #' @aliases codon.variants translated.nucl.sequences reverse.translation #' #' @usage #' codon.variants(.aaseq, .nucseq = sapply(1:length(.aaseq), #' function (i) paste0(rep('XXX', times = nchar(.aaseq[i])), #' collapse = ''))) #' #' translated.nucl.sequences(.aaseq, .nucseq = sapply(1:length(.aaseq), #' function (i) paste0(rep('XXX', times = nchar(.aaseq[i])), #' collapse = ''))) #' #' reverse.translation(.aaseq, .nucseq = paste0(rep('XXX', times = nchar(.aaseq)), #' collapse = '')) #' #' @description #' \code{codon.variants} - get all codon variants for the given nucleotide sequence with known corresponding aminoacid sequence. #' #' \code{translated.nucl.variants} - get number of nucleotide sequences which can be translated to the given aminoacid sequence. #' #' \code{reverse.translation} - get all nucleotide sequences, which can be traslated to the given aminoacid sequence. #' #' @param .aaseq Amino acid sequence. #' @param .nucseq Nucleotide sequence with 'X' letter at non-fixed positions. Other positions will be fixed. #' #' @return List with all possible variants for every aminoacid in .aaseq, number of sequences or #' character vector of candidate sequences. #' #' @examples #' codon.variants('ACT') #' translated.nucl.sequences(c('ACT', 'CASSLQ')) #' reverse.translation('T') # -> "ACA" "ACC" "ACG" "ACT" #' reverse.translation('T', 'XXT') # -> "ACT" #' translated.nucl.sequences('ACT', 'XXXXXXXC') #' codon.variants('ACT', 'XXXXXXXC') #' reverse.translation('ACT', 'XXXXXXXC') codon.variants <- function (.aaseq, .nucseq = sapply(1:length(.aaseq), function (i) paste0(rep('XXX', times = nchar(.aaseq[i])), collapse = ''))) { aas <- strsplit(.aaseq, '') nuc.nchar <- nchar(.nucseq) lapply(1:length(aas), function (i) { if (nuc.nchar[i] > 2) { triplets <- substring(.nucseq[i], seq(1, nuc.nchar[i] - 2, 3), seq(3, nuc.nchar[i], 3)) lapply(1:min(length(aas[[i]]), nuc.nchar[i] %/% 3), function (j) grep(gsub('X', '[ACGT]', triplets[j]), AA_TABLE_REVERSED[[aas[[i]][j]]], value = T)) } else { list() } }) } translated.nucl.sequences <- function (.aaseq, .nucseq = sapply(1:length(.aaseq), function (i) paste0(rep('XXX', times = nchar(.aaseq[i])), collapse = ''))) { aas <- strsplit(.aaseq, '') nuc.nchar <- nchar(.nucseq) sapply(1:length(aas), function (i) { if (nuc.nchar[i] > 2) { triplets <- substring(.nucseq[i], seq(1, nuc.nchar[i] - 2, 3), seq(3, nuc.nchar[i], 3)) prod(sapply(1:min(length(aas[[i]]), nuc.nchar[i] %/% 3), function (j) length(grep(gsub('X', '[ACGT]', triplets[j]), AA_TABLE_REVERSED[[aas[[i]][j]]], value = T)))) } else { 0 } }) } reverse.translation <- function (.aaseq, .nucseq = paste0(rep('XXX', times = nchar(.aaseq)), collapse = '')) { apply(expand.grid(codon.variants(.aaseq, .nucseq)[[1]], stringsAsFactors=F), 1, paste0, collapse = '') } #' GC-content of a nucleotide sequences. #' #' @description #' Compute the GC-content (proportion of G-C nucleotide in a sequence). #' #' @param .nucseq Character vector of nucletoide sequences. #' @return Numeric vector of \code{length(.nucseq)}. gc.content <- function (.nucseq) { sapply(strsplit(.nucseq, '', fixed=T, useBytes=T), function (l) { l <- unlist(l) t <- table(c('A', 'C', 'G', 'T', l)) r <- (t['G'] + t['C'] - 2) / length(l) names(r) <- NULL r }, USE.NAMES=F) } #' Find similar sequences. #' #' @aliases find.similar.sequences exact.match hamming.match levenshtein.match #' #' @description #' Return matrix M with two columns. For each element in row i and column j M[i,j] => distance between pattern(i) and data(j) sequences equal to or less than .max.errors. #' This function will uppercase .data and remove all strings, which have anything than A-Z letters. #' #' @usage #' find.similar.sequences(.data, .patterns = c(), .method = c('exact', 'hamm', 'lev'), #' .max.errors = 1, .verbose = T, .clear = F) #' #' exact.match(.data, .patterns = c(), .verbose = T) #' #' hamming.match(.data, .patterns = c(), .max.errors = 1, .verbose = T) #' #' levenshtein.match(.data, .patterns = c(), .max.errors = 1, .verbose = T) #' #' @param .data Vector of strings. #' @param .patterns Character vector of sequences, which will be used for searching for neighbours. #' @param .method Which method use: 'exact' for exact matching, 'hamm' for Hamming Distance, 'lev' for Levenshtein distance. #' @param .max.errors Max Hamming or Levenshtein distance between strings. Doesn't use in 'exact' setting. #' @param .verbose Should function print progress or not. // DON'T USE IT #' @param .clear if T then remove all sequences with character "*" or "~". #' #' @return Matrix with two columns [i,j], dist(data(i), data(j)) <= .max.errors. find.similar.sequences <- function (.data, .patterns = c(), .method = c('exact', 'hamm', 'lev'), .max.errors = 1, .verbose = T, .clear = F) { if (.method[1] == 'lev') { .clear <- T } data.old.indices <- grep('[*, ~]', .data, invert = T) .data <- toupper(.data[data.old.indices]) pattern.old.indices <- data.old.indices if (length(.patterns) == 0) { .patterns <- .data } else { pattern.old.indices <- 1:length(.patterns) if (.clear) { pattern.old.indices <- grep('[*, ~]', .patterns, invert = T) } .patterns <- toupper(.patterns[pattern.old.indices]) } .fun <- switch(.method[1], exact = .exact_search, hamm = .hamming_search, lev = .levenshtein_search) res <- .fun(.data, .patterns, .max.errors, .verbose) if (length(res) == 0) { matrix(ncol = 2, nrow = 1) } else { res <- cbind(res[seq(from=1, to=length(res), by=2)], res[seq(from=2, to=length(res), by=2)]) res <- res[order(res[,1]), ] if (is.null(dim(res))) { res <- t(as.matrix(res)) } res[,1] <- data.old.indices[res[,1]] res[,2] <- pattern.old.indices[res[,2]] res } } .exact_search2 <- function (.data, .patterns, .max.errors, .verbose) { ps <- data.frame(P = .patterns, Ind = 1:length(.patterns), stringsAsFactors = F) agg <- aggregate(formula = Ind ~ P, data = ps, FUN = function (x) x, simplify = F) inds <- lapply(match(.data, agg$P), function (i) if (is.na(i)) NA else unlist(agg$Ind[i], use.names = F) ) t(do.call(rbind, sapply(1:length(inds), function (i) if (!is.na(inds[[i]][1])) cbind(i, inds[[i]]), USE.NAMES = F))) } exact.match <- function (.data, .patterns = c(), .verbose = T) { find.similar.sequences(.data, .patterns, 'exact', .verbose = .verbose, F) } hamming.match <- function (.data, .patterns = c(), .max.errors = 1, .verbose = T) { find.similar.sequences(.data, .patterns, 'hamm', .max.errors, .verbose, F) } levenshtein.match <- function (.data, .patterns = c(), .max.errors = 1, .verbose = T) { find.similar.sequences(.data, .patterns, 'lev', .max.errors, .verbose, T) }tcR/R/crosses.R0000644000176200001440000005330613667026401013023 0ustar liggesusers########## Intersections among sets of sequences ########## if (getRversion() >= "2.15.1") { utils::globalVariables(c("Resamp.values", "Fun.value", "P.value")) } #' Intersection between sets of sequences or any elements. #' #' @aliases intersectClonesets intersectCount intersectLogic intersectIndices #' #' @description #' Functions for the intersection of data frames with TCR / Ig data. #' See the \code{repOverlap} function for a general interface to all overlap analysis functions. #' #' \code{intersectClonesets} - returns number of similar elements in the given two clonesets / data frames or matrix #' with counts of similar elements among each pair of objects in the given list. #' #' \code{intersectCount} - similar to \code{tcR::intersectClonesets}, but with fewer parameters and only for two objects. #' #' \code{intersectIndices} - returns matrix M with two columns, where element with index M[i, 1] in the first #' given object is similar to an element with index M[i, 2] in the second given object. #' #' \code{intersectLogic} - returns logic vector with TRUE values in positions, where element in the first given data frame #' is found in the second given data frame. #' #' @usage #' intersectClonesets(.alpha = NULL, .beta = NULL, .type = "n0e", .head = -1, .norm = F, #' .verbose = F) #' #' intersectCount(.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) #' #' intersectIndices(.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) #' #' intersectLogic(.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) #' #' @param .alpha Either first vector or data.frame or list with data.frames. #' @param .beta Second vector or data.frame or type of intersection procedure (see the \code{.type} parameter) if \code{.alpha} is a list. #' @param .type Types of intersection procedure if \code{.alpha} and \code{.beta} is data frames. String with 3 characters (see 'Details' for more information). #' @param .head Parameter for the \code{head} function, applied before intersecting. #' @param .method Method to use for intersecting string elements: 'exact' for exact matching, 'hamm' for matching strings which have <= 1 hamming distance, #' 'lev' for matching strings which have <= 1 levenshtein (edit) distance between them. #' @param .col Which columns use for fetching values to intersect. First supplied column matched with \code{.method}, others as exact values. #' @param .norm If TRUE than normalise result by product of length or nrows of the given data. #' @param .verbose if T then produce output of processing the data. #' #' @details #' Parameter \code{.type} of the \code{intersectClonesets} function is a string of length 3 #' [0an][0vja][ehl], where: #' \enumerate{ #' \item First character defines which elements intersect ("a" for elements from the column "CDR3.amino.acid.sequence", #' "n" for elements from the column "CDR3.nucleotide.sequence", other characters - intersect elements as specified); #' \item Second character defines which columns additionaly script should use #' ('0' for cross with no additional columns, 'v' for cross using the "V.gene" column, #' 'j' for cross using "J.gene" column, 'a' for cross using both "V.gene" and "J.gene" columns); #' \item Third character defines a method of search for similar sequences is use: #' "e" stands for the exact match of sequnces, "h" for match elements which have the Hamming distance between them #' equal to or less than 1, "l" for match elements which have the Levenshtein distance between tham equal to or less than 1. #' } #' #' @seealso \link{repOverlap}, \link{vis.heatmap}, \link{ozScore}, \link{permutDistTest}, \link{vis.group.boxplot} #' #' @return #' \code{intersectClonesets} returns (normalised) number of similar elements or matrix with numbers of elements. #' #' \code{intersectCount} returns number of similar elements. #' #' \code{intersectIndices} returns 2-row matrix with the first column stands for an index of an element in the given \code{x}, and the second column stands for an index of an element of \code{y} which is similar to a relative element in \code{x}; #' #' \code{intersectLogic} returns logical vector of \code{length(x)} or \code{nrow(x)}, where TRUE at position \code{i} means that element with index {i} has been found in the \code{y} #' #' @examples #' \dontrun{ #' data(twb) #' # Equivalent to intersectClonesets(twb[[1]]$CDR3.nucleotide.sequence, #' # twb[[2]]$CDR3.nucleotide.sequence) #' # or intersectCount(twb[[1]]$CDR3.nucleotide.sequence, #' # twb[[2]]$CDR3.nucleotide.sequence) #' # First "n" stands for a "CDR3.nucleotide.sequence" column, "e" for exact match. #' twb.12.n0e <- intersectClonesets(twb[[1]], twb[[2]], 'n0e') #' stopifnot(twb.12.n0e == 46) #' # First "a" stands for "CDR3.amino.acid.sequence" column. #' # Second "v" means that intersect should also use the "V.gene" column. #' intersectClonesets(twb[[1]], twb[[2]], 'ave') #' # Works also on lists, performs all possible pairwise intersections. #' intersectClonesets(twb, 'ave') #' # Plot results. #' vis.heatmap(intersectClonesets(twb, 'ave'), .title = 'twb - (ave)-intersection', .labs = '') #' # Get elements which are in both twb[[1]] and twb[[2]]. #' # Elements are tuples of CDR3 nucleotide sequence and corresponding V-segment #' imm.1.2 <- intersectLogic(twb[[1]], twb[[2]], #' .col = c('CDR3.amino.acid.sequence', 'V.gene')) #' head(twb[[1]][imm.1.2, c('CDR3.amino.acid.sequence', 'V.gene')]) #' data(twb) #' ov <- repOverlap(twb) #' sb <- matrixSubgroups(ov, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))); #' vis.group.boxplot(sb) #' } intersectClonesets <- function (.alpha = NULL, .beta = NULL, .type = 'n0e', .head = -1, .norm = F, .verbose = F) { if (class(.alpha) == 'list') { if (class(.beta) == 'character') { .type <- .beta } apply.symm(.alpha, intersectClonesets, .head = .head, .type = .type, .norm = .norm, .verbose = .verbose) } else { if (.head != -1) { .alpha <- head(.alpha, .head) .beta <- head(.beta, .head) } cols <- NULL if (class(.alpha) == 'data.frame') { if (substr(.type, 1, 1) == 'a' || substr(.type, 1, 1) == 'n') { if (substr(.type, 1, 1) == 'a') { cols <- 'CDR3.amino.acid.sequence' } else { cols <- 'CDR3.nucleotide.sequence' } if (substr(.type, 2, 2) == 'v') { cols <- c(cols, 'V.gene') } else if (substr(.type, 2, 2) == 'j') { cols <- c(cols, 'J.gene') } else if (substr(.type, 2, 2) == 'a') { cols <- c(cols, 'V.gene', 'J.gene') } else if (substr(.type, 2, 2) != '0') { cat("Second character in .type:", .type, 'is unknown!\n') } } } method <- switch(substr(.type, 3, 3), e = 'exact', h = 'hamm', l = 'lev') res <- intersectCount(.alpha, .beta, method, cols) if (.norm) { if (is.null(dim(.alpha))) { res <- res / (as.numeric(length(.alpha)) * length(.beta)) # res <- res * max(length(.alpha), length(.beta)) } else { res <- res / (as.numeric(nrow(.alpha)) * nrow(.beta)) # res <- res * max(nrow(.alpha), nrow(.beta)) } } res } } intersectCount <- function (.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) { if (.method[1] == 'exact' && (is.null(.col) || length(.col) == 1)) { if (is.null(.col)) { length(base::intersect(.alpha, .beta)) } else { length(base::intersect(.alpha[,.col], .beta[,.col])) } } else { if (.method[1] == 'exact') { nrow(dplyr::intersect(.alpha[, .col], .beta[, .col])) } else { res <- intersectIndices(unique(.alpha), unique(.beta), .method, .col) if (!is.na(res[1,1])) { nrow(res) } else { 0 } } } } intersectIndices <- function (.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) { if (!is.null(.col)) { .alpha <- as.data.frame(.alpha, stringsAsFactors = F) .beta <- as.data.frame(.beta, stringsAsFactors = F) if (length(.col) == 1) { .alpha <- .alpha[, .col] .beta <- .beta[, .col] } else { if (length(.col) == 2) { alpha.cols <- as.matrix(.alpha[, .col[2]]) beta.cols <- as.matrix(.beta[, .col[2]]) } else { alpha.cols <- .alpha[, .col[2:length(.col)]] beta.cols <- .beta[, .col[2:length(.col)]] } .alpha <- .alpha[, .col[1]] .beta <- .beta[, .col[1]] } } res <- find.similar.sequences(.alpha, .beta, .method, 1, F) if (is.na(res[1,1])) { return(res) } if (!is.null(.col) && length(.col) > 1) { for (i in 1:ncol(alpha.cols)) { res <- res[alpha.cols[res[,1], i] == beta.cols[res[,2], i], ] } } if(!is.matrix(res)) { res <- rbind(res) } if (dim(res)[1] == 0) { res <- matrix(c(NA, NA), 1, 2) } res } intersectLogic <- function (.alpha, .beta, .method = c('exact', 'hamm', 'lev'), .col = NULL) { ind <- intersectIndices(.alpha, .beta, .method, .col) if (is.null(.col)) { logic <- rep(FALSE, length(.alpha)) } else { logic <- rep(FALSE, nrow(.alpha)) } logic[ind[,1]] <- TRUE logic } #' Compute convergence characteristics of repertoires. #' #' @description #' Get a number of rows with similar aminoacid sequence but different nucleotide sequence. #' #' @param .alpha Either data frame with columns \code{.col.nuc} and \code{.col.aa} or list with such data frames. #' @param .beta Either data frame or none. #' @param .col.nuc Name of the column with nucleotide sequences. #' @param .col.aa Name of the columnw ith aminoacid sequences. #' @return If \code{.alpha} is data frame, than integer vector of length 2 with . If \code{.alpha} is a list #' than matrix M with M[i,j] = convergence.index(.alpha[[i]], .alpha[[j]]). convergence.index <- function (.alpha, .beta, .col.nuc = 'CDR3.nucleotide.sequence', .col.aa = 'CDR3.amino.acid.sequence') { if (has.class(.alpha, 'list')) { return(apply.asymm(.alpha, function (x,y) convergence.index(x, y)[1])) } a.nuc.logic <- .alpha[, .col.nuc] %in% .beta[, .col.nuc] b.nuc.logic <- .beta[, .col.nuc] %in% .alpha[, .col.nuc] a.aa.logic <- .alpha[, .col.aa] %in% .beta[, .col.aa] b.aa.logic <- .beta[, .col.aa] %in% .alpha[, .col.aa] c(Amino.acid.not.nucleotide.count.1 = length(unique(.alpha[a.aa.logic & !(a.nuc.logic), .col.aa])), Amino.acid.not.nucleotide.count.2 = length(unique(.beta[b.aa.logic & !(b.nuc.logic), .col.aa]))) } #' Overlap Z-score. #' #' @description #' Compute OZ-scores ("overlap Z scores") for values in the given matrix of overlaps, i.e.,. #' for each value compute the number of standart deviations from the mean of the matrix. #' #' @param .mat Matrix with overlap values. #' @param .symm If T then remove lower triangle matrix from counting. Doesn't work if the matrix #' has different number of rows and columns. #' @param .as.matrix If T then return #' @param .val.col If .as.matrix is T then this is a name of the column to build matrix upon: #' either "oz" for the OZ-score column, "abs" for the absolute OZ-score column, or "norm" for the #' normalised absolute OZ-score column. #' #' @seealso \link{repOverlap}, \link{intersectClonesets}, \link{permutDistTest} #' #' @examples #' \dontrun{ #' data(twb) #' mat <- repOverlap(twb) #' ozScore(mat) #' # Take 3x3 matrix #' ozScore(mat[1:3, 1:3]) #' # Return as matrix with OZ scores #' ozmat <- ozScore(mat, T, T, 'oz') #' # Return as matrix with normalised absolute OZ scores #' oznorm <- ozScore(mat, T, T, 'norm') #' # Plot it as boxplots #' sb <- matrixSubgroups(oznorm, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))); #' vis.group.boxplot(sb) #' } ozScore <- function (.mat, .symm = T, .as.matrix = F, .val.col = c('norm', 'abs', 'oz')) { if (.symm && nrow(.mat) == ncol(.mat)) { .mat[lower.tri(.mat)] <- NA } res <- melt(.mat, na.rm = T) res$OZ <- NA colnames(res) <- c("Rep1", "Rep2", "Overlap", "OZ") res$OZ <- (res$Overlap - mean(res$Overlap)) / sd(res$Overlap) res$Abs.OZ <- abs(res$OZ) res$Norm.abs.OZ <- (res$Abs.OZ - min(res$Abs.OZ)) / (max(res$Abs.OZ) - min(res$Abs.OZ)) res <- res[order(res$Abs.OZ, decreasing = T),] row.names(res) <- NULL if (.as.matrix) { res <- acast(res, Rep1 ~ Rep2, value.var = switch(.val.col[1], oz = 'OZ', abs = 'Abs.OZ', norm = 'Norm.abs.OZ')) if (sum(!colnames(.mat) %in% colnames(res))) { res <- cbind(NA, res) colnames(res)[1] <- colnames(.mat)[!(colnames(.mat) %in% colnames(res))] } if (sum(!row.names(.mat) %in% row.names(res))) { res <- rbind(res, NA) row.names(res)[nrow(res)] <- row.names(.mat)[!(row.names(.mat) %in% row.names(res))] } } res } #' Monte Carlo permutation test for pairwise and one-vs-all-wise within- and inter-group differences in a set of repertoires. #' #' @description #' WARNING: this is an experimental procedure, work is still in progress. #' #' Perform permutation tests of distances among groups for the given groups of samples and matrix of distances among all samples. #' #' @param .mat Symmetric matrix of repertoire distances. #' @param .groups Named list with names of repertoires in groups. #' @param .n Number of permutations for each pair of group. #' @param .fun A function to apply to distances. #' @param .signif Significance level. Below this value hypotheses counts as significant. #' @param .plot If T than plot the output results. Else return them as a data frame. #' @param .xlab X lab label. #' @param .title Main title of the plot. #' @param .hjust Value for adjusting the x coordinate of p-value labels on plots. #' @param .vjust Value for adjusting the y coordinate of p-value labels on plots. #' #' @seealso \link{repOverlap}, \link{intersectClonesets}, \link{ozScore}, \link{pca2euclid} #' #' @examples #' \dontrun{ #' data(twb) #' mat <- repOverlap(twb) #' permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))) #' permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D')), .fun = median) #' } permutDistTest <- function (.mat, .groups, .n = 1000, .fun = mean, .signif = .05, .plot = T, .xlab = "Values", .title = "Monte Carlo permutation testing of overlaps", .hjust = -.1, .vjust = -4) { cat("WARNING: this is an experimental procedure, work is still in progress \n") .pairwise.test <- function (.mat, .group.logic, .n, .fun) { within.val.gr1 = .fun(.mat[.group.logic, .group.logic][upper.tri(.mat[.group.logic, .group.logic])]) within.val.gr2 = .fun(.mat[!.group.logic, !.group.logic][upper.tri(.mat[!.group.logic, !.group.logic])]) inter.val = .fun(.mat[.group.logic, !.group.logic]) within.resamp.gr1 = c() within.resamp.gr2 = c() inter.resamp = c() gr1.size <- sum(.group.logic) gr2.size <- sum(!.group.logic) for (iter in 1:.n) { new.group.logic <- rep(F, nrow(.mat)) new.group.logic[ sample(1:nrow(.mat), gr1.size, F) ] <- T within.resamp.gr1 = c(within.resamp.gr1, .fun(.mat[new.group.logic, new.group.logic][upper.tri(.mat[new.group.logic, new.group.logic])])) within.resamp.gr2 = c(within.resamp.gr2, .fun(.mat[!new.group.logic, !new.group.logic][upper.tri(.mat[!new.group.logic, !new.group.logic])])) inter.resamp = c(inter.resamp, .fun(.mat[new.group.logic, !new.group.logic])) } list(within.val.gr1 = within.val.gr1, within.val.gr2 = within.val.gr2, inter.val = inter.val, within.resamp.gr1 = within.resamp.gr1, within.resamp.gr2 = within.resamp.gr2, inter.resamp = inter.resamp, within.p.gr1 = sum(within.val.gr1 > within.resamp.gr1) / .n, within.p.gr2 = sum(within.val.gr2 > within.resamp.gr2) / .n, inter.p = sum(inter.val > inter.resamp) / .n) } .intergroup.name <- function (.gr1, .gr2) { paste0(.gr1, ':', .gr2) } res <- list() pb <- set.pb((length(.groups) - 1) * length(.groups) / 2) k <- 1 p.vals <- list() for (gr in names(.groups)) { p.vals[[gr]] <- list(within = c(), inter = c()) } for (i in 1:(length(.groups)-1)) { gr1 <- names(.groups)[i] for (j in (i+1):length(.groups)) { gr2 <- names(.groups)[j] submat <- .mat[ row.names(.mat) %in% .groups[[i]] | row.names(.mat) %in% .groups[[j]] , colnames(.mat) %in% .groups[[i]] | colnames(.mat) %in% .groups[[j]] ] submat <- submat[, row.names(submat)] group.logic <- row.names(submat) %in% .groups[[i]] test.res.list <- .pairwise.test(submat, group.logic, .n, .fun) res[[k]] <- rbind(data.frame(Group1 = gr1, Group2 = gr1, Fun.value = test.res.list$within.val.gr1, Resamp.values = test.res.list$within.resamp.gr1, P.value = test.res.list$within.p.gr1, stringsAsFactors = F), data.frame(Group1 = gr1, Group2 = gr2, Fun.value = test.res.list$inter.val, Resamp.values = test.res.list$inter.resamp, P.value = test.res.list$inter.p, stringsAsFactors = F), data.frame(Group1 = gr2, Group2 = gr2, Fun.value = test.res.list$within.val.gr2, Resamp.values = test.res.list$within.resamp.gr2, P.value = test.res.list$within.p.gr2, stringsAsFactors = F)) k <- k + 1 p.vals[[gr1]][["within"]] <- c(p.vals[[gr1]][["within"]], test.res.list$within.p.gr1) names(p.vals[[gr1]][["within"]])[length(p.vals[[gr1]][["within"]])] <- gr2 p.vals[[gr2]][["within"]] <- c(p.vals[[gr2]][["within"]], test.res.list$within.p.gr2) names(p.vals[[gr2]][["within"]])[length(p.vals[[gr2]][["within"]])] <- gr1 p.vals[[gr1]][["inter"]] <- c(p.vals[[gr1]][["inter"]], test.res.list$inter.p) names(p.vals[[gr1]][["inter"]])[length(p.vals[[gr1]][["inter"]])] <- gr2 p.vals[[gr2]][["inter"]] <- c(p.vals[[gr2]][["inter"]], test.res.list$inter.p) names(p.vals[[gr2]][["inter"]])[length(p.vals[[gr2]][["inter"]])] <- gr1 add.pb(pb) } } close(pb) p <- list() for (i in 1:(k-1)) { faclevels <- unique(c(res[[i]]$Group1, res[[i]]$Group2)) faclevels <- c(faclevels[1], paste0(faclevels[1], " ~ ", faclevels[2]), faclevels[2]) res[[i]]$Group3 <- paste0(res[[i]]$Group1, " ~ ", res[[i]]$Group2) res[[i]]$Group3[res[[i]]$Group1 == res[[i]]$Group2] <- res[[i]]$Group1[res[[i]]$Group1 == res[[i]]$Group2] res[[i]]$Group3 <- factor(res[[i]]$Group3, faclevels) tmp <- do.call(rbind, lapply(split(res[[i]], res[[i]]$Group3), function (x) x[1,])) p[[i]] <- ggplot(res[[i]], aes(x = Resamp.values)) + geom_vline(aes(xintercept = Fun.value), size = 1, linetype = "twodash", colour = "blue") + geom_histogram(alpha = .4, colour = 'grey40') + geom_text(aes(x = 0, y = 0, label = paste0("P = ", P.value)), hjust = .hjust, vjust = .vjust, size = 5, data = tmp) + facet_wrap(~ Group3, ncol = 3) + ylab("Permutations, num") + xlab(.xlab) + theme_bw() } fmt.width <- 0 for (gr.name in names(.groups)) { fmt.width <- max(fmt.width, nchar(gr.name)) } fmt.width <- fmt.width + 2 flag <- F cat("Significant differences found (OBS - observed, SIM - simulated):\n") for (gr.name in names(p.vals)) { w.signs <- c() i.signs <- c() for (i in 1:length(p.vals[[gr.name]][["within"]])) { if (p.vals[[gr.name]][["within"]][i] < 1 - p.vals[[gr.name]][["within"]][i]) { w.signs <- c(w.signs, ">") } else { w.signs <- c(w.signs, "<") p.vals[[gr.name]][["within"]][i] <- 1 - p.vals[[gr.name]][["within"]][i] } } for (i in 1:length(p.vals[[gr.name]][["inter"]])) { if (p.vals[[gr.name]][["inter"]][i] < 1 - p.vals[[gr.name]][["inter"]][i]) { i.signs <- c(i.signs, ">") } else { i.signs <- c(i.signs, "<") p.vals[[gr.name]][["inter"]][i] <- 1 - p.vals[[gr.name]][["inter"]][i] } } p.vals[[gr.name]][["within"]] <- p.adjust(p.vals[[gr.name]][["within"]], "BH") p.vals[[gr.name]][["inter"]] <- p.adjust(p.vals[[gr.name]][["inter"]], "BH") # cat("\tThis groups may have come from different populations\n") # cat("\tThis groups may have non-random overlap and come from different populations\n") for (i in 1:length(p.vals[[gr.name]][["within"]])) { pval <- p.vals[[gr.name]][["within"]][i] if (pval <= .signif) { cat(' Within ', formatC(paste0('"', gr.name, '"'), width = -fmt.width), ' in a pool with ', formatC(paste0('"', names(p.vals[[gr.name]][["within"]])[i], '"'), width = fmt.width), ' : P(OBS ', w.signs[i], ' SIM) = ', pval, "\n", sep ='') flag <- T } } for (i in 1:length(p.vals[[gr.name]][["inter"]])) { pval <- p.vals[[gr.name]][["inter"]][i] if (pval <= .signif) { cat(' Between ', formatC(paste0('"', gr.name, '"'), width = -fmt.width), ' and ', formatC(paste0('"', names(p.vals[[gr.name]][["inter"]])[i], '"'), width = fmt.width + 11), ' : P(OBS ', i.signs[i], ' SIM) = ', pval, "\n", sep = '') flag <- T } } } if (!flag) { cat("No significant differences have been found.\n") } if (.plot) { suppressMessages(do.call(grid.arrange, c(p, ncol = 1, top = .title))) } else { res } }tcR/R/spectrum.R0000644000176200001440000000542613667026401013204 0ustar liggesusers########## Spectratyping ########## if (getRversion() >= "2.15.1") { utils::globalVariables(c("Length", "Val")) } #' Spectratype #' #' @description Plot a spectratype plot - a histogram of read counts / umi counts by CDR3 length. #' #' @param .data tcR data frame. #' @param .quant Either "read.count" or "umi.count" for choosing the corresponding columns, or "id" to compute avoid using counts. #' @param .gene Either NA for not using genes, "V" or "J" for corresponding genes. #' @param .plot If T than plot the spectratype plot, otherwise return a table with data for lengths and counts. #' @param .main Main title. #' @param .legend Legend title. #' @param .labs Character vector of length 2 for x-lab and y-lab. #' #' @examples #' \dontrun{ #' data(twb) #' tmp = twb[[1]] #' spectratype(tmp) #' spectratype(tmp, .quant = "id", .plot = T, .gene = 'V') #' spectratype(tmp, .quant = "read.count", .plot = F) #' } spectratype <- function (.data, .quant = c("read.count", "umi.count", "id"), .gene = "V", .plot = T, .main = "Spectratype", .legend = "Gene segment", .labs = c("CDR3 length", NA)) { .data$Length = nchar(.data$CDR3.nucleotide.sequence) if (.quant[1] == "id") { .data$Count = 1 } else { .data$Count = .data[[.column.choice(.quant[1])]] } if (is.na(.gene)) { df = summarise(group_by(do.call(select_, list(.data, "Length", "Count")), Length), Val = sum(Count)) p = ggplot() + geom_bar(aes(x = Length, y = Val), data = df, stat = "identity") } else { .gene = switch(tolower(substr(.gene, 1, 1)), v = "V.gene", j = "J.gene") df = do.call(select_, list(.data, "Length", "Count", .gene)) df = group_by_(df, "Length", .gene) df = summarise(df, Val = sum(Count)) df$Gene = df[[.gene]] df[[.gene]] = NULL df = df[order(df$Val, decreasing = T),] dup = which(cumsum(!duplicated(df$Gene)) == 12)[1] if (length(dup)) { uniq = unique(df$Gene[1:(dup - 1)]) df$Gene[!(df$Gene %in% uniq)] = "Z" # <- dirty hack to avoid factors } p = ggplot() + geom_bar(aes(x = Length, y = Val, fill = Gene), data = df, stat = "identity") + scale_fill_manual(name = .legend, breaks = c(sort(uniq), "Z"), labels=c(sort(uniq), "Other"), values = c("#9E0142", "#D53E4F", "#F46D43", "#FDAE61", "#FEE08B", "#FFFFBF", "#E6F598", "#ABDDA4", "#66C2A5", "#3288BD", "#5E4FA2", "grey75")) + ggtitle(.main) } if (.plot) { if (!is.na(.labs[2])) { p = p + ylab(.labs[2]) } else { p = p + ylab(switch(.quant[1], id = "Number of clonotypes", read.count = "Sum of reads", umi.count = "Sum of UMIs")) } p = p + xlab(.labs[1]) p + theme_linedraw() } else { as.data.frame(df) } }tcR/R/onload.R0000644000176200001440000000123113667027531012611 0ustar liggesusers.onAttach <- function(libname = find.package("tcR"), pkgname = "tcR"){ packageStartupMessage(" ======================================== !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !! The tcR package WILL SOON BE ORPHANED !! AND REMOVED FROM CRAN. !! !! A new package is available that is !! designed to replace tcR: !! immunarch -- https://immunarch.com/ !! !! We will be happy to help you to move !! to the new package. Feel free to contact us: !! http://github.com/immunomind/immunarch !! !! Sincerely, !! immunarch dev team and !! Vadim I. Nazarov, lead developer of tcR !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! =======================================") }tcR/R/io.R0000644000176200001440000001004213667027536011751 0ustar liggesusers#' Parse input files or folders with immune receptor repertoire data. #' #' @aliases repLoad #' #' @description #' Load the immune receptor repertoire data from the given input: either a file name, a list of file names, a name of the folder with repertoire files, #' or a list of folders with repertoire files. The folder / folders must contain only files with the specified format. #' Input files could be either text files or archived with gzip ("filename.txt.gz") or bzip2 ("filename.txt.bz2"). #' For a general parser of table files with cloneset data see \code{\link{parse.cloneset}}. #' #' Parsers are available for: #' MiTCR ("mitcr"), MiTCR w/ UMIs ("mitcrbc"), MiGEC ("migec"), VDJtools ("vdjtools"), #' ImmunoSEQ ("immunoseq" or 'immunoseq2' for old and new formats respectively), #' MiXCR ("mixcr"), IMSEQ ("imseq") and tcR ("tcr", data frames saved with the `repSave()` function). #' #' Output of MiXCR should contain either all hits or best hits for each gene segment. #' #' Output of IMSEQ should be generated with parameter "-on". In this case there will be no positions of aligned gene segments in the output data frame #' due to restrictions of IMSEQ output. #' #' tcR's data frames should be saved with the `repSave()` function. #' #' For details on the tcR data frame format see \link{parse.file}. #' #' @param .path Character vector with path to files and / or folders. #' @param .format String that specifies the input format. #' #' @seealso \link{parse.file} #' #' @examples #' \dontrun{ #' datalist <- repLoad(c("file1.txt", "folder_with_files1", "another_folder"), "mixcr") #' } repLoad <- function (.path, .format = c("mitcr", "migec")) { message(" ======================================== !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !! The tcR package WILL SOON BE ORPHANED !! AND REMOVED FROM CRAN. !! !! A new package is available that is !! designed to replace tcR: !! immunarch -- https://immunarch.com/ !! !! We will be happy to help you to move !! to the new package. Feel free to contact us: !! http://github.com/immunomind/immunarch !! !! Sincerely, !! immunarch dev team and !! Vadim I. Nazarov, lead developer of tcR !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! =======================================") res <- list() for (i in 1:length(.path)) { if (dir.exists(.path[i])) { res <- c(res, parse.folder(.path[i], .format[1])) } else if (file.exists(.path[i])) { res <- c(res, list(parse.file(.path[1], .format[1]))) } else { cat('Can\'t find folder or file:\t"', .path[i], '"', sep = '', end = '\n') } } res } #' Save tcR data frames to disk as text files or gzipped text files. #' #' @description #' Save repertoire files to either text files or gzipped text files. #' You can read them later by \code{repLoad} function with \code{.format = "tcr"}. #' #' @param .data Either tcR data frame or a list of tcR data frames. #' @param .format "txt" for simple tab-delimited text tables, "gz" for compressed (gzipped) tables. #' @param .names Names of output files. By default it's an empty string so names will be taken from names of the input list. #' @param .folder Path to the folder with output files. #' #' @seealso \link{repLoad} repSave <- function (.data, .format = c("txt", "gz"), .names = "", .folder = "./") { if (has.class(.data, 'data.frame')) { .data <- list(Sample = .data) } .folder <- paste0(.folder, "/") postfix <- ".txt" filefun <- function (...) file(...) if (.format[1] == "gz") { postfix <- ".txt.gz" filefun <- function (...) gzfile(...) } if (.names[1] == "") { .names = paste0(.folder, names(.data), postfix) } else { if (length(.data) != length(.names)) { cat("Number of input data frames isn't equal to number of names\n") return(NULL) } else { .names = paste0(.folder, .names, postfix) } } for (i in 1:length(.data)) { cat("Writing", .names[i], "file...\t") fc <- filefun(description = .names[i], open = "w") write.table(.data[[i]], fc, quote = F, row.names = F, sep = '\t') close(fc) cat("Done.\n") } }tcR/R/filters.R0000644000176200001440000000647513667026401013017 0ustar liggesusers########## Various ways to subset the data ########## #' Contamination filtering. #' #' @aliases contamination.stats decontamination #' #' @description #' Occasionally DNA or RNA libraries are contaminate each other. To address this issue and estimate contamination rate \code{tcR} offers #' \code{contamination.stats} and \code{decontamination} functions. The \code{decontamination} function received data #' (either data frame or a list with data frames) and a limit for clonal proportion as arguments. #' Script searches for a similar clones to the first data frame in the other (or performs pairwise searches if the given data is a list) #' and removes clones from the first data frame, which has been found in the second one with counts less or equal to 10 * counts of similar clones #' in the first one. Function \code{contamination.stats} will return the number of clones which will be removed with the \code{contamination.stats} function. #' #' @usage #' contamination.stats(.data1, .data2, .limit = 20, .col = 'Read.count') #' #' decontamination(.data1, .data2, .limit = 20, .col = 'Read.count', .symm = T) #' #' @param .data1 First data frame with columns 'CDR3.nucleotide.sequence' and 'Read.count'. Will be checked for contamination. #' @param .data2 Second data frame with such columns. Will be used for checking for sequences which contaminated the first one. #' @param .limit Parameter for filtering: all sequences from \code{.data1} which are presented in \code{.data2} and (count of in \code{.data2}) / (count of seq in \code{.data1}) >= \code{.limit} are removed. #' @param .col Column's name with clonal count. #' @param .symm if T then perform filtering out of sequences in .data1, and then from .data2. Else only from .data1. #' #' @return Filtered \code{.data1} or a list with filtered both \code{.data1} and \code{.data2}. contamination.stats <- function (.data1, .data2, .limit = 20, .col = 'Read.count') { if (has.class(.data1, 'list') && has.class(.data2, 'list')) { res1 <- sapply(1:length(.data1), function (i) contamination.stats(.data1[[i]], .data2[[i]], .limit, .col)) colnames(res1) <- paste0(names(.data1), ':', names(.data2)) res2 <- sapply(1:length(.data1), function (i) contamination.stats(.data2[[i]], .data1[[i]], .limit, .col)) colnames(res2) <- paste0(names(.data2), ':', names(.data1)) return(cbind(res1, res2)) } inds <- intersectIndices(.data1$CDR3.nucleotide.sequence, .data2$CDR3.nucleotide.sequence, 'exact') logic <- .data2[inds[,2], .col] / .data1[inds[,1], .col] >= .limit counts <- .data1[logic, .col] c(Nclones.del = length(counts), summary(counts)) } decontamination <- function (.data1, .data2, .limit = 20, .col = 'Read.count', .symm = T) { .filter <- function (.data1, .data2, .limit, .col) { inds <- intersectIndices(.data1$CDR3.nucleotide.sequence, .data2$CDR3.nucleotide.sequence, 'exact') logic <- .data2[inds[,2], .col] / .data1[inds[,1], .col] >= .limit .data1[!logic, ] } if (has.class(.data1, 'list') && has.class(.data2, 'list')) { res <- lapply(1:length(.data1), function (i) decontamination(.data1[[i]], .data2[[i]], .limit, .col, T)) return(res) } if (.symm) { list(First = .filter(.data1, .data2, .limit, .col), Second = .filter(.data2, .data1, .limit, .col)) } else { .filter(.data1, .data2, .limit, .col) } }tcR/R/stats.R0000644000176200001440000006025213667026401012476 0ustar liggesusers########## Repertoire statistics ########## #' In-frame / out-of-frame sequences filter. #' #' @aliases get.inframes get.outframes count.inframes count.outframes get.frames count.frames clonotypescount #' #' @description #' Return the given data frame with in-frame or out-of-frame sequences only. Nucleotide sequences in a column "CDR3.nucleotide.sequence" are checked if #' they length are divisible by 3 (len mod 3 == 0 => in-frame, else out-of-frame) #' #' @usage #' get.inframes(.data, .head = 0, .coding = T) #' #' get.outframes(.data, .head = 0) #' #' count.inframes(.data, .head = 0, .coding = T) #' #' count.outframes(.data, .head = 0) #' #' get.frames(.data, .frame = c('in', 'out', 'all'), .head = 0, .coding = T) #' #' count.frames(.data, .frame = c('in', 'out', 'all'), .head = 0, .coding = T) #' #' @param .data MiTCR data.frame or a list with mitcr data.frames. #' @param .frame Which *-frames to choose. #' @param .head Parameter to the head() function. Supply 0 to get all elements. \code{head} applied before subsetting, i.e. #' if .head == 500, you will get in-frames from the top 500 clonotypes. #' @param .coding if T then return only coding sequences, i.e. without stop-codon. #' #' @return Filtered data.frame or a list with such data.frames. get.inframes <- function (.data, .head = 0, .coding = T) { if (class(.data) == 'list') { return(lapply(.data, get.inframes, .head = .head, .coding = .coding)) } .data <- head(.data, if (.head == 0) {nrow(.data)} else {.head}) if (.coding) { d <- subset(.data, nchar(.data$CDR3.nucleotide.sequence) %% 3 == 0) d[grep('[*, ~]', d$CDR3.amino.acid.sequence, invert = T), ] } else { subset(.data, nchar(.data$CDR3.nucleotide.sequence) %% 3 == 0) } } get.outframes <- function (.data, .head = 0) { if (class(.data) == 'list') { return(lapply(.data, get.outframes, .head = .head)) } .data <- head(.data, if (.head == 0) {nrow(.data)} else {.head}) subset(.data, nchar(.data$CDR3.nucleotide.sequence) %% 3 != 0) } count.inframes <- function (.data, .head = 0, .coding = T) { if (class(.data) == 'list') { sapply(get.inframes(.data, .head, .coding), nrow) } else { nrow(get.inframes(.data, .head, .coding)) } } count.outframes <- function (.data, .head = 0) { if (class(.data) == 'list') { sapply(get.outframes(.data, .head), nrow) } else { nrow(get.outframes(.data, .head)) } } get.frames <- function (.data, .frame = c('in', 'out', 'all'), .head = 0, .coding = T) { if (.frame[1] == 'in') { get.inframes(.data, .head, .coding) } else if (.frame[1] == 'out') { get.outframes(.data, .head) } else { head(.data, if (.head == 0) {nrow(.data)} else {.head}) } } count.frames <- function (.data, .frame = c('in', 'out', 'all'), .head = 0, .coding = T) { if (.frame[1] == 'in') { count.inframes(.data, .head, .coding) } else if (.frame[1] == 'out') { count.outframes(.data, .head) } else { nrow(head(.data, if (.head == 0) {nrow(.data)} else {.head})) } } clonotypescount <- function(.data, .head = 0) { length(unique(as.character(head(.data, if (.head == 0) {nrow(.data)} else {.head})$CDR3.amino.acid.sequence))) } #' MiTCR data frame basic statistics. #' #' @aliases cloneset.stats repseq.stats #' #' @usage #' cloneset.stats(.data, .head = 0) #' #' repseq.stats(.data, .head = 0) #' #' @description #' Compute basic statistics of TCR repertoires: number of clones, number of clonotypes, #' number of in-frame and out-of-frame sequences, summary of "Read.count", "Umi.count" and other. #' #' @param .data tcR data frames or a list with tcR data frames. #' @param .head How many top clones use to comput summary. #' #' @return if \code{.data} is a data frame, than numeric vector with statistics. If \code{.data} is #' a list with data frames, than matrix with statistics for each data frame. #' #' @examples #' \dontrun{ #' # Compute basic statistics of list with data frames. #' cloneset.stats(immdata) #' repseq.stats(immdata) #' } cloneset.stats <- function (.data, .head = 0) { if (has.class(.data, 'list')) { res <- t(do.call(cbind, lapply(.data, cloneset.stats, .head = .head))) row.names(res) <- names(.data) return(res) } .head <- if (.head == 0) {nrow(.data)} else {.head} res <- c(as.numeric(.head), clonotypescount(.data, .head), clonotypescount(.data, .head) / .head, count.inframes(.data, .head), count.inframes(.data, .head) / .head, count.outframes(.data, .head), count.outframes(.data, .head) / .head) names(res) <- c('#Nucleotide clones','#Aminoacid clonotypes', '%Aminoacid clonotypes', '#In-frames', '%In-frames', '#Out-of-frames', '%Out-of-frames') .data <- head(.data, .head) res2 <- c(Sum = sum(.data$Read.count), summary(.data$Read.count)) names(res2) <- sub('.', '', names(res2), fixed = T) names(res2) <- paste0(names(res2), '.reads') if (!is.na(.data$Umi.count)[1]) { res3 <- c(Sum = sum(.data$Umi.count), summary(.data$Umi.count)) names(res3) <- sub('.', '', names(res3), fixed = T) names(res3) <- paste0(names(res3), '.UMIs') res2 <- c(res2, res3) } c(res, res2) } repseq.stats = function(.data, .head=0) { if (has.class(.data, "list")) { res=do.call(cbind, lapply(.data, repseq.stats, .head=.head)) dimnames(res)[[2]]=names(.data) return(t(res)) }else{ .umi <- !is.na(.data$Umi.count[1]) .head= if (.head==0){nrow(.data)} else {.head} .data=head(.data, .head) if (!.umi) { res=c(nrow(.data), sum(.data$'Read.count'), round(sum(.data$'Read.count')/nrow(.data), digits = 2)) names(res)=c('Clones', "Sum.reads", "Reads.per.clone") return(res) }else{ res=c(nrow(.data), sum(.data$'Read.count'), sum(.data$'Umi.count'), round(sum(.data$'Read.count') / sum(.data$'Umi.count'), digits = 2), round(sum(.data$'Umi.count') / nrow(.data), digits = 2)) names(res)=c('Clones', "Sum.reads", "Sum.UMIs", "Reads.per.UMI", "UMI.per.clone") return(res) } } } #' Columns statistics. #' #' @aliases column.summary insertion.stats #' #' @usage #' column.summary(.data, .factor.col, .target.col, .alphabet = NA, .remove.neg = T) #' #' insertion.stats(.data) #' #' @description #' \code{column.summary} - general function for computing summary statistics (using the \code{summary} function) for columns of the given mitcr data.frame: #' divide \code{.factor.column} by factors from \code{.alphabet} and compute statistics #' of correspondingly divided \code{.target.column}. #' #' \code{insertion.stats} - compute statistics of insertions for the given mitcr data.frame. #' #' @param .data Data frame with columns \code{.factor.col} and \code{target.col} #' @param .factor.col Columns with factors by which the data will be divided to subsets. #' @param .target.col Column with numeric values for computing summaries after dividing the data to subsets. #' @param .alphabet Character vector of factor levels. If NA than use all possible elements frim the \code{.factor.col} column. #' @param .remove.neg Remove all elements which >-1 from the \code{.target.col} column. #' #' @seealso \link{summary} #' #' @return Data.frame with first column with levels of factors and 5 columns with output from the \code{summary} function. #' #' @examples #' \dontrun{ #' # Compute summary statistics of VD insertions #' # for each V-segment using all V-segments in the given data frame. #' column.summary(immdata[[1]], 'V.gene', 'Total.insertions') #' # Compute summary statistics of VD insertions for each V-segment using only V-segments #' # from the HUMAN_TRBV_MITCR #' column.summary(immdata[[1]], 'V.gene', 'Total.insertions', HUMAN_TRBV_MITCR) #' } column.summary <- function (.data, .factor.col, .target.col, .alphabet = NA, .remove.neg = T) { if (length(.alphabet) != 0 && !is.na(.alphabet[1])) { .data[!(.data[, .factor.col] %in% .alphabet), .factor.col] <- 'Other' } if (.remove.neg) { .data <- .data[.data[, .target.col] > -1, ] } res <- do.call(rbind, tapply(.data[, .target.col], .data[, .factor.col], function (x) c(summary(x)))) res <- data.frame(A = row.names(res), res, stringsAsFactors=F) names(res)[1] <- .factor.col row.names(res) <- NULL res } insertion.stats <- function (.data) { if (class(.data) == 'list') { return(lapply(.data, insertion.stats)) } vd <- column.summary(.data, 'V.gene', 'VD.insertions', HUMAN_TRBV_MITCR) names(vd)[-1] <- paste0('VD.', names(vd)[-1]) dj <- column.summary(.data, 'V.gene', 'DJ.insertions', HUMAN_TRBV_MITCR) names(dj)[-1] <- paste0('DJ.', names(dj)[-1]) tot <- column.summary(.data, 'V.gene', 'Total.insertions', HUMAN_TRBV_MITCR) names(tot)[-1] <- paste0('Total.', names(tot)[-1]) res <- merge(vd, dj, by = 'V.gene', all = T) res <- merge(res, tot, by = 'V.gene', all = T) res } #' Find target clonotypes and get columns' value corresponded to that clonotypes. #' #' @description #' Find the given target clonotypes in the given list of data.frames and get corresponding values of desired columns. #' #' @param .data List with mitcr data.frames or a mitcr data.frame. #' @param .targets Target sequences or elements to search. Either character vector or a matrix / data frame (not a data table!) with two columns: first for sequences, second for V-segments. #' @param .method Method, which will be used to find clonotypes: #' #' - "exact" performs exact matching of targets; #' #' - "hamm" finds targets and close sequences using hamming distance <= 1; #' #' - "lev" finds targets and close sequences using levenshtein distance <= 1. #' #' @param .col.name Character vector with column names which values should be returned. #' @param .target.col Character vector specifying name of columns in which function will search for a targets. #' Only first column's name will be used for matching by different method, others will match exactly. #' \code{.targets} should be a two-column character matrix or data frame with second column for V-segments. #' @param .verbose if T then print messages about the search process. #' #' @return Data.frame. #' #' @examples #' \dontrun{ #' # Get ranks of all given sequences in a list of data frames. #' immdata <- set.rank(immdata) #' find.clonotypes(.data = immdata, .targets = head(immdata[[1]]$CDR3.amino.acid.sequence), #' .method = 'exact', .col.name = "Rank", .target.col = "CDR3.amino.acid.sequence") #' # Find close by levenhstein distance clonotypes with similar V-segments and return #' # their values in columns 'Read.count' and 'Total.insertions'. #' find.clonotypes(.data = twb, .targets = twb[[1]][, c('CDR3.amino.acid.sequence', 'V.gene')], #' .col.name = c('Read.count', 'Total.insertions'), .method = 'lev', #' .target.col = c('CDR3.amino.acid.sequence', 'V.gene')) #' } find.clonotypes <- function (.data, .targets, .method = c('exact', 'hamm', 'lev'), .col.name = 'Read.count', .target.col = 'CDR3.amino.acid.sequence', .verbose = T) { if (is.character(.targets) && length(.target.col) != 1) { cat("Target columns doesn't match the given .targets!\n") return() } if (!is.character(.targets) && ncol(.targets) != length(.target.col)) { cat("Target columns doesn't match the given .targets!\n") return() } if (has.class(.data, 'data.frame')) { .data <- list(Sample = .data) } if (.method[1] == 'lev') { .data <- lapply(.data, function (.data) .data[grep('[*, ~]', .data[, .target.col[1]], invert = T),]) } .verbose.msg('Searching for targets...\n', .verbose) if (.verbose) pb <- set.pb(length(.data)) dt.list <- list() for (i in 1:length(.data)) { if (.verbose) add.pb(pb) if (length(.target.col) == 1) { inds <- intersectIndices(.targets, .data[[i]][, .target.col], .method) } else { colnames(.targets) <- .target.col inds <- intersectIndices(.targets, .data[[i]][, .target.col], .method, .target.col) } inds <- inds[!duplicated(inds[,2]),] if(!is.matrix(inds)) { inds <- rbind(inds) } if (dim(inds)[1] == 0) { inds <- matrix(c(NA, NA), 1, 2) } res <- list() if (!is.na(inds[1,1])) { for (col.name in .col.name) { res[[col.name]] <- .data[[i]][inds[,2], col.name] } dt.list[[names(.data)[i]]] <- as.data.table(data.frame(.data[[i]][inds[,2], .target.col], res, stringsAsFactors = F), F) setnames(dt.list[[names(.data)[i]]], c(.target.col, .col.name)) setkeyv(dt.list[[names(.data)[i]]], .target.col) tc <- dt.list[[names(.data)[i]]][[.target.col[1]]] dups <- duplicated(tc) dups.i <- rep.int(0, length(dups)) k <- 1 for (j in 1:length(dups)) { if (dups[j]) { dups.i[j] <- k k <- k + 1 } else { k <- 1 } } dt.list[[names(.data)[i]]][[.target.col[1]]] <- sapply(1:length(tc), function (k) { if (k > 1) { if (tc[k] == tc[k-1]) { paste0(tc[k], '.', dups.i[k]) } else { tc[k] } } else { tc[k] } }, USE.NAMES = F) } else { dt.list[[names(.data)[i]]] <- do.call(data.table, sapply(c(.target.col, .col.name), function (cn) { x <- NA class(x) <- class(.data[[1]][1, cn]) if (length(.target.col) == 1) { rep(x, times = length(.targets)) } else { rep(x, times = nrow(.targets)) } }, simplify = F)) if (length(.target.col) == 1) { dt.list[[names(.data)[i]]][[.target.col]] <- .targets } else { for (tc in .target.col) { dt.list[[names(.data)[i]]][[tc]] <- .targets[[tc]] } } setnames(dt.list[[names(.data)[i]]], c(.target.col, .col.name)) setkeyv(dt.list[[names(.data)[i]]], .target.col) } } if (.verbose) close(pb) res <- dt.list[[1]] if (length(dt.list) > 1) { for (i in 2:length(dt.list)) { res <- merge(res, dt.list[[i]], by = .target.col, all = T, allow.cartesian = T, suffixes = paste0('.', c(names(dt.list)[i-1], names(dt.list)[i]))) } } for (i in 1:length(.col.name)) { if (.col.name[i] %in% names(res)) { setnames(res, .col.name[i], paste0(.col.name[i], '.', names(dt.list)[length(dt.list)])) } } res <- as.data.frame(res, stringsAsFactors = F) if (length(.col.name) > 1) { res <- res[, c(1:length(.target.col), cbind(seq(length(.target.col) + 1, ncol(res), 2), seq(length(.target.col) + 2, ncol(res), 2)))] } res[[.target.col[1]]] <- sapply(strsplit(res[[.target.col[1]]], '.', T, F, T), '[', 1, USE.NAMES = F) res <- res[order(res[[.target.col[1]]]),] tc <- res[[.target.col[1]]] dups <- duplicated(tc) dups.i <- rep('', length(dups)) k <- 1 for (j in 1:length(dups)) { if (dups[j]) { dups.i[j] <- paste0('.', as.character(k)) k <- k + 1 } else { k <- 1 } } row.names(res) <- paste0(res[[.target.col[1]]], dups.i) res } #' Perform sequential cross starting from the top of a data frame. #' #' @aliases top.cross top.cross.vec top.cross.plot #' #' @description #' \code{top.cross} - get top crosses of the given type between each pair of the given data.frames with \code{top.cross} function. #' #' \code{top.cross.vec} - get vector of cross values for each top with the \code{top.cross.vec} function. #' #' \code{top.cross.plot} - plot a plots with result with the \code{top.cross.plot} function. #' #' @usage #' top.cross(.data, .n = NA, .data2 = NULL, .type = 'ave', .norm = F, .verbose = T) #' #' top.cross.vec(.top.cross.res, .i, .j) #' #' top.cross.plot(.top.cross.res, .xlab = 'Top X clonotypes', #' .ylab = 'Normalised number of shared clonotypes', .nrow = 2, #' .legend.ncol = 1, .logx = T, .logy = T) #' #' @param .data Either list of data.frames or a data.frame. #' @param .n Integer vector of parameter appled to the head function; same as .n in the top.fun function. See "Details" for more information. #' @param .data2 Second data.frame or NULL if .data is a list. #' @param .type Parameter .type to the \code{tcR::intersect} function. #' @param .norm Parameter .norm to the \code{tcR::intersect} function. #' @param .top.cross.res Result from the \code{top.cross} function. #' @param .i,.j Coordinate of a cell in each matrix. #' @param .xlab Name for a x-lab. #' @param .ylab Name for a y-lab. #' @param .nrow Number of rows of sub-plots in the output plot. #' @param .legend.ncol Number of columns in the output legend. #' @param .logx if T then transform x-axis to log-scale. #' @param .logy if T then transform y-axis to log-scale. #' @param .verbose if T then plot a progress bar. #' #' @return #' \code{top.cross} - return list for each element in \code{.n} with intersection matrix (from \code{tcR::intersectClonesets}). #' #' \code{top.cross.vec} - vector of length \code{.n} with \code{.i}:\code{.j} elements of each matrix. #' #' \code{top.cross.plot} - grid / ggplot object. #' #' @details #' Parameter \code{.n} can have two possible values. It could be either integer vector of numbers (same as in the \code{top.fun} function) or #' NA and then it will be replaced internally by the value \code{.n <- seq(5000, min(sapply(.data, nrow)), 5000)}. #' #' @seealso \code{\link{intersect}} #' #' @examples #' \dontrun{ #' immdata.top <- top.cross(immdata) #' top.cross.plot(immdata.top) #' } top.cross <- function (.data, .n = NA, .data2 = NULL, .type = 'ave', .norm = F, .verbose = T) { if (!is.null(.data2)) { .data <- list(.data, .data2) } if (is.na(.n)[1]) { .n <- seq(5000, min(sapply(.data, nrow)), 5000) } # res <- lapply(.n, function(i) apply.symm(.data, cross, .head = i, .type = .type, .norm = .norm, .verbose=F)) res <- lapply(.n, function(i) { if (.verbose) cat('Head == ', i, ' :\n', sep = '') intersectClonesets(.data, .type, .head = i, .norm = .norm, .verbose = .verbose)}) names(res) <- .n res } top.cross.vec <- function (.top.cross.res, .i, .j) { sapply(.top.cross.res, function (mat) mat[.i, .j] ) } top.cross.plot <- function (.top.cross.res, .xlab = 'Top X clonotypes', .ylab = 'Normalised number of shared clonotypes', .nrow = 2, .legend.ncol = 1, .logx = T, .logy = T) { data.names <- colnames(.top.cross.res[[1]]) len <- length(data.names) ps <- lapply(1:len, function (i) { data <- data.frame(Top = as.numeric(names(.top.cross.res)), sapply((1:len), function (j) { res <- top.cross.vec(.top.cross.res, i, j) # res[is.na(res)] <- 0 res }), sapply((1:len), function (j) { rep(data.names[j], times=length(.top.cross.res)) })) names(data) <- c('Top', data.names, paste0(data.names, 'C')) p <- ggplot(data = data) for (j in (1:len)) { p <- p + geom_point(aes_string(x = 'Top', y = data.names[j], color = paste0(data.names[j], 'C'))) + geom_line(aes_string(x = 'Top', y = data.names[j], color = paste0(data.names[j], 'C'))) + xlab(.xlab) + ylab(.ylab) + theme_linedraw() } p + ggtitle(data.names[i]) + theme(legend.position = 'none') if (.logx) { p <- p + scale_x_log10() } if (.logy) { p <- p + scale_y_log10() } p <- p + .colourblind.discrete(len, T) } ) data <- data.frame(Top = as.numeric(names(.top.cross.res)), sapply((1:len), function (j) rep.int(1, length(.top.cross.res))), sapply((1:len), function (j) rep(data.names[j], times=length(.top.cross.res)))) names(data) <- c('Top', data.names, paste0(data.names, 'C')) p <- ggplot(data = data) for (j in (1:len)) { p <- p + geom_point(aes_string(x = 'Top', y = data.names[j], color = paste0(data.names[j], 'C'))) } # add.legend(ps, .nrow, .legend.ncol) sample.plot <- p + .colourblind.discrete(len, T) leg <- gtable_filter(ggplot_gtable(ggplot_build(sample.plot + guides(colour=guide_legend(title = 'Subject', ncol=.legend.ncol)))), "guide-box") grid.arrange(do.call(arrangeGrob, c(lapply(ps, function (x) x + theme(legend.position="none")), nrow = .nrow)), leg, widths=unit.c(unit(1, "npc") - leg$width, leg$width), nrow = 1, top ='Top crosses') # arrangeGrob(do.call(arrangeGrob, c(ps[1:2], nrow = .nrow)), leg, widths=unit.c(unit(1, "npc") - leg$width, leg$width), nrow = 1, top ='Top cross') } #' Bootstrap for data frames in package tcR. #' #' @description #' Resample rows (i.e., clones) in the given data frame and apply the given function to them. #' #' @param .data Data frame. #' @param .fun Function applied to each sample. #' @param .n Number of iterations (i.e., size of a resulting distribution). #' @param .size Size of samples. For \code{.sim} == "uniform" stands for number of rows to take. #' For \code{.sim} == "percentage" stands for number of UMIs / read counts to take. #' @param .sim A character string indicating the type of simulation required. Possible values are #' "uniform" or "percentage". See "Details" for more details of type of simulation. #' @param .postfun Function applied to the resulting list: list of results from each processed sample. #' @param .verbose if T then show progress bar. #' @param .prop.col Column with proportions for each clonotype. #' @param ... Further values passed to \code{.fun}. #' #' @return #' Either result from \code{.postfun} or list of length \code{.n} with values of \code{.fun}. #' #' @details #' Argument \code{.sim} can take two possible values: "uniform" (for uniform distribution), when #' each row can be taken with equal probability, and "perccentage" when each row can be taken with #' probability equal to its "Read.proportion" column. #' #' @examples #' \dontrun{ #' # Apply entropy.seg function to samples of size 20000 from immdata$B data frame for 100 iterations. #' bootstrap.tcr(immdata[[2]], .fun = entropy.seg, .n = 100, .size = 20000, .sim = 'uniform') #' } bootstrap.tcr <- function (.data, .fun = entropy.seg, .n = 1000, .size = nrow(.data), .sim = c('uniform', 'percentage'), .postfun = function (x) { unlist(x) }, .verbose = T, .prop.col = 'Read.proportion', ...) { .sample.fun <- function (d) { d[sample(1:nrow(d), .size, T),] } if (.sim[1] == 'percentage') { .sample.fun <- function (d) { new.reads <- rmultinom(1, .size, d[, .prop.col]) d$Read.count <- new.reads d[, .prop.col] <- new.reads / sum(new.reads) d[new.reads > 0,] } } if (.verbose) { pb <- set.pb(.n) } res <- lapply(1:.n, function (n) { if (.verbose) { add.pb(pb) } .fun(.sample.fun(.data), ...) }) if (.verbose) { close(pb) } .postfun(res) } # mean + IQR #' Clonal space homeostasis. #' #' @description #' Compute clonal space homeostatsis - statistics of how many space occupied by clones #' with specific proportions. #' #' @param .data Cloneset data frame or list with such data frames. #' @param .clone.types Named numeric vector. #' @param .prop.col Which column to use for counting proportions. #' #' @seealso \link{vis.clonal.space} #' #' @examples #' \dontrun{ #' data(twb) #' # Compute summary space of clones, that occupy #' # [0, .05) and [.05, 1] proportion. #' clonal.space.homeostasis(twb, c(Low = .05, High = 1))) #' # Low (0 < X <= 0.05) High (0.05 < X <= 1) #' # Subj.A 0.9421980 0.05780198 #' # Subj.B 0.9239454 0.07605463 #' # Subj.C 0.8279270 0.17207296 #' # Subj.D 1.0000000 0.00000000 #' # I.e., for Subj.D sum of all read proportions for clones #' # which have read proportion between 0 and .05 is equal to 1. #' } clonal.space.homeostasis <- function (.data, .clone.types = c(Rare = .00001, Small = .0001, Medium = .001, Large = .01, Hyperexpanded = 1), .prop.col = 'Read.proportion') { .clone.types <- c(None = 0, .clone.types) if (has.class(.data, 'data.frame')) { .data <- list(Sample = .data) } mat <- matrix(0, length(.data), length(.clone.types) - 1, dimnames = list(names(.data), names(.clone.types)[-1])) .data <- lapply(.data, '[[', .prop.col) for (i in 2:length(.clone.types)) { mat[,i-1] <- sapply(.data, function (x) sum(x[x > .clone.types[i-1] & x <= .clone.types[i]])) colnames(mat)[i-1] <- paste0(names(.clone.types[i]), ' (', .clone.types[i-1], ' < X <= ', .clone.types[i], ')') } mat }tcR/R/repdiversity.R0000644000176200001440000000671313667027733014104 0ustar liggesusers#' General function for the repertoire diversity estimation. #' #' @description #' General interface to all cloneset diversity functions. #' #' @param .data Cloneset or a list of clonesets. #' @param .method Which method to use for the diversity estimation. See "Details" for methods. #' @param .quant Which column to use for the quantity of clonotypes: "read.count" for the "Read.count" column, #' "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, "umi.prop" for #' the "Umi.proportion" column. #' @param .q q-parameter for the Diversity index. #' @param .norm If T than compute the normsalised entropy. #' @param .do.norm One of the three values - NA, T or F. If NA than check for distrubution (sum(.data) == 1) #' and normalise it with the given laplace correction value if needed. if T then do normalisation and laplace #' correction. If F than don't do normalisaton and laplace correction. #' @param .laplace Value for Laplace correction. #' #' @details #' You can see a more detailed description for each diversity method at \link{diversity}. #' #' Parameter \code{.method} can have one of the following value each corresponding to the specific method: #' #' - "div" for the true diversity, or the effective number of types (basic function \code{diversity}). #' #' - "inv.simp" for the inverse Simpson index (basic function \code{inverse.simpson}). #' #' - "gini" for the Gini coefficient (basic function \code{gini}). #' #' - "gini.simp" for the Gini-Simpson index (basic function \code{gini.simpson}). #' #' - "chao1" for the Chao1 estimator (basic function \code{chao1}). #' #' - "entropy" for the Shannon entropy measure (basic function \code{entropy}). #' #' @seealso \link{diversity}, \link{entropy} #' #' @examples #' \dontrun{ #' data(twb) #' twb.div <- repDiversity(twb, "chao1", "read.count") #' } repDiversity <- function (.data, .method = c("chao1", "gini.simp", "inv.simp", "gini", "div", "entropy"), .quant = c("read.count", "umi.count", "read.prop", "umi.prop"), .q = 5, .norm = F, .do.norm = NA, .laplace = 0) { message(" ======================================== !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !! The tcR package WILL SOON BE ORPHANED !! AND REMOVED FROM CRAN. !! !! A new package is available that is !! designed to replace tcR: !! immunarch -- https://immunarch.com/ !! !! We will be happy to help you to move !! to the new package. Feel free to contact us: !! http://github.com/immunomind/immunarch !! !! Sincerely, !! immunarch dev team and !! Vadim I. Nazarov, lead developer of tcR !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! =======================================") quant <- .column.choice(.quant, T) fun <- switch(.method[1], chao1 = function (x, ...) chao1(x), gini.simp = gini.simpson, inv.simp = inverse.simpson, gini = gini, div = function (x, ...) diversity(x, .q = .q, ...), entropy = function (x, ...) entropy(x, .norm = .norm, ...), { .verbose.msg("You have specified an invalid method identifier. Selected method: chao1\n", T); chao1 }) if (has.class(.data, 'data.frame')) { .data <- list(Sample = .data) } .data <- .fix.listnames(.data) sapply(.data, function (x) fun(x[[quant]], .do.norm = .do.norm, .laplace = .laplace)) }tcR/R/plots.R0000644000176200001440000011236113667026401012500 0ustar liggesusers########## Various plotting functions ########## if (getRversion() >= "2.15.1") { utils::globalVariables(c("Segment", 'Size', 'Freq', 'Sample', 'V.gene', 'J.gene', '..count..', 'Time.point', 'Proportion', 'Sequence', 'Lower', 'Upper', 'Lengths', 'Read.count', 'Var', 'Value', 'Group', 'variable', 'name', 'value', 'Kmers', 'Count', 'People', 'First', 'Second', 'Var1', 'Q0.025', 'Q0.975', 'Mean', 'Type', 'Clone.size', 'Q1', 'Q2', 'Symbol', 'Gene', 'Genes', 'Sample', 'label', 'Xrep', 'Yrep', 'Clonotype')) } # red - yellow - green .ryg.gradient <- function (.min = NA, .max = NA) { cs <- c('#66FF00', '#FFFF66', '#FF6633') if (!is.na(.min)) { scale_fill_gradientn(limits = c(.min, .max), colours = cs, na.value = 'grey60') } else { scale_fill_gradientn(colours = cs, na.value = 'grey60') } } # white/orange/yellow - green - blue # colourblind - friendly # for fill .colourblind.gradient <- function (.min = NA, .max = NA, .colour = F) { # cs <- c("#FFFFD9", "#41B6C4", "#225EA8") # cs <- c("#FFFFBB", "#41B6C4", "#225EA8") # cs <- c("#FFBB00", "#41B6C4", "#225EA8") <- old version # cs <- c("#FF4B20", "#FFB433", "#C6EDEC", "#85CFFF", "#0348A6") # cs <- c("#FF4B20", "#FFB433", "#C6FDEC", "#7AC5FF", "#0348A6") # scale_fill_gradientn(guide='colourbar', colours=c("#0072B2", "#EEEEEE", "#D55E00") cs <- c(c("#0072B2", "#EEEEEE", "#D55E00")) if (!is.na(.min)) { if (.colour) { scale_colour_gradientn(limits = c(.min, .max), guide='colorbar', colours = cs, na.value = 'grey60') } else { scale_fill_gradientn(limits = c(.min, .max), guide='colorbar', colours = cs, na.value = 'grey60') } } else { if (.colour) { scale_colour_gradientn(colours = cs, na.value = 'grey60') } else { scale_fill_gradientn(colours = cs, na.value = 'grey60') } } } # white/orange/yellow - green - blue # colourblind - friendly # for fill and colour .colourblind.vector <- function() { c("#FF4B20", "#FFB433", "#C6FDEC", "#7AC5FF", "#0348A6") } .colourblind.discrete <- function (.n, .colour = F) { # cs <- c("#FFFFD9", "#41B6C4", "#225EA8") # cs <- c("#FFFFBB", "#41B6C4", "#225EA8") # cs <- c("#FFBB00", "#41B6C4", "#225EA8") <- old version # cs <- c("#FF4B20", "#FFB433", "#C6FDEC", "#7AC5FF", "#0348A6") cs <- .colourblind.vector() if (.colour) { scale_colour_manual(values = colorRampPalette(cs)(.n)) } else { scale_fill_manual(values = colorRampPalette(cs)(.n)) } } .colourblind.discrete2 <- function (.n, .colour = F) { # cs <- c("#FFFFD9", "#41B6C4", "#225EA8") # cs <- c("#FFFFBB", "#41B6C4", "#225EA8") cs <- c("#FFAB00", "#41B6C4", "#225EA8") # <- old version # cs <- c("#FF4B20", "#FFB433", "#C6FDEC", "#7AC5FF", "#0348A6") # cs <- c("#FF4B20", "#FFB433", "#0348A6") if (.colour) { scale_colour_manual(values = colorRampPalette(cs)(.n)) } else { scale_fill_manual(values = colorRampPalette(cs)(.n)) } } # light blues - dark blues # for fill .blues.gradient <- function (.min = NA, .max = NA) { cs <- c("#F7FBFF", "#9ECAE1", "#2171B5") if (!is.na(.min)) { scale_fill_gradientn(limits = c(.min, .max), colours = cs, na.value = 'grey60') } else { scale_fill_gradientn(colours = cs, na.value = 'grey60') } } #' Plot a histogram of lengths. #' #' @description #' Plot a histogram of distribution of lengths of CDR3 nucleotide sequences. On y-axis are sum of read counts for each length. #' #' @param .data Data frame with columns 'CDR3.nucleotide.sequence' and 'Read.count' or list with such data frames. #' @param .ncol If .data is a list, than number of columns in a grid of histograms for each data frame in \code{.data}. Else not used. #' @param .name Title for this plot. #' @param .col Name of the column to use in computing the lengths distribution. #' #' @details #' If \code{.data} is a data frame, than one histogram will be plotted. Is \code{.data} is a list, than grid of histograms #' will be plotted. #' #' @return ggplot object. #' #' @examples #' \dontrun{ #' load('immdata.rda') #' # Plot one histogram with main title. #' vis.count.len(immdata[[1]], 'Main title here') #' # Plot a grid of histograms with 2 columns. #' vis.count.len(immdata, 2) #' } vis.count.len <- function (.data, .ncol = 3, .name = "", .col = 'Read.count') { if (has.class(.data, 'list')) { return(do.call(grid.arrange, c(lapply(1:length(.data), function (i) vis.count.len(.data[[i]], .col = .col, .name = names(.data)[i])), ncol = .ncol))) } tmp <- aggregate(as.formula(paste0(.col, " ~ nchar(CDR3.nucleotide.sequence)")), .data, sum) names(tmp) <- c('Lengths', "Count") ggplot() + geom_bar(aes(x = Lengths, y = Count, fill = Count), data = tmp, stat = 'identity', colour = 'black') + .colourblind.gradient(min(tmp$Count), max(tmp$Count)) + ggtitle(.name) + theme_linedraw() } #' Plot a histogram of counts. #' #' @description #' Plot a histogram of distribution of counts of CDR3 nucleotide sequences. On y-axis are number of counts. #' #' @param .data Cloneset data frame or a list of clonesets. #' @param .ncol If .data is a list, than number of columns in a grid of histograms for each data frame in \code{.data}. Else not used. #' @param .name Title for this plot. #' @param .col Name of the column with counts. #' #' @details #' If \code{.data} is a data frame, than one histogram will be plotted. Is \code{.data} is a list, than grid of histograms #' will be plotted. #' #' @return ggplot object. #' #' @examples #' \dontrun{ #' load('immdata.rda') #' # Plot one histogram with main title. #' vis.number.count(immdata[[1]], 'Main title here') #' # Plot a grid of histograms with 2 columns. #' vis.number.count(immdata, 2) #' } vis.number.count <- function (.data, .ncol = 3, .name = 'Histogram of clonotypes read counts', .col = "Read.count") { # cat('Limits for x-axis set to (0,50). Transform y-axis to sqrt(y).\n') if (has.class(.data, 'list')) { return(do.call(grid.arrange, c(lapply(1:length(.data), function (i) vis.number.count(.data[[i]], .col = .col, .name = names(.data)[i])), ncol = .ncol))) } counts <- data.frame(Count = .data[[.col]]) ggplot() + xlim(min(counts$Count), 300) + ylab('Frequency') + geom_histogram(aes(x = Count, fill = ..count..), data = counts, binwidth = 1, colour = 'black') + coord_trans(x = 'log10') + scale_y_log10() + ggtitle(.name) + .colourblind.gradient() + theme_linedraw() } #' Heatmap. #' #' @aliases vis.heatmap #' #' @description #' Plot a heatmap from a matrix or a data.frame #' #' @param .data Either a matrix with colnames and rownames specifyed or a data.frame with the first column of #' strings for row names and other columns stands for values. #' @param .title Main title of the plot. #' @param .labs Labs names. Character vector of length 2 (for naming x-axis and y-axis). #' @param .legend Title for the legend. #' @param .na.value Replace NAs with this values. #' @param .text if T then print \code{.data} values at tiles. #' @param .scientific If T then force show scientific values in the heatmap plot. #' @param .signif.digits Number of significant digits to show. Default - 4. #' @param .size.text Size for the text in the cells of the heatmap, 4 by default. #' @param .no.legend If T than remove the legend from the plot. #' @param .no.labs If T than remove x / y labels names from the plot. #' #' @return ggplot object. #' #' @examples #' \dontrun{ #' # Load your data. #' load('immdata.rda') #' # Perform cloneset overlap by amino acid sequences with V-segments. #' imm.av <- repOverlap(immdata, .seq = 'aa', .vgene = T) #' # Plot a heatmap. #' vis.heatmap(imm.av, .title = 'Immdata - (ave)-intersection') #' } vis.heatmap <- function (.data, .title = "Number of shared clonotypes", .labs = c('Sample', 'Sample'), .legend = 'Shared clonotypes', .na.value = NA, .text = T, .scientific = FALSE, .signif.digits = 4, .size.text = 4, .no.legend = F, .no.labs = F) { if (has.class(.data, 'data.frame')) { names <- .data[,1] .data <- as.matrix(.data[,-1]) row.names(.data) <- names } else if (is.null(dim(.data))) { .data = as.matrix(.data) } if (is.null(colnames(.data))) { colnames(.data) <- paste0('C', 1:ncol(.data)) } if (is.null(row.names(.data))) { row.names(.data) <- paste0('C', 1:nrow(.data)) } .data[is.na(.data)] <- .na.value tmp <- as.data.frame(.data) tmp$name <- row.names(.data) m <- melt(tmp, id.var = c('name')) m[,1] <- factor(m[,1], levels = rev(rownames(.data))) m[,2] <- factor(m[,2], levels = colnames(.data)) .cg <- .colourblind.gradient(min(m$value), max(m$value)) m$label <- format(m$value, scientific = .scientific, digits = .signif.digits) p <- ggplot(m, aes(x = variable, y = name, fill = value)) p <- p + geom_tile(aes(fill = value), colour = "white") if (.text) { p <- p + geom_text(aes(fill = value, label = label), size = .size.text) } # p <- p + geom_text(aes(fill = value, label = value)) # p <- p + .ryg.gradient(min(m$value), max(m$value)) p <- p + .cg # p <- p + .blues.gradient(min(m$value), max(m$value)) p <- p + ggtitle(.title) + guides(fill = guide_colourbar(title=.legend)) + xlab(.labs[1]) + ylab(.labs[2]) + coord_fixed() + theme_linedraw() + theme(axis.text.x = element_text(angle=90, vjust = .5)) + scale_x_discrete(expand=c(0,0)) + scale_y_discrete(expand=c(0,0)) if (.no.legend) { p <- p + theme(legend.position="none") } if (.no.labs) { p <- p + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) } p } #' Boxplot for groups of observations. #' #' @aliases vis.group.boxplot #' #' @description #' Plot boxplots for each group. #' #' @param .data Either a matrix with colnames and rownames specifyed or a data frame with the first column of #' strings for row names and other columns stands for values. #' @param .groups Named list with character vectors for names of elements for each group. If NA than each #' member is in the individual group. #' @param .title Main title of the plot. #' @param .labs Labs names. Character vector of length 1 (for naming both axis with same name) or 2 (first elements stands for x-axis). #' @param .rotate.x if T then rotate x-axis. #' @param .violin If T then plot a violin plot. #' @param .notch "notch" parameter to the \code{geom_boxplot} ggplo2 function. #' @param ... Parameters passed to \code{melt}, applied to \code{.data} before plotting in \code{vis.group.boxplot}. #' #' @return ggplot object. #' #' @examples #' \dontrun{ #' names(immdata) # "A1" "A2" "B1" "B2" "C1" "C2" #' # Plot a boxplot for V-usage for each plot #' # three boxplots for each group. #' vis.group.boxplot(freq.Vb(immdata), #' list(A = c('A1', 'A2'), B = c('B1', 'B2'), C = c('C1', 'C2')), #' c('V segments', 'Frequency')) #' #' data(twb) #' ov <- repOverlap(twb) #' sb <- matrixSubgroups(ov, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D'))); #' vis.group.boxplot(sb) #' } vis.group.boxplot <- function (.data, .groups = NA, .labs = c('V genes', 'Frequency'), .title = '', .rotate.x = T, .violin = T, .notch = F, ...) { # if (has.class(.data, 'data.frame')) { # .data$Sample <- .data[,1] # .data <- .data[,c(1,3,2)] # } else { if (ncol(.data) > 2) { .data <- melt(.data, ...) } else { .data$Sample = .data[,1] .data = .data[,c(1,3,2)] } # } colnames(.data) <- c('Var', 'Sample', 'Value') .data$Group <- as.character(.data$Sample) if (!is.na(.groups)[1]) { for (i in 1:length(.groups)) { for (name in .groups[[i]]) { .data$Group[.data$Sample == name] <- names(.groups)[i] } } } p <- ggplot() + geom_boxplot(aes(x = Var, y = Value, fill = Group), data = .data, colour = 'black', notch = .notch) if (.violin) { p <- p +geom_violin(aes(x = Var, y = Value, fill = Group), alpha = .2, data = .data) } if (length(.labs) >= 2) { p <- p + xlab(.labs[1]) + ylab(.labs[2]) } p <- p + ggtitle(.title) + theme_linedraw() if (.rotate.x) { p <- p + theme(axis.text.x = element_text(angle=90)) } p + .colourblind.discrete(length(unique(.data$Group))) } #' Histogram of segments usage. #' #' @aliases vis.V.usage vis.J.usage #' #' @description #' Plot a histogram or a grid of histograms of V- / J-usage. #' #' @param .data Mitcr data frame or a list with mitcr data frames. #' @param .genes Gene alphabet passed to \link{geneUsage}. #' @param .main Main title of the plot. #' @param .ncol Number of columns in a grid of histograms if \code{.data} is a list and \code{.dodge} is F. #' @param .coord.flip if T then flip coordinates. #' @param .labs Character vector of length 2 with names for x-axis and y-axis. #' @param .dodge If \code{.data} is a list, than if this is T plot V-usage for all data frames to the one histogram. #' @param ... Parameter passed to \code{geneUsage}. By default the function compute V-usage or J-usage for beta chains #' w/o using read counts and w/ "Other" segments. #' #' @return ggplot object. #' #' @examples #' \dontrun{ #' # Load your data. #' load('immdata.rda') #' # Compute V-usage statistics. #' imm1.vs <- geneUsage(immdata[[1]], HUMAN_TRBV) #' vis.V.usage(immdata, HUMAN_TRBV, .main = 'Immdata V-usage [1]', .dodge = T) #' # Plot a histogram for one data frame using all gene segment data from V.gene column. #' vis.V.usage(imm1.vs, NA, .main = 'Immdata V-usage [1]') #' # Plot a grid of histograms - one histogram for V-usage for each data frame in .data. #' vis.V.usage(immdata, HUMAN_TRBV, .main = 'Immdata V-usage', .dodge = F, .other = F) #' } vis.gene.usage <- function (.data, .genes = NA, .main = "Gene usage", .ncol = 3, .coord.flip = F, .dodge = F, .labs = c("Gene", "Frequency"), ...) { if (!is.na(.genes[1])) { res <- geneUsage(.data, .genes, ...) } else { res <- .data } if (class(res[[2]]) != "factor") { res <- melt(res) res <- res[1:nrow(res), ] colnames(res) <- c('Gene', 'Sample', 'Freq') } if (length(unique(res$Sample)) > 1) { if (.dodge) { p = ggplot() + geom_bar(aes(x = Gene, y = Freq, fill = Sample), data = res, stat = 'identity', position = position_dodge(), colour = 'black') + theme_linedraw() + ggtitle(.main) + theme(axis.text.x = element_text(angle=90, vjust = .5)) + .colourblind.discrete(length(unique(res$Sample))) + scale_y_continuous(expand = c(.02,0)) + xlab(.labs[1]) + ylab(.labs[2]) if (.coord.flip) { p <- p + coord_flip() } p } else { res <- split(res, res$Sample) ps <- lapply(1:length(res), function (i) { vis.gene.usage(res[[i]], NA, names(res)[i], 0, .coord.flip, .labs = .labs, ...) }) do.call(grid.arrange, c(ps, ncol = .ncol, top = .main) ) } } else { p <- ggplot() + geom_bar(aes(x = Gene, y = Freq, fill = Freq), data = res, stat = 'identity', colour = 'black') if (.coord.flip) { p <- p + coord_flip() } p + theme_linedraw() + theme(axis.text.x = element_text(angle=90, vjust = .5)) + ggtitle(.main) + .colourblind.gradient() + scale_y_continuous(expand = c(.02,0)) + xlab(.labs[1]) + ylab(.labs[2]) } } #' PCA result visualisation #' #' @description #' Plot the given pca results with colour divided by the given groups. #' #' @param .data Result from prcomp() function or a data frame with two columns 'First' and 'Second' #' stands for the first PC and the second PC. #' @param .groups List with names for groups and indices of the group members. If NA than each #' member is in the individual group. #' @param .text If T than print the names of the subjects. #' #' @return ggplot object. #' #' @examples #' \dontrun{ #' data(twb) #' tmp = geneUsage(twb) #' vis.pca(prcomp(t(tmp[,-1]))) #' } vis.pca <- function (.data, .groups = NA, .text = T) { if (has.class(.data, 'data.frame')) { dnames <- row.names(.data) .data <- data.frame(First = .data[,1], Second = .data[,2], Sample = row.names(.data), Group = rep('group0', times = length(.data[,2])), stringsAsFactors=F) } else { dnames <- row.names(.data$x) .data <- data.frame(First = .data$x[,1], Second = .data$x[,2], Sample = row.names(.data$x), Group = rep('group0', times = length(.data$x[,2])), stringsAsFactors=F) } if (is.na(.groups[1])) { .groups <- lapply(1:nrow(.data), function (i) i) names(.groups) <- dnames } for (i in 1:length(.groups)) { for (j in 1:length(.groups[[i]])) { .data$Group[.groups[[i]][j]] <- names(.groups)[i] } } p = ggplot() + geom_point(aes(x = First, y = Second, colour = Group), size = 3, data = .data) if (.text) { p = p + geom_text(aes(x = First, y = Second, label = Sample, colour = Group), data = .data, hjust=0, vjust=0) } p = p + theme_linedraw() + .colourblind.discrete2(length(.groups), T) p } #' Radar-like / spider-like plots. #' #' @description #' Plot a grid of radar(-like) plots for visualising a distance among objects. #' #' @param .data Square data frame or matrix with row names and col names stands for objects and values for distances. #' @param .ncol Number of columns in the grid. #' @param .which Character vector, which datasets to show. #' @param .expand Integer vector of length 2, for \code{scale_y_continous(expand = .expand)} function. #' #' @seealso \link{repOverlap}, \link{js.div} #' #' @examples #' \dontrun{ #' load('immdata.rda') #' # Compute Jensen-Shannon divergence among V-usage of repertoires. #' imm.js <- js.div.seg(immdata, .verbose = F) #' # Plot it. #' vis.radarlike(imm.js) #' } vis.radarlike <- function (.data, .ncol = 3, .which = NA, .expand = c(.25, 0)) { step = ncol(.data) data.names <- colnames(.data) .data <- as.data.frame(melt(.data)) .data[is.na(.data[,3]),3] <- 0 if (!is.na(.which[1])) { .data = .data[.data$Var2 %in% .which, ] } ps <- lapply(seq(1, nrow(.data), step), function (l) { ggplot(.data[l:(l+step-1),], aes(x = Var1, y = value, fill = Var1)) + geom_bar(colour = 'black', stat = 'identity') + coord_polar() + ggtitle(names(.data)[l]) + scale_y_continuous(expand = .expand) + guides(fill = guide_legend(title="Sample")) + theme_linedraw() + xlab('') + ylab('') + ggtitle(.data$Var2[l]) + .colourblind.discrete(length(data.names)) }) do.call(grid.arrange, c(ps, ncol = .ncol)) } #' Visualisation of top clones proportions. #' #' @description #' Visualisation of proportion of the top clones. #' #' @param .data Data frame with clones. #' @param .head Integer vector of clones for the \code{.head} parameter for the \code{top.proportion} function. #' @param .col Parameter \code{.col} for the \code{top.proportion} function. #' #' @seealso \code{top.proportion} #' #' @examples #' \dontrun{ #' vis.top.proportions(immdata) #' } vis.top.proportions <- function (.data, .head = c(10, 100, 1000, 10000, 30000, 100000, 300000, 1000000), .col = "Read.count") { if (has.class(.data, 'data.frame')) { .data <- list(Sample = .data) } res <- sapply(.head, function (h) top.proportion(.data, h, .col)) tmp <- res if (is.null(dim(tmp))) { tmp <- t(as.matrix(tmp)) res <- t(as.matrix(res)) } for (i in 2:ncol(res)) { tmp[,i] <- res[,i] - res[,i-1] } res <- tmp colnames(res) <- paste0('[', c(1, .head[-length(.head)] + 1), ':', .head, ')') res <- as.data.frame(res) res$People <- factor(row.names(res), levels = row.names(res)) res <- melt(res) # res$variable <- factor(as.character(res$variable), labels = paste0('[', c(1, .head[-length(.head)] + 1), ':', .head, ')'), ordered = T) ggplot() + geom_bar(aes(x = People, y = value, fill = variable), data = res, stat = 'identity', position = 'stack', colour = 'black')+ theme_linedraw() + theme(axis.text.x = element_text(angle=90, vjust = .5)) + ylab("Clonal proportion") + xlab("Sample") + ggtitle("Summary proportion of the top N clones") + guides(fill = guide_legend("Top N clones")) + .colourblind.discrete(length(.head)) # scale_y_continuous(expand = c(0, 0)) } #' Rarefaction statistics visualisation. #' #' @description #' Plot a line with mean unique clones. #' #' @param .muc.res Output from the \code{muc} function. #' @param .groups List with names for groups and names of the group members. If NULL than each #' member is in the individual group. #' @param .log if T then log-scale the y axis. #' @param .names If T then print number of samples. #' #' @seealso \link{rarefaction} #' #' @examples #' \dontrun{ #' data(twb) #' names(twb) # "Subj.A" "Subj.B" "Subj.C" "Subj.D" #' twb.rar <- rarefaction(twb, .col = "Read.count") #' vis.rarefaction(twb.rar, list(A = c("Subj.A", "Subj.B"), B = c("Subj.C", "Subj.D"))) #' } vis.rarefaction <- function (.muc.res, .groups = NULL, .log = F, .names = T) { .muc.res$Group <- .muc.res$People if (!is.null(.groups)) { for (i in 1:length(.groups)) { for (j in 1:length(.groups[[i]])) { .muc.res$Group[.muc.res$People == .groups[[i]][j] ] <- names(.groups)[i] } } } .muc.res$Type <- factor(.muc.res$Type, levels = c('interpolation', 'extrapolation'), ordered = T) p <- ggplot() + # geom_point(aes(x = Size, y = Mean, colour = Group), data = .muc.res, size = 2) + geom_line(aes(x = Size, y = Mean, colour = Group, Group = People, linetype = Type), data = .muc.res) + # geom_errorbar(aes(x = Size, y = Mean, ymin = Q0.025, ymax = Q0.975, colour = Group), data = .muc.res) + xlab('Sample size') + ylab('Clones') + ggtitle("Rarefaction analysis") + theme_linedraw() + .colourblind.discrete(length(unique(.muc.res$Group)), T) if (.names) { for (subj in unique(.muc.res$People)) { tmp <- tail(.muc.res[.muc.res$People == subj, ], 1) p <- p + geom_text(aes(x = Size, y = Mean, label = People), data = tmp, hjust=1, vjust=1) } } if (.log) { p <- p + scale_x_log10() } p } #' Plot of the most frequent kmers. #' #' @description #' Plot a distribution (bar plot) of the most frequent kmers in a data. #' #' @param .kmers Data frame with two columns "Kmers" and "Count" or a list with such data frames. See Examples. #' @param .head Number of the most frequent kmers to choose for plotting from each data frame. #' @param .position Character vector of length 1. Position of bars for each kmers. Value for the \code{ggplot2} argument \code{position}. #' #' @seealso \code{get.kmers} #' #' @examples #' \dontrun{ #' # Load necessary data and package. #' library(gridExtra) #' load('immdata.rda') #' # Get 5-mers. #' imm.km <- get.kmers(immdata) #' # Plots for kmer proportions in each data frame in immdata. #' p1 <- vis.kmer.histogran(imm.km, .position = 'stack') #' p2 <- vis.kmer.histogran(imm.km, .position = 'fill') #' grid.arrange(p1, p2) #' } vis.kmer.histogram <- function (.kmers, .head = 100, .position = c('stack', 'dodge', 'fill')) { kmers.df <- data.frame(Kmers = '') for (i in 2:ncol(.kmers)) { kmers.df <- merge(head(.kmers[order(.kmers[, i], decreasing = T), c(1,i)], .head), kmers.df, all = T) } kmers.df[is.na(kmers.df)] <- 0 kmers.df <- melt(kmers.df[-1,]) names(kmers.df) <- c('Kmers', 'People', 'Count') p <- ggplot() + geom_bar(aes(x = Kmers, y = Count, fill = People), data = kmers.df, stat = 'identity', position = .position[1]) + theme_linedraw() if (.position[1] == 'stack' || .position[1] == 'dodge') { p <- p + ylab('Count') + theme(axis.text.x = element_text(angle=90, vjust = .5)) } else { p <- p + ylab('Proportions') + theme(axis.text.x = element_text(angle=90, vjust = .5)) } p + scale_y_continuous(expand = c(0, 0)) + .colourblind.discrete2(length(unique(kmers.df$People))) } #' Visualise clonal dynamics among time points. #' #' @description #' Visualise clonal dynamics (i.e., changes in frequency or count) with error bars of given #' clones among time points. #' #' @param .changed Result from the \code{find.clonotypes} function, i.e. data frame with first #' columns with sequences (nucleotide or amino acid) and other columns are columns with frequency / count #' for each time point for each clone. #' @param .lower Similar to .changed but values are lower bound for clonal count / frequency. #' @param .upper Similar to .changed but values are upper bound for clonal count / frequency. #' @param .log if T then log-scale y-axis. #' #' @return ggplot object. vis.clonal.dynamics <- function (.changed, .lower, .upper, .log = T) { .changed <- melt(.changed, id.vars = names(.changed)[1]) .lower <- melt(.lower, id.vars = names(.changed)[1]) .upper <- melt(.upper, id.vars = names(.changed)[1]) names(.changed) <- c('Sequence', 'Time.point', 'Proportion') d <- cbind(.changed, Lower = .lower[,3], Upper = .upper[,3]) p <- ggplot() + geom_line(aes(x = Time.point, y = Proportion, colour = Sequence, group = Sequence), data = d) + geom_errorbar(aes(x = Time.point, y = Proportion, colour = Sequence, ymin = Lower, ymax = Upper), data = d, width = .25) + theme_linedraw() + theme(axis.text.x = element_text(angle=90)) + .colourblind.discrete(length(unique(.changed$Sequence)), .colour = T) if (.log) { p <- p + scale_y_log10() } p } #' Visualise occupied by clones homeostatic space among Samples or groups. #' #' @description #' Visualise which clones how much space occupy. #' #' @param .clonal.space.data Data from the \code{fclonal.space.homeostasis} function. #' @param .groups List of named character vector with names of Samples #' in \code{.clonal.space.data} for grouping them together. #' #' @seealso \link{clonal.space.homeostasis} #' #' @return ggplot object. vis.clonal.space <- function (.clonal.space.data, .groups = NULL) { melted <- melt(.clonal.space.data) colnames(melted) <- c('Sample', 'Clone.size', 'Proportion') melted$Sample <- as.character(melted$Sample) melted$Proportion <- as.numeric(as.character(melted$Proportion)) melted$Group <- melted$Sample if (!is.null(.groups)) { for (i in 1:length(.groups)) { for (j in 1:length(.groups[[i]])) { melted$Group[melted$Sample == .groups[[i]][j] ] <- names(.groups)[i] } } perc <- melt(tapply(melted$Proportion, list(melted$Group, melted$Clone.size), function (x) c(quantile(x, probs = .25), mean(x), quantile(x, probs = .75)))) # return(perc) perc <- data.frame(row.names(perc), perc, stringsAsFactors = F) colnames(perc) <- c("Index", 'Group', "Clone.size", 'Q1', 'Mean', 'Q2') print(perc) p <- ggplot() + geom_bar(aes(x = Group, y = Mean, fill = Clone.size), data = perc, colour = 'black', stat = 'identity') + geom_errorbar(aes(x = Group, ymin = Q1, ymax = Q2), data = perc, colour = 'black') + xlab("Sample") } else { melted$Group = factor(melted$Group, levels = row.names(.clonal.space.data)) p <- ggplot() + geom_bar(aes(x = Group, y = Proportion, fill = Clone.size), data = melted, colour = 'black', stat = 'identity', position = 'stack') + xlab("Sample") } p + theme_linedraw() + theme(axis.text.x = element_text(angle=90, vjust = .5)) + ylab("Occupied homeostatic space, proportion") + ggtitle("Clonal space homeostasis") + guides(fill = guide_legend("Clone size")) + .colourblind.discrete(length(unique(melted$Clone.size))) + scale_y_continuous(expand = c(.01, .01)) + scale_x_discrete(expand = c(.02, .02)) } #' Logo - plots for amino acid and nucletide profiles. #' #' @description #' Plot logo-like graphs for visualising of nucleotide or amino acid motif sequences / profiles. #' #' @param .data Output from the \code{kmer.profile} function. #' @param .replace.zero.with.na if T then replace all zeros with NAs, therefore letters with #' zero frequency wont appear at the plot. #' @param .jitter.width,.jitter.height,.dodge.width Parameters to \code{position_jitterdodge} #' for aligning text labels of letters. #' #' @return ggplot2 object #' #' @examples #' \dontrun{ #' d <- kmer_profile(c('CASLL', 'CASSQ', 'CASGL')) #' vis.logo(d) #' } vis.logo <- function (.data, .replace.zero.with.na = T, .jitter.width = .01, .jitter.height = .01, .dodge.width = .15) { .data <- melt(.data) if (.replace.zero.with.na) { .data$value[.data$value == 0] <- NA } ggplot(aes(x = variable, y = value, fill = Symbol, colour = Symbol), data = .data) + geom_point(colour = 'black') + geom_text(aes(label = Symbol), size = 5, position = position_jitterdodge(jitter.width = .jitter.width, jitter.height = .jitter.height, dodge.width = .dodge.width)) + xlab("Position") + ylab("Proportion") + theme_linedraw() } #' Visualisation of shared clonotypes occurrences among repertoires. #' #' @description #' Visualise counts or proportions of shared clonotypes among repertoires. #' Code adapted from https://www.r-bloggers.com/ggplot2-cheatsheet-for-visualizing-distributions/. #' #' @param .shared.rep Shared repertoires, as from \link{shared.repertoire} function. #' @param .x.rep Which repertoire show on x-axis. Either a name or an index of a repertoire #' in the \code{.shared.rep} or NA to choose all repertoires. #' @param .y.rep Which repertoire show on y-axis. Either a name or an index of a repertoire #' in the \code{.shared.rep} or NA to choose all repertoires. #' @param .title Main title of the plot. #' @param .ncol Number of columns in the resulting plot. #' @param .point.size.modif Modify this to correct sizes of points. #' @param .cut.axes If T than cut axes' limits to show only frequencies that exists. #' @param .density If T than plot densities of shared and unique clonotypes. #' @param .lm If T than fit and plot a linear model to shared clonotypes. #' @param .radj.size Size of the text for R^2-adjusted. #' @param .plot If F than return grobs instead of plotting. #' #' @return ggplot2 object or plot #' #' @seealso \link{shared.repertoire} #' #' @examples #' \dontrun{ #' data(twb) #' # Show shared nucleotide clonotypes of all possible pairs #' # using the Read.proportion column #' twb.sh <- shared.repertoire(twb, "n0rp") #' vis.shared.clonotypes(twb.sh, .ncol = 4) #' #' # Show shared amino acid + Vseg clonotypes of pairs #' # including the Subj.A (the first one) using #' # the Read.count column. #' twb.sh <- shared.repertoire(twb, "avrc") #' vis.shared.clonotypes(twb.sh, 1, NA, .ncol = 4) #' # same, just another order of axis #' vis.shared.clonotypes(twb.sh, NA, 1, .ncol = 4) #' #' # Show shared nucleotide clonotypes of Subj.A (the first one) #' # Subj.B (the second one) using the Read.proportion column. #' twb.sh <- shared.repertoire(twb, "n0rp") #' vis.shared.clonotypes(twb.sh, 1, 2) #' #' # Show the same plot, but with much larget points. #' vis.shared.clonotypes(twb.sh, 1, 2, .point.size.modif = 3) #' } vis.shared.clonotypes <- function (.shared.rep, .x.rep = NA, .y.rep = NA, .title = NA, .ncol = 3, .point.size.modif = 1, .cut.axes = T, .density = T, .lm = T, .radj.size = 3.5, .plot = T) { mat <- shared.matrix(.shared.rep) if (is.na(.x.rep) && is.na(.y.rep)) { ps <- list() for (i in 1:ncol(mat)) { for (j in 1:ncol(mat)) { ps <- c(ps, list(vis.shared.clonotypes(.shared.rep, i, j, '', .point.size.modif = .point.size.modif, .cut.axes = .cut.axes, .density = .density, .lm = .lm, .radj.size = .radj.size, .plot = F))) } } grid.arrange(grobs = ps, ncol = .ncol, top = .title) } else if (is.na(.x.rep)) { ps <- lapply(1:ncol(mat), function (i) { vis.shared.clonotypes(.shared.rep, i, .y.rep, '', .point.size.modif = .point.size.modif, .cut.axes = .cut.axes, .density = .density, .lm = .lm) }) do.call(grid.arrange, c(ps, ncol = .ncol, top = .title)) } else if (is.na(.y.rep)) { ps <- lapply(1:ncol(mat), function (j) { vis.shared.clonotypes(.shared.rep, .x.rep, j, '', .point.size.modif = .point.size.modif, .cut.axes = .cut.axes, .density = .density, .lm = .lm) }) do.call(grid.arrange, c(ps, ncol = .ncol, top = .title)) } else { if (!is.character(.x.rep)) { .x.rep <- colnames(mat)[.x.rep] } if (!is.character(.y.rep)) { .y.rep <- colnames(mat)[.y.rep] } if (.x.rep == .y.rep) { return(rectGrob(gp=gpar(col="white"))) } df <- data.frame(cbind(mat[, .x.rep], mat[, .y.rep])) df[,1] = df[,1] / sum(df[,1], na.rm = T) df[,2] = df[,2] / sum(df[,2], na.rm = T) df_full = df df <- df[!is.na(df[,1]) & !is.na(df[,2]), ] freq <- log10(sqrt(as.numeric(df[, 1]) * df[, 2])) / 2 names(df) <- c("Xrep", "Yrep") names(df_full) <- c("Xrep", "Yrep") pnt.cols <- log(df[, 1] / df[, 2]) suppressWarnings(pnt.cols[pnt.cols > 0] <- pnt.cols[pnt.cols > 0] / max(pnt.cols[pnt.cols > 0])) suppressWarnings(pnt.cols[pnt.cols < 0] <- -pnt.cols[pnt.cols < 0] / min(pnt.cols[pnt.cols < 0])) if (.cut.axes) { mat.lims <- c(min(as.matrix(df_full), na.rm = T), max(as.matrix(df_full), na.rm = T)) } else { mat.lims <- c(min(as.matrix(df_full), na.rm = T), 1) } empty <- ggplot()+geom_point(aes(1,1), colour="white") + theme( plot.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank() ) min_df = min(floor(log10(min(df_full[,1], na.rm = T))), floor(log10(min(df_full[,2], na.rm = T)))) max_df = max(trunc(log10(max(df_full[,1], na.rm = T))), trunc(log10(max(df_full[,2], na.rm = T)))) breaks_values = 10**seq(min_df, 1) breaks_labels = format(log10(breaks_values), scientific = F) grey_col = "#CCCCCC" points = ggplot() + geom_point(aes(x = Xrep, y = Yrep, size = freq, fill = pnt.cols), data = df, shape=21) + scale_radius(range = c(.point.size.modif, .point.size.modif * 6)) + geom_abline(intercept = 0, slope = 1, linetype = "dashed") + theme_linedraw() + .colourblind.gradient(min(pnt.cols), max(pnt.cols)) + scale_x_log10(breaks = breaks_values, labels = breaks_labels, lim = mat.lims, expand = c(.015, .015)) + scale_y_log10(breaks = breaks_values, labels = breaks_labels, lim = mat.lims, expand = c(.015, .015)) + theme(legend.position="none") + xlab(.x.rep) + ylab(.y.rep) if (!is.na(.title)) { points = points + ggtitle(.title) } if (.lm) { adj.R.sq = summary(lm(Yrep ~ Xrep, df))$adj. points = points + geom_smooth(aes(x = Xrep, y = Yrep), method = "lm", data = df, fullrange = T, colour = "grey20", size = .5) + geom_text(aes(x = max(df_full, na.rm = T) / 4, y = min(df_full, na.rm = T), label = paste0("R^2(adj.) = ", as.character(round(adj.R.sq, 2)))), size = .radj.size) # ggtitle(paste0("R^2(adj.) = ", as.character(round(adj.R.sq, 2)))) } if (.density) { df2 = data.frame(Clonotype = df_full[!is.na(df_full[,1]) & is.na(df_full[,2]), 1], Type = "unique", stringsAsFactors = F) df2 = rbind(df2, data.frame(Clonotype = df_full[!is.na(df_full[,1]) & !is.na(df_full[,2]), 1], Type = "shared", stringsAsFactors = F)) top_plot = ggplot() + geom_density(aes(x = Clonotype, fill = Type), colour = "grey25", data = df2, alpha = .3) + scale_x_log10(breaks = 10**(seq(min_df, 0)), lim = mat.lims, expand = c(.12, .015)) + theme_bw() + theme(axis.title.x = element_blank(), axis.title.y = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), legend.position = "none") + scale_fill_manual(values = colorRampPalette(c(.colourblind.vector()[5], grey_col))(2)) df2 = data.frame(Clonotype = df_full[!is.na(df_full[,2]) & is.na(df_full[,1]), 2], Type = "unique", stringsAsFactors = F) df2 = rbind(df2, data.frame(Clonotype = df_full[!is.na(df_full[,2]) & !is.na(df_full[,1]), 2], Type = "shared", stringsAsFactors = F)) right_plot = ggplot() + geom_density(aes(x = Clonotype, fill = Type), colour = "grey25", data = df2, alpha = .3) + scale_x_log10(breaks = 10**(seq(min_df, 0)), lim = mat.lims, expand = c(.12, .015)) + coord_flip() + theme_bw() + theme(axis.title.x = element_blank(), axis.title.y = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), legend.position = "none") + scale_fill_manual(values = colorRampPalette(c(.colourblind.vector()[1], grey_col))(2)) if (.plot) { grid.arrange(top_plot, empty, points, right_plot, ncol=2, nrow=2, widths=c(4, 1), heights=c(1, 4)) } else { arrangeGrob(top_plot, empty, points, right_plot, ncol=2, nrow=2, widths=c(4, 1), heights=c(1, 4)) } } else { points } } } # vis.hill.numbers <- function (.hill.nums, .groups = NA) { # # } tcR/R/infoanalysis.R0000644000176200001440000001160413667026401014034 0ustar liggesusers#' Normalised log assymetry. #' #' @description #' Compute the value of the normalised log assymetry measure for the given data.frames #' of the counts of shared clones. #' #' @param .alpha First mitcr data.frame or a list with data.frames. #' @param .beta Second mitcr data.frame or NULL if \code{.alpha} is a list. #' @param .by Which column use to merge. See "Details". #' #' @details #' Merge two data frames by the given column and compute #' value Sum(Log((Percentage for shared clone S from alpha) / (Percentage for shared clone S from beta))) / (# of shared clones). #' #' @return Value of the normalised log assymetry measure for the given data.frames. assymetry<-function(.alpha, .beta = NULL, .by = 'CDR3.nucleotide.sequence'){ if (class(.alpha) == 'list') { return(apply.asymm(.alpha, assymetry, .by = .by)) } m<-merge(.alpha, .beta, by=.by) sum(log(m$Percentage.x/m$Percentage.y))/nrow(m) } #' Repertoires' analysis using information measures applied to V- and J- segment frequencies. #' #' @aliases entropy.seg js.div.seg #' #' @description #' Information approach to repertoire analysis. Function \code{entropy.seg} applies Shannon entropy to V-usage and hence measures variability of V-usage. #' Function \code{js.div.seg} applied Jensen-Shannon divergence to V-usage of two or more data frames and hence measures distance among this V-usages. #' #' @usage #' entropy.seg(.data, .genes = HUMAN_TRBV, .frame = c('all', 'in', 'out'), #' .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), #' .ambig = F) #' #' js.div.seg(.data, .genes = HUMAN_TRBV, .frame = c('all', 'in', 'out'), #' .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), #' .norm.entropy = T, .ambig = F, .verbose = F, .data2 = NULL) #' #' @param .data Mitcr data.frame or a list with mitcr data.frames. #' @param .data2 NULL if .data is a list, or a second mitcr data.frame. #' @param .genes Parameter to the \code{geneUsage} function. #' @param .frame Character vector of length 1 specified which *-frames should be used: #' only in-frame ('in'), out-of-frame ('out') or all sequences ('all'). #' @param .norm.entropy if T then divide result by mean entropy of 2 segments' frequencies. #' @param .ambig Parameter passed to \code{geneUsage}. #' @param .quant Which column to use for the quantity of clonotypes: NA for computing only number of genes without using clonotype counts, "read.count" for the "Read.count" column, #' "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, "umi.prop" for #' the "Umi.proportion" column. #' @param .verbose If T than output the data processing progress bar. #' #' @return For \code{entropy.seg} - numeric integer with entropy value(s). For \code{js.div.seg} - integer of vector one if \code{.data} and \code{.data2} are provided; #' esle matrix length(.data) X length(.data) if \code{.data} is a list. #' #' @seealso \link{vis.heatmap}, \link{vis.group.boxplot}, \link{geneUsage} entropy.seg <- function (.data, .genes = HUMAN_TRBV, .frame = c('all', 'in', 'out'), .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), .ambig = F) { if (class(.data) == 'list') { return(sapply(.data, entropy.seg, .quant = .quant, .frame = .frame, .genes = .genes, .ambig = .ambig)) } .data <- get.frames(.data, .frame) if (has.class(.genes, "list") && length(.genes) == 2) { entropy(geneUsage(.data, .genes = .genes, .quant = .quant, .ambig = .ambig)) } else { entropy(as.matrix(geneUsage(.data, .genes = .genes, .quant = .quant, .ambig = .ambig)[,-1])) } } js.div.seg <- function (.data, .genes = HUMAN_TRBV, .frame = c('all', 'in', 'out'), .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), .norm.entropy = T, .ambig = F, .verbose = F, .data2 = NULL) { if (class(.data) == 'list') { if (length(.data) == 2) { return(js.div.seg(.data[[1]], .genes, .frame, .quant, .norm.entropy, .ambig, .verbose, .data[[2]])) } else { return(apply.symm(.data, function (x, y) { js.div.seg(.data = x, .data2 = y, .quant = .quant, .frame = .frame, .ambig = .ambig, .norm.entropy = .norm.entropy, .genes = .genes) }, .verbose = .verbose)) } } .data <- get.frames(.data, .frame) .data2 <- get.frames(.data2, .frame) if (has.class(.genes, "list") && length(.genes) == 2) { freq.alpha <- geneUsage(.data, .genes = .genes, .quant = .quant, .ambig = .ambig) freq.beta <- geneUsage(.data2, .genes = .genes, .quant = .quant, .ambig = .ambig) } else { freq.alpha <- geneUsage(.data, .genes = .genes, .quant = .quant, .ambig = .ambig)[,-1] freq.beta <- geneUsage(.data2, .genes = .genes, .quant = .quant, .ambig = .ambig)[,-1] } nrm = if (.norm.entropy) 0.5 * (entropy(freq.alpha) + entropy(freq.beta)) else 1 js.div(freq.alpha, freq.beta) / nrm }tcR/R/RcppExports.R0000644000176200001440000000144713667551225013641 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 .exact_search <- function(vec, patterns, max_error = 1L, verbose = TRUE) { .Call('_tcR_exact_search', PACKAGE = 'tcR', vec, patterns, max_error, verbose) } .exact_search_list <- function(vec, patterns_list, max_error = 1L, verbose = TRUE) { .Call('_tcR_exact_search_list', PACKAGE = 'tcR', vec, patterns_list, max_error, verbose) } .hamming_search <- function(vec, patterns, max_error = 1L, verbose = TRUE) { .Call('_tcR_hamming_search', PACKAGE = 'tcR', vec, patterns, max_error, verbose) } .levenshtein_search <- function(vec, patterns, max_error = 1L, verbose = TRUE) { .Call('_tcR_levenshtein_search', PACKAGE = 'tcR', vec, patterns, max_error, verbose) } tcR/R/docdata.R0000644000176200001440000006512513667026401012743 0ustar liggesusers.set.attr <- function (.data, .attr, .value) { attr(.data, .attr) <- .value .data } #' Tables with genetic code. #' #' @docType data #' #' @aliases AA_TABLE AA_TABLE_REVERSED #' #' @description #' Tables with genetic code. #' #' @format #' AA_TABLE: #' #' \code{Class 'table' Named chr [1:65] "K" "N" "K" "N" ... #' ..- attr(*, "names")= chr [1:65] "AAA" "AAC" "AAG" "AAT" ...} #' #' AA_TABLE_REVERSED: #' #' \code{List of 22 #' $ *: chr [1:3] "TAA" "TAG" "TGA" #' $ A: chr [1:4] "GCA" "GCC" "GCG" "GCT" #' $ C: chr [1:2] "TGC" "TGT" #' $ D: chr [1:2] "GAC" "GAT" #' ... #' } #' #' @examples #' \dontrun{ #' AA_TABLE['ATG'] # => "M" #' AA_TABLE_REVERSED['K'] # => list(K = c("AAA", "AAG")) #' } AA_TABLE <- table(c('TCA', 'TCG', 'TCC', 'TCT', 'TTT', 'TTC', 'TTA', 'TTG', 'TAT', 'TAC', 'TAA', 'TAG', 'TGT', 'TGC', 'TGA', 'TGG', 'CTA', 'CTG', 'CTC', 'CTT', 'CCA', 'CCG', 'CCC', 'CCT', 'CAT', 'CAC', 'CAA', 'CAG', 'CGA', 'CGG', 'CGC', 'CGT', 'ATT', 'ATC', 'ATA', 'ATG', 'ACA', 'ACG', 'ACC', 'ACT', 'AAT', 'AAC', 'AAA', 'AAG', 'AGT', 'AGC', 'AGA', 'AGG', 'GTA', 'GTG', 'GTC', 'GTT', 'GCA', 'GCG', 'GCC', 'GCT', 'GAT', 'GAC', 'GAA', 'GAG', 'GGA', 'GGG', 'GGC', 'GGT', 'NNN')) AA_TABLE[c('TCA', 'TCG', 'TCC', 'TCT', 'TTT', 'TTC', 'TTA', 'TTG', 'TAT', 'TAC', 'TAA', 'TAG', 'TGT', 'TGC', 'TGA', 'TGG', 'CTA', 'CTG', 'CTC', 'CTT', 'CCA', 'CCG', 'CCC', 'CCT', 'CAT', 'CAC', 'CAA', 'CAG', 'CGA', 'CGG', 'CGC', 'CGT', 'ATT', 'ATC', 'ATA', 'ATG', 'ACA', 'ACG', 'ACC', 'ACT', 'AAT', 'AAC', 'AAA', 'AAG', 'AGT', 'AGC', 'AGA', 'AGG', 'GTA', 'GTG', 'GTC', 'GTT', 'GCA', 'GCG', 'GCC', 'GCT', 'GAT', 'GAC', 'GAA', 'GAG', 'GGA', 'GGG', 'GGC', 'GGT', 'NNN')] <- c('S', 'S', 'S', 'S', 'F', 'F', 'L', 'L', 'Y', 'Y', '*', '*', 'C', 'C', '*', 'W', 'L', 'L', 'L', 'L', 'P', 'P', 'P', 'P', 'H', 'H', 'Q', 'Q', 'R', 'R', 'R', 'R', 'I', 'I', 'I', 'M', 'T', 'T', 'T', 'T', 'N', 'N', 'K', 'K', 'S', 'S', 'R', 'R', 'V', 'V', 'V', 'V', 'A', 'A', 'A', 'A', 'D', 'D', 'E', 'E', 'G', 'G', 'G', 'G', '~') AA_TABLE_REVERSED <- sapply(unique(AA_TABLE), function (aa) { names(AA_TABLE)[AA_TABLE == aa] }) AA_TABLE_REVERSED <- AA_TABLE_REVERSED[order(names(AA_TABLE_REVERSED))] #' Alphabets of TCR and Ig gene segments. #' #' @docType data #' #' @name segments.alphabets #' #' @aliases genealphabets HUMAN_TRAV HUMAN_TRAJ HUMAN_TRBV HUMAN_TRBD HUMAN_TRBJ HUMAN_TRBV_MITCR HUMAN_TRGV HUMAN_TRGJ HUMAN_TRDV HUMAN_TRDD HUMAN_TRDJ HUMAN_IGHV HUMAN_IGHD HUMAN_IGHJ HUMAN_IGKV HUMAN_IGKJ HUMAN_IGLJ HUMAN_IGLV HUMAN_TRBV_ALS HUMAN_TRBV_FAM HUMAN_TRBV_GEN MACMUL_TRBJ MACMUL_TRBV MOUSE_TRBJ MOUSE_TRBV MOUSE_TRAV MOUSE_TRAJ MOUSE_IGKV MOUSE_IGKJ MOUSE_IGHV MOUSE_IGHD MOUSE_IGHJ MOUSE_TRDD MOUSE_TRDV MOUSE_TRDJ MOUSE_TRGV MOUSE_TRGJ MOUSE_IGLJ MOUSE_IGLV #' #' @usage #' HUMAN_TRAV #' #' HUMAN_TRAJ #' #' HUMAN_TRBV #' #' HUMAN_TRBD #' #' HUMAN_TRBJ #' #' HUMAN_TRBV_MITCR #' #' HUMAN_TRBV_ALS #' #' HUMAN_TRGV #' #' HUMAN_TRGJ #' #' HUMAN_TRDV #' #' HUMAN_TRDD #' #' HUMAN_TRDJ #' #' MOUSE_TRBV #' #' MOUSE_TRBJ #' #' MOUSE_TRAV #' #' MOUSE_TRAJ #' #' MOUSE_IGKV #' #' MOUSE_IGKJ #' #' MOUSE_IGHV #' #' MOUSE_IGHD #' #' MOUSE_IGHJ #' #' MACMUL_TRBV #' #' MACMUL_TRBJ #' #' HUMAN_IGHV #' #' HUMAN_IGHD #' #' HUMAN_IGHJ #' #' HUMAN_IGLV #' #' HUMAN_IGLJ #' #' MOUSE_IGLJ #' #' MOUSE_IGLV #' #' @description #' Character vector with names for segments. With \code{tcR} we provided alphabets for all alpha, beta, #' gamma and delta chains gene segments. #' #' @format #' Each \code{_} is a character vector. is an identifier of species, is concatenated three #' identifiers of cell type ("TR**" for TCR, "IG**" for Ig), chain (e.g., "**A*" for alpha chains) and gene segment ("***V" for V(ariable) gene segment, #' "***J" for J(oining) gene segment, "***D" for D(iversity) gene segment). #' #' @examples #' \dontrun{ #' HUMAN_TRBV[1] # => "TRBV10-1" #' } HUMAN_TRAV <- c('TRAV1-1', 'TRAV1-2', 'TRAV10', 'TRAV11', 'TRAV12-1', 'TRAV12-2', 'TRAV12-3', 'TRAV13-1', 'TRAV13-2', 'TRAV14/DV4', 'TRAV16', 'TRAV17', 'TRAV18', 'TRAV19', 'TRAV2', 'TRAV20', 'TRAV21', 'TRAV22', 'TRAV23/DV6', 'TRAV24', 'TRAV25', 'TRAV26-1', 'TRAV26-2', 'TRAV27', 'TRAV29/DV5', 'TRAV3', 'TRAV30', 'TRAV34', 'TRAV35', 'TRAV36/DV7', 'TRAV38-1', 'TRAV38-2/DV8', 'TRAV39', 'TRAV4', 'TRAV40', 'TRAV41', 'TRAV5', 'TRAV6', 'TRAV7', 'TRAV8-1', 'TRAV8-2', 'TRAV8-3', 'TRAV8-4', 'TRAV8-6', 'TRAV8-7', 'TRAV9-1', 'TRAV9-2') HUMAN_TRAJ <- c('TRAJ10', 'TRAJ11', 'TRAJ12', 'TRAJ13', 'TRAJ14', 'TRAJ15', 'TRAJ16', 'TRAJ17', 'TRAJ18', 'TRAJ20', 'TRAJ21', 'TRAJ22', 'TRAJ23', 'TRAJ24', 'TRAJ26', 'TRAJ27', 'TRAJ28', 'TRAJ29', 'TRAJ3', 'TRAJ30', 'TRAJ31', 'TRAJ32', 'TRAJ33', 'TRAJ34', 'TRAJ36', 'TRAJ37', 'TRAJ38', 'TRAJ39', 'TRAJ4', 'TRAJ40', 'TRAJ41', 'TRAJ42', 'TRAJ43', 'TRAJ44', 'TRAJ45', 'TRAJ46', 'TRAJ47', 'TRAJ48', 'TRAJ49', 'TRAJ5', 'TRAJ50', 'TRAJ52', 'TRAJ53', 'TRAJ54', 'TRAJ56', 'TRAJ57', 'TRAJ6', 'TRAJ7', 'TRAJ8', 'TRAJ9') HUMAN_TRBV <- c('TRBV10-1', 'TRBV10-2', 'TRBV10-3', 'TRBV11-1', 'TRBV11-2', 'TRBV11-3', 'TRBV12-4', 'TRBV12-3', 'TRBV12-5', 'TRBV13', 'TRBV14', 'TRBV15', 'TRBV16', 'TRBV18', 'TRBV19', 'TRBV2', 'TRBV20-1', 'TRBV21-1', 'TRBV23-1', 'TRBV24-1', 'TRBV25-1', 'TRBV27', 'TRBV28', 'TRBV29-1', 'TRBV3-1', 'TRBV30', 'TRBV4-1', 'TRBV4-2', 'TRBV4-3', 'TRBV5-1', 'TRBV5-4', 'TRBV5-5', 'TRBV5-6', 'TRBV5-8', 'TRBV6-1', 'TRBV6-3', 'TRBV6-2', 'TRBV6-4', 'TRBV6-5', 'TRBV6-6', 'TRBV6-7', 'TRBV7-1', 'TRBV7-2', 'TRBV7-3', 'TRBV7-4', 'TRBV7-6', 'TRBV7-7', 'TRBV7-8', 'TRBV7-9', 'TRBV9') HUMAN_TRBV <- .set.attr(HUMAN_TRBV, "column", "V.gene") HUMAN_TRBD <- c('TRBD1', 'TRBD2') HUMAN_TRBJ <- c('TRBJ1-1', 'TRBJ1-2', 'TRBJ1-3', 'TRBJ1-4', 'TRBJ1-5', 'TRBJ1-6', 'TRBJ2-1', 'TRBJ2-2', 'TRBJ2-3', 'TRBJ2-4', 'TRBJ2-5', 'TRBJ2-6', 'TRBJ2-7') HUMAN_TRBV_MITCR <- c('TRBV10-1', 'TRBV10-2', 'TRBV10-3', 'TRBV11-1', 'TRBV11-2', 'TRBV11-3', 'TRBV12-4, TRBV12-3', 'TRBV12-5', 'TRBV13', 'TRBV14', 'TRBV15', 'TRBV16', 'TRBV18', 'TRBV19', 'TRBV2', 'TRBV20-1', 'TRBV21-1', 'TRBV23-1', 'TRBV24-1', 'TRBV25-1', 'TRBV27', 'TRBV28', 'TRBV29-1', 'TRBV3-1', 'TRBV30', 'TRBV4-1', 'TRBV4-2', 'TRBV4-3', 'TRBV5-1', 'TRBV5-4', 'TRBV5-5', 'TRBV5-6', 'TRBV5-8', 'TRBV6-1', 'TRBV6-3, TRBV6-2', 'TRBV6-4', 'TRBV6-5', 'TRBV6-6', 'TRBV6-7', 'TRBV7-1', 'TRBV7-2', 'TRBV7-3', 'TRBV7-4', 'TRBV7-6', 'TRBV7-7', 'TRBV7-8', 'TRBV7-9', 'TRBV9') HUMAN_TRBV_ALS <- c('TRBV10-1', 'TRBV10-2', 'TRBV10-3', 'TRBV11-1', 'TRBV11-2', 'TRBV11-3', 'TRBV12-4', 'TRBV12-3', 'TRBV12-5', 'TRBV13', 'TRBV14', 'TRBV15', 'TRBV16', 'TRBV18', 'TRBV19', 'TRBV2*01', 'TRBV20-1', 'TRBV21-1', 'TRBV23-1', 'TRBV24-1', 'TRBV25-1', 'TRBV27', 'TRBV28', 'TRBV29-1', 'TRBV3-1*01', 'TRBV30', 'TRBV4-1', 'TRBV4-2', 'TRBV4-3', 'TRBV5-1', 'TRBV5-4', 'TRBV5-5', 'TRBV5-6', 'TRBV5-8', 'TRBV6-1', 'TRBV6-3', 'TRBV6-2', 'TRBV6-4', 'TRBV6-5', 'TRBV6-6', 'TRBV6-7', 'TRBV7-1', 'TRBV7-2', 'TRBV7-3', 'TRBV7-4', 'TRBV7-6', 'TRBV7-7', 'TRBV7-8', 'TRBV7-9', 'TRBV9') HUMAN_TRBV_ALS <- .set.attr(HUMAN_TRBV_ALS, "column", "V.allele") # HUMAN_TRBV_FAM <- c() # HUMAN_TRBV_GEN <- c() # HUMAN_TRBV_ALS <- c() HUMAN_TRDD = c('TRDD1', 'TRDD2', 'TRDD3') HUMAN_TRDJ = c('TRDJ1', 'TRDJ3', 'TRDJ4', 'TRDJ2') HUMAN_TRDV = c('TRDV3', 'TRAV38-2/DV8', 'TRDV2', 'TRAV23/DV6', 'TRAV29/DV5', 'TRAV36/DV7', 'TRAV14/DV4', 'TRDV1') HUMAN_TRGJ = c('TRGJ1', 'TRGJP1', 'TRGJ2', 'TRGJP', 'TRGJP2') HUMAN_TRGV = c('TRGV5P', 'TRGV10', 'TRGV5', 'TRGV3', 'TRGVA', 'TRGV8', 'TRGV4', 'TRGV11', 'TRGV2', 'TRGV9', 'TRGV1') HUMAN_IGHD = c('IGHD4-4', 'IGHD4-23', 'IGHD2-2', 'IGHD1-7', 'IGHD3-3', 'IGHD5-18', 'IGHD4/OR15-4a', 'IGHD2-21', 'IGHD4/OR15-4b', 'IGHD1/OR15-1a', 'IGHD1-14', 'IGHD6-13', 'IGHD5-24', 'IGHD2/OR15-2a', 'IGHD5-12', 'IGHD6-19', 'IGHD4-11', 'IGHD5-5', 'IGHD1/OR15-1b', 'IGHD3/OR15-3a', 'IGHD4-17', 'IGHD6-25', 'IGHD1-1', 'IGHD3-16', 'IGHD2-8', 'IGHD3-10', 'IGHD3/OR15-3b', 'IGHD5/OR15-5a', 'IGHD3-9', 'IGHD7-27', 'IGHD1-20', 'IGHD1-26', 'IGHD3-22', 'IGHD6-6', 'IGHD2-15', 'IGHD2/OR15-2b', 'IGHD5/OR15-5b') HUMAN_IGHJ = c('IGHJ4', 'IGHJ1', 'IGHJ6', 'IGHJ3', 'IGHJ2', 'IGHJ5') HUMAN_IGHV = c('IGHV4-4', 'IGHV7-40', 'IGHV2-5', 'IGHV4-28', 'IGHV6-1', 'IGHV3/OR16-6', 'IGHV1-69D', 'IGHV1-2', 'IGHV5-51', 'IGHV2/OR16-5', 'IGHV3-30-52', 'IGHV3-53', 'IGHV3/OR16-13', 'IGHV3-74', 'IGHV1-69-2', 'IGHV3-22', 'IGHV4-34', 'IGHV1-45', 'IGHV3-35', 'IGHV3-64', 'IGHV4-61', 'IGHV3-47', 'IGHV7-34-1', 'IGHV4-59', 'IGHV3-19', 'IGHV3-15', 'IGHV4-39', 'IGHV2-70D', 'IGHV3-20', 'IGHV1-18', 'IGHV3-30-22', 'IGHV4-38-2', 'IGHV3-54', 'IGHV5-10-1', 'IGHV3-33', 'IGHV3-21', 'IGHV3-7', 'IGHV1/OR15-2', 'IGHV4-31', 'IGHV3/OR16-8', 'IGHV3-23D', 'IGHV3-30-42', 'IGHV3-62', 'IGHV3-23', 'IGHV1-8', 'IGHV3/OR16-15', 'IGHV1/OR15-1', 'IGHV3-43D', 'IGHV4-30-2', 'IGHV3-NL1', 'IGHV3-64D', 'IGHV3-13', 'IGHV3-52', 'IGHV3-11', 'IGHV3-30-33', 'IGHV1/OR15-4', 'IGHV5-78', 'IGHV4-55', 'IGHV3-16', 'IGHV3-33-2', 'IGHV7-4-1', 'IGHV2-26', 'IGHV4-30-4', 'IGHV1/OR21-1', 'IGHV1/OR15-9', 'IGHV3/OR15-7', 'IGHV3-9', 'IGHV3-30', 'IGHV3-29', 'IGHV3-38', 'IGHV2-70', 'IGHV1-NL1', 'IGHV3-30-5', 'IGHV1/OR15-5', 'IGHV1-3', 'IGHV7-81', 'IGHV3/OR16-14', 'IGHV1-68', 'IGHV3-63', 'IGHV3/OR16-10', 'IGHV4/OR15-8', 'IGHV1-58', 'IGHV3-25', 'IGHV3-69-1', 'IGHV1-38-4', 'IGHV3/OR16-16', 'IGHV1-69', 'IGHV3-32', 'IGHV1-46', 'IGHV1-24', 'IGHV3/OR16-9', 'IGHV3-38-3', 'IGHV3-30-2', 'IGHV3-48', 'IGHV1/OR15-3', 'IGHV3-30-3', 'IGHV3-73', 'IGHV3-49', 'IGHV3-66', 'IGHV3-43', 'IGHV2-10', 'IGHV3/OR16-12', 'IGHV3-71', 'IGHV3-72') HUMAN_IGKJ = c('IGKJ5', 'IGKJ4', 'IGKJ3', 'IGKJ2', 'IGKJ1') HUMAN_IGKV = c('IGKV2-30', 'IGKV1-17', 'IGKV1-5', 'IGKV1/OR1-1', 'IGKV1/OR2-11', 'IGKV2D-30', 'IGKV1-9', 'IGKV2-40', 'IGKV6D-41', 'IGKV1D-43', 'IGKV3-7', 'IGKV6D-21', 'IGKV2-29', 'IGKV1/OR2-1', 'IGKV1-13', 'IGKV3D-20', 'IGKV1-NL1', 'IGKV1D-12', 'IGKV3D-7', 'IGKV1/OR10-1', 'IGKV1/OR2-108', 'IGKV1D-16', 'IGKV1/OR2-118', 'IGKV1-39', 'IGKV1/OR2-9', 'IGKV1D-17', 'IGKV1/ORY-1', 'IGKV1/OR-4', 'IGKV1D-8', 'IGKV1D-42', 'IGKV2D-24', 'IGKV1-27', 'IGKV1/OR15-118', 'IGKV1/OR22-5', 'IGKV1/OR9-1', 'IGKV2/OR2-7D', 'IGKV2D-18', 'IGKV1/OR-3', 'IGKV1/OR9-2', 'IGKV1D-37', 'IGKV1D-39', 'IGKV2D-29', 'IGKV5-2', 'IGKV2D-28', 'IGKV3/OR2-268', 'IGKV2D-26', 'IGKV3-20', 'IGKV1-8', 'IGKV2D-40', 'IGKV6-21', 'IGKV3-11', 'IGKV3-15', 'IGKV1/OR2-3', 'IGKV1-12', 'IGKV2/OR22-4', 'IGKV1/OR2-2', 'IGKV1/OR-2', 'IGKV7-3', 'IGKV1/OR2-0', 'IGKV3D-11', 'IGKV1-16', 'IGKV1D-33', 'IGKV1-33', 'IGKV3D-15', 'IGKV1D-13', 'IGKV2-18', 'IGKV1-6', 'IGKV2-28', 'IGKV4-1', 'IGKV1-37', 'IGKV2-4', 'IGKV2-24') HUMAN_IGLJ = c('IGLJ6', 'IGLJ5', 'IGLJ3', 'IGLJ4', 'IGLJ1', 'IGLJ7', 'IGLJ2') HUMAN_IGLV = c('IGLV2-11', 'IGLV7-46', 'IGLV1-44', 'IGLV5-52', 'IGLV4-3', 'IGLV3-19', 'IGLV3-27', 'IGLV2-18', 'IGLV2-8', 'IGLV3-21', 'IGLV2-33', 'IGLV3-16', 'IGLV3-13', 'IGLV2-14', 'IGLV1-62', 'IGLV3-31', 'IGLV3-9', 'IGLV4-60', 'IGLV1-51', 'IGLV7-43', 'IGLV5-45', 'IGLV8-61', 'IGLV3-1', 'IGLV1-40', 'IGLV3-25', 'IGLV4-69', 'IGLV9-49', 'IGLV5-39', 'IGLV1-47', 'IGLV2-NL1', 'IGLV2-34', 'IGLV11-55', 'IGLV2-5', 'IGLV8/OR8-1', 'IGLV10-54', 'IGLV3-12', 'IGLV3-22', 'IGLV1-41', 'IGLV5-37', 'IGLV5-48', 'IGLV1-36', 'IGLV3-10', 'IGLV3-32', 'IGLV6-57', 'IGLV2-23', 'IGLV1-50') MOUSE_TRAV <- c('TRAV1', 'TRAV10', 'TRAV10D', 'TRAV10N', 'TRAV11', 'TRAV11D', 'TRAV11N', 'TRAV12-1', 'TRAV12-2', 'TRAV12-3', 'TRAV12D-1', 'TRAV12D-2', 'TRAV12D-3', 'TRAV12N-1', 'TRAV12N-2', 'TRAV12N-3', 'TRAV13-1', 'TRAV13-2', 'TRAV13-3', 'TRAV13-4/DV7', 'TRAV13-5', 'TRAV13D-1', 'TRAV13D-2', 'TRAV13D-3', 'TRAV13D-4', 'TRAV13N-1', 'TRAV13N-2', 'TRAV13N-3', 'TRAV13N-4', 'TRAV14-1', 'TRAV14-2', 'TRAV14-3', 'TRAV14D-1', 'TRAV14D-2', 'TRAV14D-3/DV8', 'TRAV14N-1', 'TRAV14N-2', 'TRAV14N-3', 'TRAV15-1/DV6-1', 'TRAV15-2/DV6-2', 'TRAV15D-1/DV6D-1', 'TRAV15D-2/DV6D-2', 'TRAV15N-1', 'TRAV15N-2', 'TRAV16', 'TRAV16D/DV11', 'TRAV16N', 'TRAV17', 'TRAV18', 'TRAV19', 'TRAV2', 'TRAV20', 'TRAV21/DV12', 'TRAV3-1', 'TRAV3-3', 'TRAV3-4', 'TRAV3D-3', 'TRAV3N-3', 'TRAV4-2', 'TRAV4-3', 'TRAV4-4/DV10', 'TRAV4D-2', 'TRAV4D-3', 'TRAV4D-4', 'TRAV4N-3', 'TRAV4N-4', 'TRAV5-1', 'TRAV5-2', 'TRAV5-4', 'TRAV5D-4', 'TRAV5N-4', 'TRAV6-1', 'TRAV6-2', 'TRAV6-3', 'TRAV6-4', 'TRAV6-5', 'TRAV6-6', 'TRAV6-7/DV9', 'TRAV6D-3', 'TRAV6D-4', 'TRAV6D-5', 'TRAV6D-6', 'TRAV6D-7', 'TRAV6N-5', 'TRAV6N-6', 'TRAV6N-7', 'TRAV7-1', 'TRAV7-2', 'TRAV7-3', 'TRAV7-4', 'TRAV7-5', 'TRAV7-6', 'TRAV7D-2', 'TRAV7D-3', 'TRAV7D-4', 'TRAV7D-5', 'TRAV7D-6', 'TRAV7N-4', 'TRAV7N-5', 'TRAV7N-6', 'TRAV8-1', 'TRAV8-2', 'TRAV8D-1', 'TRAV8D-2', 'TRAV8N-2', 'TRAV9-1', 'TRAV9-2', 'TRAV9-3', 'TRAV9-4', 'TRAV9D-1', 'TRAV9D-2', 'TRAV9D-3', 'TRAV9D-4', 'TRAV9N-2', 'TRAV9N-3', 'TRAV9N-4') MOUSE_TRAJ <- c('TRAJ11', 'TRAJ12', 'TRAJ13', 'TRAJ15', 'TRAJ16', 'TRAJ17', 'TRAJ18', 'TRAJ2', 'TRAJ21', 'TRAJ22', 'TRAJ23', 'TRAJ24', 'TRAJ26', 'TRAJ27', 'TRAJ28', 'TRAJ30', 'TRAJ31', 'TRAJ32', 'TRAJ33', 'TRAJ34', 'TRAJ35', 'TRAJ37', 'TRAJ38', 'TRAJ39', 'TRAJ4', 'TRAJ40', 'TRAJ42', 'TRAJ43', 'TRAJ45', 'TRAJ46', 'TRAJ48', 'TRAJ49', 'TRAJ5', 'TRAJ50', 'TRAJ52', 'TRAJ53', 'TRAJ54', 'TRAJ56', 'TRAJ57', 'TRAJ58', 'TRAJ59', 'TRAJ6', 'TRAJ7', 'TRAJ9') MOUSE_TRBV <- c('TRBV1', 'TRBV12-1', 'TRBV12-2', 'TRBV13-1', 'TRBV13-2', 'TRBV13-3', 'TRBV14', 'TRBV15', 'TRBV16', 'TRBV17', 'TRBV19', 'TRBV2', 'TRBV20', 'TRBV23', 'TRBV24', 'TRBV26', 'TRBV29', 'TRBV3', 'TRBV30', 'TRBV31', 'TRBV4', 'TRBV5') MOUSE_TRBJ <- c('TRBJ1-1', 'TRBJ1-2', 'TRBJ1-3', 'TRBJ1-4', 'TRBJ1-5', 'TRBJ2-1', 'TRBJ2-2', 'TRBJ2-3', 'TRBJ2-4', 'TRBJ2-5', 'TRBJ2-7') MOUSE_IGKV <- c('IGKV4-54','IGKV4-59','IGKV4-60','IGKV4-61','IGKV8-16','IGKV9-123', 'IGKV9-128','IGKV1-131','IGKV14-130','IGKV12-89','IGKV1-132', 'IGKV3-4','IGKV4-91','IGKV8-23-1','IGKV4-77','IGKV6-15', 'IGKV6-25','IGKV6-14','IGKV9-119','IGKV4-58','IGKV4-74', 'IGKV3-10','IGKV1-35','IGKV9-124','IGKV3-7','IGKV8-19', 'IGKV1-88','IGKV8-34','IGKV7-33','IGKV13-76','IGKV4-63', 'IGKV2-a','IGKV3-3','IGKV4-62','IGKV18-36','IGKV1-133', 'IGKV3-9','IGKV6-23','IGKV12-98','IGKV3-8','IGKV1-110', 'IGKV4-57-1','IGKV12-47','IGKV12-44','IGKV4-70','IGKV4-90', 'IGKV4-73','IGKV4-79','IGKV6-32','IGKV4-56','IGKV2-137', 'IGKV14-126','IGKV5-48','IGKV4-69','IGKV1-115','IGKV4-57', 'IGKV6-13','IGKV3-12','IGKV4-72','IGKV11-125','IGKV3-2', 'IGKV12-38','IGKV6-29','IGKV6-b','IGKV20-101-2','IGKV6-20', 'IGKV14-111','IGKV10-95','IGKV4-81','IGKV4-92','IGKV15-103', 'IGKV19-93','IGKV4-52','IGKV2-109','IGKV5-45','IGKV5-39', 'IGKV8-26','IGKV1-122','IGKV1-135','IGKV6-c','IGKV12-46', 'IGKV17-121','IGKV6-17','IGKV13-84','IGKV4-50','IGKV1-117', 'IGKV14-100','IGKV4-55','IGKV4-80','IGKV12-e','IGKV8-18', 'IGKV4-68','IGKV5-43','IGKV6-d','IGKV3-1','IGKV10-94', 'IGKV9-120','IGKV13-82','IGKV8-28','IGKV8-21','IGKV4-51', 'IGKV12-40','IGKV8-30','IGKV9-129','IGKV15-101','IGKV4-86', 'IGKV2-112','IGKV4-75','IGKV8-24','IGKV12-41','IGKV13-85', 'IGKV17/OR19-2','IGKV4-83','IGKV4-71','IGKV8-27','IGKV15-102', 'IGKV5-37','IGKV4-78','IGKV17-127','IGKV17/OR16-3','IGKV1-99', 'IGKV3-5','IGKV4-53','IGKV10-96','IGKV16-104') MOUSE_IGKJ <- c('IGKJ5','IGKJ2','IGKJ1','IGKJ4','IGKJ3') MOUSE_IGHV = c('IGHV1-69','IGHV1S96','IGHV5-17','IGHV3-1','IGHV5-6-2','IGHV1-63', 'IGHV8-13','IGHV5-6','IGHV1-28','IGHV1-47','IGHV1-18', 'IGHV1S14','IGHV1S29','IGHV1S70','IGHV1S135','IGHV14S4', 'IGHV5-12-4','IGHV1-27','IGHV3-4','IGHV5-6-3','IGHV12-2', 'IGHV8-5','IGHV1-84','IGHV1S10','IGHV1S136','IGHV8-4', 'IGHV1-85','IGHV8-2','IGHV1-14','IGHV1S32','IGHV1-31', 'IGHV1-5','IGHV1-81','IGHV1-78','IGHV13-2','IGHV7-4', 'IGHV6-4','IGHV1-79','IGHV5-9-1','IGHV3S1','IGHV1-17-1', 'IGHV2-6-1','IGHV5-6-5','IGHV1S118','IGHV2-6','IGHV1-56', 'IGHV9-3-1','IGHV1S130','IGHV1-43','IGHV1-46','IGHV2-4-1', 'IGHV5-9-5','IGHV5-9-2','IGHV1S112','IGHV1-19','IGHV10-1', 'IGHV3-2','IGHV5S4','IGHV8-7','IGHV5-6-1','IGHV5S21', 'IGHV8-12','IGHV8-10','IGHV1S124','IGHV1-72','IGHV1S82', 'IGHV1S81','IGHV8-8','IGHV12-1-1','IGHV1-26','IGHV1S22', 'IGHV3-6','IGHV1-71','IGHV12-2-1','IGHV1S65','IGHV1-58', 'IGHV1-23','IGHV1-7','IGHV7-2','IGHV1-53','IGHV1S56', 'IGHV1-37','IGHV1-24','IGHV1S31','IGHV1S83','IGHV1S20', 'IGHV4-1','IGHV3-8','IGHV13-1','IGHV8S6','IGHV1S34', 'IGHV1S35','IGHV1-59','IGHV8S9','IGHV1S47','IGHV1-62-1', 'IGHV10-3','IGHV1S111','IGHV1S21','IGHV1S87','IGHV1-48', 'IGHV5-6-6','IGHV8-8-1','IGHV1S16','IGHV1-49','IGHV1S92', 'IGHV1-20','IGHV1-70','IGHV1-42','IGHV1S100','IGHV2-6-6', 'IGHV5-6-4','IGHV10S4','IGHV1S134','IGHV1S95','IGHV5-16', 'IGHV3-7','IGHV1S26','IGHV1-54','IGHV1-67','IGHV2-6-5', 'IGHV1S52','IGHV1-51','IGHV1-66','IGHV1-12','IGHV1S61', 'IGHV1S37','IGHV14-3','IGHV1S113','IGHV2-3-1','IGHV1-83', 'IGHV1-16','IGHV1S127','IGHV6-6','IGHV1-55','IGHV1S12', 'IGHV1-25','IGHV1S11','IGHV1S41','IGHV1S53','IGHV1S74', 'IGHV5-15','IGHV1-80','IGHV1-64','IGHV1S107','IGHV1S68', 'IGHV5S12','IGHV2-5-1','IGHV2S3','IGHV5-1','IGHV4-2', 'IGHV2-6-3','IGHV1S72','IGHV2-6-8','IGHV1S84','IGHV2-7', 'IGHV1S46','IGHV3S7','IGHV12-1','IGHV6S2','IGHV5-12-1', 'IGHV9-2','IGHV9-3','IGHV11-1','IGHV1-62-2','IGHV7-3', 'IGHV1-13','IGHV1-35','IGHV12-1-2','IGHV14-4','IGHV1S19', 'IGHV1-11','IGHV1S122','IGHV1S5','IGHV1S120','IGHV2-9', 'IGHV1-22','IGHV1-19-1','IGHV1-60','IGHV1-61','IGHV1S44', 'IGHV5S24','IGHV3-5','IGHV1S126','IGHV1S101','IGHV3-3', 'IGHV9-1','IGHV1-77','IGHV2-3','IGHV5-21','IGHV1S50', 'IGHV1S33','IGHV2-2','IGHV5-12','IGHV6-7','IGHV8-6', 'IGHV12-3','IGHV14-2','IGHV14-1','IGHV5-12-2','IGHV6-5', 'IGHV1S121','IGHV1S108','IGHV8-14','IGHV9-2-1','IGHV1-4', 'IGHV10S3','IGHV8-11','IGHV5-9','IGHV1S9','IGHV16-1', 'IGHV1-87','IGHV2-9-2','IGHV1-21-1','IGHV1-36','IGHV1-39', 'IGHV1S78','IGHV1-62-3','IGHV1-76','IGHV2-6-2','IGHV2-6-4', 'IGHV9S7','IGHV1-9','IGHV9-4','IGHV1S49','IGHV1-50', 'IGHV5-9-4','IGHV1S73','IGHV6-3','IGHV2-5','IGHV1-15', 'IGHV11-2','IGHV15-2','IGHV5-9-3','IGHV2-2-1','IGHV1S45', 'IGHV2-4','IGHV1S103','IGHV1-82','IGHV5-2','IGHV5-4', 'IGHV1S36','IGHV9S8','IGHV1S17','IGHV1S28','IGHV1-8', 'IGHV7-1','IGHV1S30','IGHV1-52','IGHV1S137','IGHV2-9-1', 'IGHV1-74','IGHV8-9','IGHV2-6-7','IGHV1S110','IGHV1S132', 'IGHV1-32','IGHV1S18','IGHV5S9','IGHV1S15','IGHV1-21', 'IGHV1-34','IGHV6S4','IGHV1S55','IGHV1S75','IGHV6S3', 'IGHV1S40','IGHV2-2-2','IGHV1S51','IGHV1S67','IGHV1-75', 'IGHV8S2') MOUSE_IGHJ = c('IGHJ1','IGHJ3','IGHJ2','IGHJ4') MOUSE_IGHD = c('IGHD2-10','IGHD5-4','IGHD2-13','IGHD2-4','IGHD1-3','IGHD2-2', 'IGHD2-6','IGHD3-2','IGHD3-3','IGHD4-1','IGHD5-6', 'IGHD2-11','IGHD2-7','IGHD2-5','IGHD5-5','IGHD6-4', 'IGHD5-2','IGHD5-3','IGHD2-14','IGHD6-1','IGHD1-1', 'IGHD6-3','IGHD1-2','IGHD2-9','IGHD2-1','IGHD2-8', 'IGHD3-1','IGHD5-1','IGHD6-2','IGHD2-3','IGHD2-12') MOUSE_IGLV = c('IGLV5','IGLV2','IGLV6','IGLV4','IGLV3','IGLV7', 'IGLV1','IGLV8') MOUSE_IGLJ = c('IGLJ1','IGLJ3P','IGLJ5','IGLJ3','IGLJ2','IGLJ4') MOUSE_TRDD = c('TRDD2', 'TRDD1') MOUSE_TRDJ = c('TRDJ1', 'TRDJ2') MOUSE_TRDV = c('TRAV15D-1/DV6D-1', 'TRAV16D/DV11', 'TRAV4-4/DV10', 'TRAV21/DV12', 'TRDV1', 'TRAV15D-2/DV6D-2', 'TRAV13-4/DV7', 'TRAV6-7/DV9', 'TRDV2-1', 'TRAV15-2/DV6-2', 'TRAV14D-3/DV8', 'TRDV5', 'TRDV2-2', 'TRDV4', 'TRAV15-1/DV6-1') MOUSE_TRGJ = c('TRGJ4', 'TRGJ2', 'TRGJ1', 'TRGJ3') MOUSE_TRGV = c('TRGV4', 'TRGV6', 'TRGV5', 'TRGV3', 'TRGV7', 'TRGV1', 'TRGV2') MACMUL_TRBV <- c('TRBD1', 'TRBD2', 'TRBJ1-1', 'TRBJ1-2', 'TRBJ1-3', 'TRBJ1-4', 'TRBJ1-5', 'TRBJ1-6', 'TRBJ2-1', 'TRBJ2-2', 'TRBJ2-2P', 'TRBJ2-3', 'TRBJ2-4', 'TRBJ2-5', 'TRBJ2-6', 'TRBJ2-7', 'TRBV1-1', 'TRBV10-1', 'TRBV10-2', 'TRBV10-3', 'TRBV11-1', 'TRBV11-2', 'TRBV11-3', 'TRBV12-1', 'TRBV12-2', 'TRBV12-3', 'TRBV12-4', 'TRBV13', 'TRBV14', 'TRBV15', 'TRBV16', 'TRBV18', 'TRBV19', 'TRBV2-1', 'TRBV2-2', 'TRBV2-3', 'TRBV20-1', 'TRBV21-1', 'TRBV22-1', 'TRBV23-1', 'TRBV24-1', 'TRBV25-1', 'TRBV27', 'TRBV28', 'TRBV29-1', 'TRBV3-1', 'TRBV3-2', 'TRBV3-3', 'TRBV3-4', 'TRBV30', 'TRBV4-1', 'TRBV4-2', 'TRBV4-3', 'TRBV5-1', 'TRBV5-10', 'TRBV5-2', 'TRBV5-3', 'TRBV5-4', 'TRBV5-5', 'TRBV5-6', 'TRBV5-7', 'TRBV5-8', 'TRBV5-9', 'TRBV6-1', 'TRBV6-2', 'TRBV6-3', 'TRBV6-4', 'TRBV6-5', 'TRBV6-6', 'TRBV6-7', 'TRBV7-10', 'TRBV7-2', 'TRBV7-3', 'TRBV7-4', 'TRBV7-5', 'TRBV7-6', 'TRBV7-7', 'TRBV7-9', 'TRBV9') MACMUL_TRBJ <- c('TRBJ1-1', 'TRBJ1-2', 'TRBJ1-3', 'TRBJ1-4', 'TRBJ1-5', 'TRBJ1-6', 'TRBJ2-1', 'TRBJ2-2', 'TRBJ2-3', 'TRBJ2-4', 'TRBJ2-5', 'TRBJ2-6', 'TRBJ2-7') #' Segment data. #' #' @docType data #' #' @aliases genesegments #' #' @name segments.list #' #' @description #' \code{segments} is a list with 5 data frames with data of human alpha-beta chain segments. #' Elements names as "TRAV", "TRAJ", "TRBV", "TRVJ", "TRVD". Each data frame consists of 5 columns: #' #' - V.allelles / J.allelles / D.allelles - character column with names of V/D/J-segments. #' #' - CDR3.position - position in the full nucleotide segment sequence where CDR3 starts. #' #' - Full.nucleotide.sequence - character column with segment CDR1-2-3 sequence. #' #' - Nucleotide.sequence - character column with segment CDR3 sequences. #' #' - Nucleotide.sequence.P - character column with segment CDR3 sequences with P-insertions. #' #' @format #' \code{genesegments} is a list with data frames. #' #' @examples #' \dontrun{ #' data(genesegments) #' genesegments$Nucleotide.sequence[segments$TRBV[,1] == "TRBV10-1"] #' } NULL #' List with assembling probabilities of beta chain TCRs. #' #' @docType data #' #' @name beta.prob #' #' @aliases beta.prob #' #' @description #' \code{beta.prob} is a list with probabilities of TCR assembling taken from #' \code{Murugan et al. Statistical inference of the generation probability #' of T-cell receptors from sequence repertoires}. It's a list with the following elements: #' #' - P.V - matrix with 1 column and row names stands for V-beta segments. Each element is #' a probability of choosing corresponding V-beta segment. #' #' - P.del.D1 - matrix 17x17 with probabilities of choosing D5-D3 deletions for TRBD1. #' #' - P.del.D1 - matrix 17x17 with probabilities of choosing D5-D3 deletions for TRBD2. #' #' - P.ins.len - matrix with first columns stands for number of insertions, second and third columns filled #' with probability values of choosing corresponding number of insertions in VD- and DJ-junctions #' correspondingly. #' #' - P.ins.nucl - data frame with probability of choosing a nucleotide in the insertion on junctions with known #' previous nucleotide. First column with names of nucleotides, 2-5 columns are probabilities of choosing #' adjacent nucleotide in VD-junction, 6-9 columns are probabilities of choosing adjacent nucleotide in DJ-junction. #' #' - P.del.J - matrix with the first column "P.del.V" with number of deletions, and other columns with #' names for V-segments and with probabilities of corresponding deletions. #' #' - P.del.J - matrix with the first column "P.del.J" with number of deletions, and other columns with #' names for J-segments and with probabilities of corresponding deletions. #' #' - P.J.D - matrix with two columns ("TRBD1" and "TRBD2") and 13 rows with row names stands for #' J-beta segments. Each element is a mutual probability of choosing J-D segments. #' #' @format #' \code{beta.prob} is a list of matrices and data frames. #' #' @examples #' \dontrun{ #' # Generate 10 kmers with adjacent nucleotide probability. #' generate.kmers.prob(rep.int(10, 10), .probs=beta.prob$P.ins.nucl[,c(1, 2:5)]) #' } NULL #' Twins alpha-beta chain data #' #' @docType data #' #' @name twinsdata #' #' @aliases twa twb #' #' @description #' \code{twa.rda}, \code{twb.rda} - data frames with downsampled to the 10000 most #' abundant clonesets and 4 samples data of twins data (alpha and beta chains). #' Link: http://labcfg.ibch.ru/tcr.html #' #' @format #' \code{twa} and \code{twb} are lists of 4 data frames with 10000 row in each. #' #' @examples #' \dontrun{ #' data(twa) #' data(twb) #' } NULLtcR/R/dataproc.R0000644000176200001440000006122513667026401013136 0ustar liggesusersif(getRversion() >= "2.15.1") utils::globalVariables(c("alpha.prob", "beta.prob", "fill_vec", "fill_reads")) #' Compute the number of deletions in MiTCR data frames. #' #' @aliases get.deletions.alpha get.deletions.beta #' #' @usage #' get.deletions.alpha(.data, .Vs = segments$TRAV, .Js = segments$TRAJ) #' #' get.deletions.beta(.data, .Vs = segments$TRBV, .Js = segments$TRBJ, .Ds = segments$TRBD) #' #' @description #' Get deletions for VD, DJ, 5'D and 3'D ends and two columns with #' total deletions for VD/DJ and 5'D/3'D deletions for the given mitcr data.frame #' with 0-indexes columns. Cases, in which deletions cannot be determined, will have -1 #' in their cell. #' #' @param .data Mitcr data.frame. #' @param .Vs Table of V segments; must have 'V.segment' and 'Nucleotide.sequence' columns. #' @param .Js Table of J segments; must have 'J.segment' and 'Nucleotide.sequence' columns. #' @param .Ds Table of D segments; must have 'D.segment' and 'Nucleotide.sequence' columns. #' #' @return Mitcr data.frame with 3 (for alpha chains) or 5 (for beta chains) new columns for deletions. #' #' @details By default, \code{*.table} parameters are taken from the \code{segments} data frame which #' can be loaded to your R environment with data(segments). Data for segments has been taken from IMGT. #' #' @examples #' \dontrun{ #' data(segments) #' immdata <- get.deletions.beta(.data) #' immdata.prob <- tcr.prob.df(immdata) #' } get.deletions.alpha <- function (.data, .Vs = segments$TRAV, .Js = segments$TRAJ) { cat('Preparing data...\n') Last.V.nucleotide.position <- .data$Last.V.nucleotide.position First.J.nucleotide.position <- .data$First.J.nucleotide.position seqs <- .data$CDR3.nucleotide.sequence cat('Computing V deletions...\n') pb <- txtProgressBar(max = length(seqs), style = 3) Vs <- sapply(.data$V.gene, function (x) { V <- .split.get(x) res <- .Vs$Nucleotide.sequence[.Vs$V.alleles == V][1] if (is.na(res)) { res <- .Vs$Nucleotide.sequence[.Vs$V.alleles == paste(V, '-1', sep = '')][1] } if (is.na(res)) { res <- .Vs$Nucleotide.sequence[.Vs$V.alleles == substr(V, 1, nchar(V) - 2)][1] } add.pb(pb) res } ) V.deletions <- nchar(Vs) - Last.V.nucleotide.position - 1 close(pb) cat('Computing J deletions...\n') pb <- txtProgressBar(max = length(seqs), style = 3) Js <- sapply(.data$J.gene, function (x) { J <- .split.get(x) res <- .Js$Nucleotide.sequence[.Js$J.alleles == J][1] if (is.na(res)) { res <- .Js$Nucleotide.sequence[.Js$J.alleles == paste(J, '-1', sep = '')][1] } if (is.na(res)) { res <- .Js$Nucleotide.sequence[.Js$J.alleles == substr(J, 1, nchar(J) - 2)][1] } add.pb(pb) res } ) J.deletions <- nchar(Js) - (nchar(seqs) - First.J.nucleotide.position) cat('Aligning J segments...\n') pb <- set.pb(length(Js)) tmp <- sapply(1:length(Js), function (j) { J.seq <- Js[j] data.nchar <- nchar(seqs[j]) .data <- seqs[j] J.nchar <- nchar(J.seq) add.pb(pb) flag <- F for (i in 1:(min(data.nchar, J.nchar))) { if (substr(J.seq, J.nchar - i + 1, J.nchar - i + 1) != substr(.data, data.nchar - i + 1, data.nchar - i + 1)) { flag <- T break } } if (flag) i <- i - 1 substr(J.seq, J.nchar - i + 1, J.nchar) }) close(pb) J.deletions <- nchar(Js) - nchar(tmp) close(pb) .data$V.deletions <- V.deletions .data$J.deletions <- J.deletions .data$Total.deletions <- V.deletions + J.deletions .data$Total.deletions[V.deletions < 0 | J.deletions < 0] <- -1 .data } get.deletions.beta <- function (.data, .Vs = segments$TRBV, .Js = segments$TRBJ, .Ds = segments$TRBD) { if (has.class(.data, 'list')) { res <- lapply(1:length(.data), function (i) { cat('Processing data from', paste0('"', names(.data)[i], '"...', collapse=''), i,'/',length(.data),'\n') get.deletions.beta(.data[[i]], .Vs = .Vs, .Js = .Js, .Ds = .Ds) } ) names(res) <- names(.data) return(res) } cat('Preparing data...\n') pb <- txtProgressBar(max = 5, style = 3) Last.V.nucleotide.position <- .data$Last.V.nucleotide.position add.pb(pb) First.D.nucleotide.position <- .data$First.D.nucleotide.position add.pb(pb) Last.D.nucleotide.position <- .data$Last.D.nucleotide.position add.pb(pb) First.J.nucleotide.position <- .data$First.J.nucleotide.position add.pb(pb) seqs <- .data$CDR3.nucleotide.sequence add.pb(pb) close(pb) cat('Computing VD deletions...\n') pb <- txtProgressBar(max = length(seqs), style = 3) Vs <- sapply(.data$V.gene, function (x) { V <- .split.get(x) res <- .Vs$Nucleotide.sequence[.Vs$V.segment == V][1] if (is.na(res)) { res <- .Vs$Nucleotide.sequence[.Vs$V.segment == paste(V, '-1', sep = '')][1] } if (is.na(res)) { res <- .Vs$Nucleotide.sequence[.Vs$V.segment == substr(V, 1, nchar(V) - 2)][1] } add.pb(pb) res } ) VD.deletions <- nchar(Vs) - Last.V.nucleotide.position - 1 close(pb) cat('Computing DJ deletions...\n') pb <- txtProgressBar(max = length(seqs), style = 3) Js <- sapply(.data$J.gene, function (x) { J <- .split.get(x) res <- .Js$Nucleotide.sequence[.Js$J.segment == J][1] if (is.na(res)) { res <- .Js$Nucleotide.sequence[.Js$J.segment == paste(J, '-1', sep = '')][1] } if (is.na(res)) { res <- .Js$Nucleotide.sequence[.Js$J.segment == substr(J, 1, nchar(J) - 2)][1] } add.pb(pb) res } ) DJ.deletions <- nchar(Js) - (nchar(seqs) - First.J.nucleotide.position) close(pb) cat("Computing D'5 and D'3 deletions...\n") pb <- txtProgressBar(max = length(seqs) * 2, style=3) D.gene <- .data$D.gene D.straight <- sapply(1:length(D.gene), function (i) { add.pb(pb) substr(seqs[i], First.D.nucleotide.position[i] + 1, Last.D.nucleotide.position[i] + 1) } ) D.rev <- revcomp(D.straight) D.deletions <- sapply(1:length(D.gene), function (i) { D <- .split.get(D.gene[i]) first.index <- -1 res <- c(-1, -1) if (D != '') { D.str <- .Ds$Nucleotide.sequence[.Ds[,1] == D] # D.sub <- substr(seqs[i], # First.D.nucleotide.position[i] + 1, # Last.D.nucleotide.position[i] + 1) D.sub <- D.straight[i] first.index <- regexpr(D.sub, D.str, fixed=T, useBytes=T)[1] if (first.index != -1) { res[1] <- first.index - 1 res[2] <- nchar(D.str) - (first.index + nchar(D.sub) - 1) } else { # first.index <- regexpr(rev.comp(D.sub), D.str, fixed=T, useBytes=T)[1] first.index <- regexpr(D.rev[i], D.str, fixed=T, useBytes=T)[1] if (first.index != -1) { res[1] <- first.index - 1 res[2] <- nchar(D.str) - (first.index + nchar(D.sub) - 1) } } } add.pb(pb) res } ) close(pb) .data$VD.deletions <- VD.deletions .data$DJ.deletions <- DJ.deletions .data$D5.deletions <- D.deletions[1,] .data$D3.deletions <- D.deletions[2,] .data$Total.deletions <- VD.deletions + DJ.deletions + D.deletions[1,] + D.deletions[2,] .data$Total.deletions[VD.deletions < 0 | DJ.deletions < 0 | D.deletions[1,] < 0 | D.deletions[2,] < 0] <- -1 .data } #' Generate random nucleotide TCR sequences. #' #' @description #' Given the list of probabilities and list of segments (see "Details"), generate a artificial TCR repertoire. #' #' @param .count Number of TCR sequences to generate. #' @param .chain Either "alpha" or "beta" for alpha and beta chain respectively. #' @param .segments List of segments (see "Details"). #' @param .P.list List of probabilities (see "Details"). #' #' @details #' For the generation of a artifical TCR repertoire user need to provide two objects: the list with segments and the list with probabilities. #' List with segments is a list of 5 elements with 5 names: "TRAV", "TRAJ", "TRBV", "TRBD", "TRBJ". Each element is a data frame with following columns #' (order is matters!): "V.allelles" with names for V-segments (for TRAV and TRBV; for others is "J.allelles" or "D.allelles"), "CDR3.position" (the function doesn't use it, but you #' should provide it, fill it with zeros, for example), "Full.nucleotide.sequence" (the function doesn't use it), "Nucleotide.sequence" (function uses it for getting nucleotide #' sequences of segments) and "Nucleotide.sequence.P" (the function doesn't use it). #' #' List with probabilities is quite complicated, so just call \code{data(beta.prob)} for beta chain probabilities (alpha chain probabilities will be added soon). #' #' @return Mitcr data.frame with generated sequences. #' #' @seealso \link{genesegments} \link{beta.prob} #' #' @examples #' \dontrun{ #' # Load list of segments provided along with tcR. #' data(genesegments) #' # Load list of probabilities provided along with tcR. #' data(beta.prob) #' # Generate repertoire of beta chian with 10000 sequences. #' artif.rep <- generate.tcR(10000, 'beta') #' View(artif.rep) #' } generate.tcr <- function (.count = 1, .chain = c('beta', 'alpha'), .segments, .P.list = if (.chain[1] == 'alpha') alpha.prob else beta.prob) { # .unique = -1 cat('Preparing necessary data...\n') ############### # ALPHA CHAIN # ############### if (.chain[1] == 'alpha') { pb <- set.pb(3) .V.J.prob <- .P.list$P.V.J V.J.sample <- sample2D(.V.J.prob, .count) V.sample <- V.J.sample[,1] add.pb(pb) J.sample <- V.J.sample[,2] add.pb(pb) .del.V.prob <- .P.list$P.del.V # names(.del.V.prob)[-1] <- sapply(names(.del.V.prob)[-1], function (x) { sub('.', '-', x, fixed=T) }) V.dels <- sapply(V.sample, function (x) { sample(x=0:(nrow(.del.V.prob) - 1), size=1, prob=.del.V.prob[,x]) } ) add.pb(pb) .del.J.prob <- .P.list$P.del.J # names(.del.J.prob)[-1] <- sapply(names(.del.J.prob)[-1], function (x) { sub('.', '-', x, fixed=T) }) J.dels <- sapply(J.sample, function (x) { sample(x=0:(nrow(.del.J.prob) - 1), size=1, prob=.del.J.prob[,x]) } ) add.pb(pb) cat('Generating segments and insertions...\n') pb <- set.pb(4) .Vs <- .segments$TRAV V <- .Vs$Nucleotide.sequence[match(V.sample, .Vs[,1])] add.pb(pb) .Js <- .segments$TRAJ J <- .Js$Nucleotide.sequence[match(J.sample, .Js[,1])] add.pb(pb) .ins.len.prob <- .P.list$P.ins.len VJ.ins.lens <- sample(x=0:(nrow(.ins.len.prob) - 1), size=.count, replace=T, prob=.ins.len.prob[,2]) add.pb(pb) .ins.nucl.prob <- .P.list$P.ins.nucl VJ.ins.nucls <- generate.kmers.prob(VJ.ins.lens, .probs=.ins.nucl.prob[,c(1, 2:5)], .last.nucl = substr(V, nchar(V) - V.dels, nchar(V) - V.dels)) add.pb(pb) close(pb) cat('Finalising sequences...\n') pb <- set.pb(.count) Vns <- nchar(V) Jns <- nchar(J) seqs <- sapply(1:.count, function (i) { add.pb(pb) paste0(substr(V[i], 1, Vns[i] - V.dels[i]), VJ.ins.nucls[i], substr(J[i], J.dels[i] + 1, Jns[i]), collapse = '') } ) close(pb) cat('Generating data frame...\n') res<-data.frame(Read.count = 1, CDR3.nucleotide.sequence = seqs, CDR3.amino.acid.sequence = bunch.translate(seqs), V.gene = V.sample, J.gene = J.sample, D.gene = '', VD.insertions = -1, DJ.insertions = -1, Total.insertions = VJ.ins.lens, VJ.inserted.sequence = VJ.ins.nucls, VD.deletions = V.dels, DJ.deletions = J.dels, Total.deletions = V.dels + J.dels, stringsAsFactors=F) # if (.inframe){ # res<-cbind(rep(1,nrow(res)),res) # names(res)[1]<-"Read.count" # res<-get.outframes((res),T) # } } ############## # BETA CHAIN # ############## else { pb <- set.pb(7) .V.prob <- .P.list$P.V V.sample <- sample(x=row.names(.V.prob), size=.count, replace=T, prob=.V.prob) add.pb(pb) .D.J.prob <-.P.list$P.J.D JD.sample <- sample2D(.D.J.prob, .count=.count) add.pb(pb) .del.V.prob <- .P.list$P.del.V # names(.del.V.prob)[-1] <- sapply(names(.del.V.prob)[-1], function (x) { sub('.', '-', x, fixed=T) }) VD.dels <- sapply(V.sample, function (x) { sample(x=0:(nrow(.del.V.prob) - 1), size=1, prob=.del.V.prob[,x]) } ) add.pb(pb) .del.J.prob <- .P.list$P.del.J # names(.del.J.prob)[-1] <- sapply(names(.del.J.prob)[-1], function (x) { sub('.', '-', x, fixed=T) }) DJ.dels <- apply(JD.sample, 1, function (x) { sample(x=0:(nrow(.del.J.prob) - 1), size=1, prob=.del.J.prob[,x[1]]) } ) add.pb(pb) D1s <- JD.sample[,2] == 'TRBD1' D5.D3.dels <- matrix(rep('', times=length(JD.sample)), ncol = 2) add.pb(pb) .del.D1.prob <- .P.list$P.del.D1 row.names(.del.D1.prob) <- 0:(nrow(.del.D1.prob) - 1) D5.D3.dels[D1s,] <- sample2D(.del.D1.prob, .count=length(which(D1s))) add.pb(pb) .del.D2.prob <- .P.list$P.del.D2 row.names(.del.D2.prob) <- 0:(nrow(.del.D2.prob) - 1) D5.D3.dels[!D1s,] <- sample2D(.del.D2.prob, .count=dim(JD.sample)[1] - length(which(D1s))) add.pb(pb) cat('Generating segments and insertions...\n') pb <- set.pb(7) .Vs <- .segments$TRBV V <- .Vs$Nucleotide.sequence[match(V.sample, .Vs[,1])] add.pb(pb) .Js <- .segments$TRBJ J <- .Js$Nucleotide.sequence[match(JD.sample[,1], .Js[,1])] add.pb(pb) .Ds <- .segments$TRBD D <- .Ds$Nucleotide.sequence[match(JD.sample[,2], .Ds[,1])] add.pb(pb) .ins.len.prob <- .P.list$P.ins.len VD.ins.lens <- sample(x=0:(nrow(.ins.len.prob) - 1), size=.count, replace=T, prob=.ins.len.prob[,2]) add.pb(pb) DJ.ins.lens <- sample(x=0:(nrow(.ins.len.prob) - 1), size=.count, replace=T, prob=.ins.len.prob[,3]) add.pb(pb) # Few secs here .ins.nucl.prob <- .P.list$P.ins.nucl VD.ins.nucls <- generate.kmers.prob(VD.ins.lens, .probs=.ins.nucl.prob[,c(1, 2:5)], .last.nucl = substr(V, nchar(V) - VD.dels, nchar(V) - VD.dels)) add.pb(pb) # Few secs here DJ.ins.nucls <- generate.kmers.prob(DJ.ins.lens, .probs=.ins.nucl.prob[,c(1, 6:9)], .last.nucl = substr(D, nchar(D) - as.integer(D5.D3.dels[,2]), nchar(D) - as.integer(D5.D3.dels[,2]))) add.pb(pb) close(pb) close(pb) cat('Finalising sequences...\n') pb <- set.pb(.count) Vns <- nchar(V) Dns <- nchar(D) Jns <- nchar(J) seqs <- sapply(1:.count, function (i) { add.pb(pb) paste0(substr(V[i], 1, Vns[i] - VD.dels[i]), VD.ins.nucls[i], substr(D[i], as.integer(D5.D3.dels[i,1]) + 1, Dns[i] - as.integer(D5.D3.dels[i,2])), DJ.ins.nucls[i], substr(J[i], DJ.dels[i] + 1, Jns[i]), collapse = '') } ) seqs.sep <- sapply(1:.count, function (i) { add.pb(pb) paste(substr(V[i], 1, Vns[i] - VD.dels[i]), VD.ins.nucls[i], substr(D[i], as.integer(D5.D3.dels[i,1]) + 1, Dns[i] - as.integer(D5.D3.dels[i,2])), DJ.ins.nucls[i], substr(J[i], DJ.dels[i] + 1, Jns[i]), sep = ':') } ) close(pb) cat('Generating data frame...\n') res<-data.frame(Read.count = 1, CDR3.nucleotide.sequence = seqs, CDR3.nucleotide.sequence.sep = seqs.sep, CDR3.amino.acid.sequence = bunch.translate(seqs), V.gene = V.sample, J.gene = JD.sample[,1], D.gene = JD.sample[,2], VD.insertions = VD.ins.lens, DJ.insertions = DJ.ins.lens, Total.insertions = VD.ins.lens + DJ.ins.lens, VJ.inserted.sequence = VD.ins.nucls, DJ.inserted.sequence = DJ.ins.nucls, VD.deletions = VD.dels, DJ.deletions = DJ.dels, Total.deletions = VD.dels + DJ.dels, stringsAsFactors=F) # if (.inframe){ # res<-cbind(rep(1,nrow(res)),res) # names(res)[1]<-"Read.count" # res<-get.outframes((res),T) # } } res$Inframe <- nchar(res$CDR3.nucleotide.sequence) %% 3 == 0 cat('Generated', sum(res$Inframe), 'in-frame sequences and', nrow(res) - sum(res$Inframe), 'out-of-frame sequences.') res } #' Proportions of specifyed subsets of clones. #' #' @aliases tailbound.proportion clonal.proportion top.proportion #' #' @usage #' tailbound.proportion(.data, .bound = 2, .col = 'Read.count') #' #' top.proportion(.data, .head = 10, .col = 'Read.count') #' #' clonal.proportion(.data, .perc = 10, .col = 'Read.count') #' #' @description #' Get a specifyed subset of the given repertiure and compute which proportion in counts #' it has comparing to the overall count. #' #' \code{tailbound.proportion} - subset by the count; #' #' \code{top.proportion} - subset by rank (top N clones); #' #' \code{clonal.proportion} - subset by a summary percentage (top N clones which in sum has the given percentage). #' #' @param .data Data frame or a list with data frames. #' @param .bound Subset the \code{.data} by \code{.col} <= \code{.bound}. #' @param .head How many top values to choose - parameter to the \code{.head} function. #' @param .perc Percentage (0 - 100). #' @param .col Column's name with counts of sequences. #' #' @return For \code{tailbound.proportion} - numeric vector of percentage. #' #' For \code{top.proportion} - numeric vector of percentage for top clones. #' For \code{clonal.proportion} - vector or matrix with values for number of clones, occupied percentage and proportion of the #' chosen clones to the overall count of clones. #' @seealso \link{vis.top.proportions}, \link{prop.sample} #' #' @examples #' \dontrun{ #' # How many clones fill up approximately #' clonal.proportion(immdata, 25) # the 25% of the sum of values in 'Read.count'? #' #' # What proportion of the top-10 clones' reads #" top.proportion(immdata, 10) # to the overall number of reads? #' vis.top.proportions(immdata) # Plot this proportions. #' #' # What proportion of sequences which #' # has 'Read.count' <= 100 to the #' tailbound.proportion(immdata, 100) # overall number of reads? #' } tailbound.proportion <- function (.data, .bound = 2, .col = 'Read.count') { if (has.class(.data, 'list')) { return(sapply(.data, tailbound.proportion, .bound = .bound, .col = .col)) } sum(.data[, .col] <= .bound) / nrow(.data) } top.proportion <- function (.data, .head = 10, .col = 'Read.count') { if (has.class(.data, 'list')) { return(sapply(.data, top.proportion, .head = .head, .col = .col)) } sum(head(.data[, .col], .head)) / sum(.data[, .col]) } clonal.proportion <- function (.data, .perc = 10, .col = 'Read.count') { if (has.class(.data, 'list')) { return(t(sapply(.data, clonal.proportion, .perc = .perc, .col = .col))) } prop <- 0 n <- 0 col <- .data[, .col] col.sum <- sum(col) while (prop < col.sum * (.perc / 100)) { n <- n + 1 prop <- prop + col[n] } c(Clones = n, Percentage = 100 * signif((prop / col.sum), 3), Clonal.count.prop = n / nrow(.data)) } #' Rearrange columns with counts for clonesets. #' #' @description #' Replace Read.count with Umi.count, recompute Percentage and sort data. #' #' @param .data Data frame with columns "Umi.count" and "Read.count". #' #' @return Data frame with new "Read.count" and "Percentage" columns. barcodes.to.reads <- function (.data) { if (has.class(.data, 'list')) { return(lapply(.data, barcodes.to.reads)) } .data$Read.count <- .data$Umi.count .data$Percentage <- .data$Read.count / sum(.data$Read.count) .data[order(.data$Percentage, decreasing = T),] } #' Resample data frame using values from the column with number of clonesets. #' #' @aliases resample downsample prop.sample #' #' @description #' Resample data frame using values from the column with number of clonesets. Number of clonestes (i.e., rows of a MiTCR data frame) #' are reads (usually the "Read.count" column) or UMIs (i.e., barcodes, usually the "Umi.count" column). #' #' @usage #' resample(.data, .n = -1, .col = c("read.count", "umi.count")) #' #' downsample(.data, .n, .col = c("read.count", "umi.count")) #' #' prop.sample(.data, .perc = 50, .col = c("read.count", "umi.count")) #' #' @param .data Data frame with the column \code{.col} or list of such data frames. #' @param .n Number of values / reads / UMIs to choose. #' @param .perc Percentage (0 - 100). See "Details" for more info. #' @param .col Which column choose to represent quanitites of clonotypes. See "Details". #' #' @return Subsampled data frame. #' #' @details #' \code{resample}. Using multinomial distribution, compute the number of occurences for each cloneset, than remove zero-number clonotypes and #' return resulting data frame. Probabilities for \code{rmultinom} for each cloneset is a percentage of this cloneset in #' the \code{.col} column. It's a some sort of simulation of how clonotypes are chosen from the organisms. For now it's not working #' very well, so use \code{downsample} instead. #' #' \code{downsample}. Choose \code{.n} clones (not clonotypes!) from the input repertoires without any probabilistic simulation, but #' exactly computing each choosed clones. Its output is same as for \code{resample} (repertoires), but is more consistent and #' biologically pleasant. #' #' \code{prop.sample}. Choose the first N clonotypes which occupies \code{.perc} percents of overall UMIs / reads. #' #' @seealso \link{rmultinom}, \link{clonal.proportion} #' #' @examples #' \dontrun{ #' # Get 100K reads (not clones!). #' immdata.1.100k <- resample(immdata[[1]], 100000, .col = "read.count") #' } resample <- function (.data, .n = -1, .col = c("read.count", "umi.count")) { if (has.class(.data, 'list')) { if (length(.n) != length(.data)) { .n <- c(.n, rep.int(-1, length(.data) - length(.n))) } return(lapply(.data, resample, .n = .n, .col = .col)) } .col <- .column.choice(.col[1]) if (.n == -1) { .n <- sum(.data[, .col]) } new.bc <- rmultinom(1, .n, .data[, .col] / sum(.data[, .col])) .data[, .col] <- new.bc non.zeros <- new.bc != 0 .data <- .data[non.zeros, ] perc.col <- paste0(strsplit(.col, ".", T)[[1]][1], ".proportion") .data[[perc.col]] <- new.bc[non.zeros] / sum(new.bc) .data[order(.data[[perc.col]], decreasing = T),] } downsample <- function (.data, .n, .col = c("read.count", "umi.count")) { if (has.class(.data, 'list')) { return(lapply(.data, downsample, .n = .n, .col = .col)) } col_current <- .column.choice(.col[1]) read_vec <- .data[, col_current] read_indices <- rep(0, sum(read_vec)) cppFunction( ' NumericVector fill_vec(NumericVector read_vec, NumericVector read_indices) { int dummy = 0; for (int i = 0; i < read_vec.size(); i++) { for (int j = dummy; j < (read_vec[i] + dummy); j++) { read_indices[j] = i; } dummy = dummy + read_vec[i]; } return read_indices; } ' ) read_indices <- fill_vec(read_vec, read_indices) new_counts <- sample(read_indices, .n) new_reads <- rep(0, length(read_vec)) cppFunction( ' NumericVector fill_reads(NumericVector new_reads, NumericVector new_counts) { for (int i = 0; i < new_counts.size(); i++) { new_reads[new_counts[i]] = new_reads[new_counts[i]] + 1; } return new_reads; } ' ) .data[, col_current] <- fill_reads(new_reads, new_counts) subset(.data, .data[, col_current] > 0) } prop.sample <- function (.data, .perc = 50, .col = c("read.count", "umi.count")) { if (has.class(.data, "data.frame")) { .data = list(Data = .data) } .col = .column.choice(.col[1]) res = lapply(.data, function (df) { df[1:(clonal.proportion(df, .perc, .col)[1]), ] }) if (length(res) == 1) { res[[1]] } else { res } } #' Set new columns "Rank" and "Index". #' #' @aliases set.rank set.index #' #' @description #' Set new columns "Rank" and "Index": #' #' set.rank <==> .data$Rank = rank(.data[, .col], ties.method = 'average') #' #' set.index <==> .data$Index = 1:nrow(.data) in a sorted data frame by \code{.col} #' #' @param .data Data frame or list with data frames. #' @param .col Character vector with name of the column to use for ranking or indexing. #' #' @return Data frame with new column "Rank" or "Index" or list with such data frames. set.rank <- function (.data, .col = "Read.count") { if (has.class(.data, 'list')) { lapply(.data, set.rank, .col = .col) } else { y <- 1 / .data[[.col]] .data$Rank <- rank(y, ties.method = 'average') .data } } set.index <- function (.data, .col = "Read.count") { if (has.class(.data, 'list')) { return(lapply(.data, set.index)) } .data <- .data[order(.data[, .col], decreasing = T), ] .data$Index <- 1:nrow(.data) .data }tcR/R/mitcr.R0000644000176200001440000000457313667026401012462 0ustar liggesusers#' Start MiTCR directly from the package. #' #' @description #' Start the MiTCR tools directly from the package with given settings. #' #' @param .input,.output Input and output files. #' @param .file.path Path prepending to \code{.input} and \code{.output}. #' If input and output is empty, but .file.path is specified, #' than process all files from the folder \code{.file.path} #' @param ... Specify input and output files and arguments #' of the MITCR without first '-' to run it. #' @param .mitcr.path Path to MiTCR .jar file. #' @param .mem Volume of memory available to MiTCR. #' #' @details #' Don't use spaces in paths! #' You should have insalled JDK 1.7 to make it works. #' #' @examples #' \dontrun{ #' # Equal to #' # java -Xmx8g -jar ~/programs/mitcr.jar -pset flex #' # -level 2 ~/data/raw/TwA1_B.fastq.gz ~/data/mitcr/TwA1_B.txt #' startmitcr('raw/TwA1_B.fastq.gz', 'mitcr/TwA1_B.txt', .file.path = '~/data/', #' pset = 'flex', level = 1, 'debug', .mitcr.path = '~/programs/', .mem = '8g') #' } startmitcr <- function (.input = '', .output = '', ..., .file.path = '', .mitcr.path = '~/programs/', .mem = '4g') { if (.input == '') { startmitcr(list.files(.file.path), paste0('/mitcr/', list.files(.file.path), '.txt'), ..., .file.path = .file.path, .mitcr.path = .mitcr.path, .mem = .mem) } else if (length(.input) > 1) { for (i in 1:length(.input)) { startmitcr(.input[i], .output[i], ..., .file.path = .file.path, .mitcr.path = .mitcr.path, .mem = .mem) } } else { if (.file.path[length(.file.path)] != '/' && .file.path != '') { .file.path <- paste(.file.path, '/', sep = '') } .input <- paste(.file.path, .input, sep = '') .output <- paste(.file.path, .output, sep = '') args <- list(...) args.names <- names(args) if (is.null(names(args))) { args.names <- rep('', length(args)) } args.str <- '' for (i in 1:length(args)) { if (args.names[i] == '') { args.str <- paste(args.str, paste('-', args[[i]], sep = '')) } else { args.str <- paste(args.str, paste('-', args.names[i], sep = ''), args[[i]]) } } args.str <- paste(args.str, .input, .output) system(paste('java', paste('-Xmx', .mem, sep = ''), '-jar', paste(.mitcr.path, 'mitcr.jar', sep = ''), args.str)) } }tcR/R/measures.R0000644000176200001440000002357313667026401013171 0ustar liggesusers########## Evaluation of distributions, vectors and sets ########## #' Information measures. #' #' @aliases entropy js.div kl.div #' #' @description #' Functions for information measures of and between distributions of values. #' #' Warning! #' Functions will check if \code{.data} if a distribution of random variable (sum == 1) or not. #' To force normalisation and / or to prevent this, set \code{.do.norm} to TRUE (do normalisation) #' or FALSE (don't do normalisation). For \code{js.div} and \code{kl.div} vectors of values must have #' equal length. #' #' Functions: #' #' - The Shannon entropy quantifies the uncertainty (entropy or degree of surprise) #' associated with this prediction. #' #' - Kullback-Leibler divergence (information gain, information divergence, #' relative entropy, KLIC) is a non-symmetric measure of the difference between #' two probability distributions P and Q (measure of information lost when Q is used to #' approximate P). #' #' - Jensen-Shannon divergence is a symmetric version of KLIC. Square root of this #' is a metric often referred to as Jensen-Shannon distance. #' #' @usage #' entropy(.data, .norm = F, .do.norm = NA, .laplace = 1e-12) #' #' kl.div(.alpha, .beta, .do.norm = NA, .laplace = 1e-12) #' #' js.div(.alpha, .beta, .do.norm = NA, .laplace = 1e-12, .norm.entropy = F) #' #' @param .data,.alpha,.beta Vector of values. #' @param .norm if T then compute normalised entropy (H / Hmax). #' @param .do.norm One of the three values - NA, T or F. If NA than check for distrubution \code{(sum(.data) == 1)}. #' and normalise if needed with the given laplace correction value. if T then do normalisation and laplace #' correction. If F than don't do normalisaton and laplace correction. #' @param .laplace Value for Laplace correction which will be added to every value in the .data. #' @param .norm.entropy if T then normalise JS-divergence by entropy. #' #' @return Shannon entropy, Jensen-Shannon divergence or Kullback-Leibler divergence values. #' #' @seealso \link{similarity}, \link{diversity} entropy <- function (.data, .norm = F, .do.norm = NA, .laplace = 1e-12) { .data <- check.distribution(.data, .do.norm, .laplace, .warn.zero = T) res <- - sum(.data * log2(.data)) if (.norm) { res / log2(length(.data)) } else { res } } kl.div <- function (.alpha, .beta, .do.norm = NA, .laplace = 1e-12) { .alpha <- check.distribution(.alpha, .do.norm, .laplace, .warn.zero = T) .beta <- check.distribution(.beta, .do.norm, .laplace, .warn.zero = T) sum(log2(.alpha / .beta) * .alpha) } js.div <- function (.alpha, .beta, .do.norm = NA, .laplace = 1e-12, .norm.entropy = F) { .alpha <- check.distribution(.alpha, .do.norm, .laplace, .warn.zero = T) .beta <- check.distribution(.beta, .do.norm, .laplace, .warn.zero = T) nrm = if (.norm.entropy) 0.5 * (entropy(.alpha, F) + entropy(.beta, F)) else 1 M <- (.alpha + .beta) / 2 0.5 * (kl.div(.alpha, M, F) + kl.div(.beta, M, F)) / nrm } #' Log-likelihood. #' #' @description #' Compute the log-likelihood of the given distribution or vector of counts. #' #' @param .data Vector for distribution or counts. #' @param .base Logarightm's base for the loglikelihood. #' @param .do.norm Parameter to the \code{check.distribution} function. #' @param .laplace Laplace correction, Parameter to the \code{check.distribution} function. #' #' @return Loglikelihood value. loglikelihood <- function (.data, .base = 2, .do.norm = NA, .laplace = 0.000000000001) { .data <- check.distribution(.data, .do.norm, .laplace) sum(log(.data, .base)) } #' Set and vector similarity measures. #' #' @aliases similarity cosine.similarity tversky.index overlap.coef morisitas.index jaccard.index horn.index #' #' @description #' Functions for computing similarity between two vectors or sets. See "Details" for exact formulas. #' #' - Cosine similarity is a measure of similarity between two vectors of an inner product space that measures the cosine of the angle between them. #' #' - Tversky index is an asymmetric similarity measure on sets that compares a variant to a prototype. #' #' - Overlap cofficient is a similarity measure related to the Jaccard index that measures the overlap between two sets, and is defined as the size of the intersection divided by the smaller of the size of the two sets. #' #' - Jaccard index is a statistic used for comparing the similarity and diversity of sample sets. #' #' - Morisita's overlap index is a statistical measure of dispersion of individuals in a population. It is used to compare overlap among samples (Morisita 1959). This formula is based on the assumption that increasing the size of the samples will increase the diversity because it will include different habitats (i.e. different faunas). #' #' - Horn's overlap index based on Shannon's entropy. #' #' Use the \link{repOverlap} function for computing similarities of clonesets. #' #' @usage #' cosine.similarity(.alpha, .beta, .do.norm = NA, .laplace = 0) #' #' tversky.index(x, y, .a = 0.5, .b = 0.5) #' #' overlap.coef(.alpha, .beta) #' #' jaccard.index(.alpha, .beta, .intersection.number = NA) #' #' morisitas.index(.alpha, .beta, .do.unique = T) #' #' horn.index(.alpha, .beta, .do.unique = T) #' #' @param .alpha,.beta,x,y Vector of numeric values for cosine similarity, vector of any values #' (like characters) for \code{tversky.index} and \code{overlap.coef}, matrix or data.frame with 2 columns for \code{morisitas.index} and \code{horn.index}, #' either two sets or two numbers of elements in sets for \code{jaccard.index}. #' @param .a,.b Alpha and beta parameters for Tversky Index. Default values gives the Jaccard index measure. #' @param .do.norm One of the three values - NA, T or F. If NA than check for distrubution (sum(.data) == 1) #' and normalise if needed with the given laplace correction value. if T then do normalisation and laplace #' correction. If F than don't do normalisaton and laplace correction. #' @param .laplace Value for Laplace correction. #' @param .do.unique if T then call unique on the first columns of the given data.frame or matrix. #' @param .intersection.number Number of intersected elements between two sets. See "Details" for more information. #' @details #' For \code{morisitas.index} input data are matrices or data.frames with two columns: first column is #' elements (species or individuals), second is a number of elements (species or individuals) in a population. #' #' Formulas: #' #' Cosine similarity: \code{cos(a, b) = a * b / (||a|| * ||b||)} #' #' Tversky index: \code{S(X, Y) = |X and Y| / (|X and Y| + a*|X - Y| + b*|Y - X|)} #' #' Overlap coefficient: \code{overlap(X, Y) = |X and Y| / min(|X|, |Y|)} #' #' Jaccard index: \code{J(A, B) = |A and B| / |A U B|} #' For Jaccard index user can provide |A and B| in \code{.intersection.number} otherwise it will be computed #' using \code{base::intersect} function. In this case \code{.alpha} and \code{.beta} expected to be vectors of elements. #' If \code{.intersection.number} is provided than \code{.alpha} and \code{.beta} are exptected to be numbers of elements. #' #' Formula for Morisita's overlap index is quite complicated and can't be easily shown here, so just look at this webpage: http://en.wikipedia.org/wiki/Morisita%27s_overlap_index #' #' #' @return Value of similarity between the given sets or vectors. #' #' @seealso \link{repOverlap}, \link{intersectClonesets}, \link{entropy}, \link{diversity} #' #' @examples #' \dontrun{ #' jaccard.index(1:10, 2:20) #' a <- length(unique(immdata[[1]][, c('CDR3.amino.acid.sequence', 'V.gene')])) #' b <- length(unique(immdata[[2]][, c('CDR3.amino.acid.sequence', 'V.gene')])) #' # Next #' jaccard.index(a, b, repOverlap(immdata[1:2], .seq = 'aa', .vgene = T)) #' # is equal to #' repOverlap(immdata[1:2], 'jaccard', seq = 'aa', .vgene = T) #' } cosine.similarity <- function (.alpha, .beta, .do.norm = NA, .laplace = 0) { .alpha <- check.distribution(.alpha, .do.norm, .laplace) .beta <- check.distribution(.beta, .do.norm, .laplace) sum(.alpha * .beta) / (sum(.alpha ^ 2) * sum(.beta ^ 2)) } tversky.index <- function (x, y, .a = 0.5, .b = 0.5) { XiY <- length(intersect(x, y)) XiY / (XiY + .a * length(setdiff(x, y)) + .b * length(setdiff(y, x))) } overlap.coef <- function (.alpha, .beta) { length(intersect(.alpha, .beta)) / min(length(.alpha), length(.beta)) } jaccard.index <- function (.alpha, .beta, .intersection.number = NA) { if (is.na(.intersection.number)) { abin <- length(intersect(.alpha, .beta)) abin / (length(unique(.alpha)) + length(unique(.beta)) - abin) } else { .intersection.number / (.alpha + .beta - .intersection.number) } } morisitas.index <- function (.alpha, .beta, .do.unique = T) { colnames(.alpha) <- c('Species', 'Count') colnames(.beta) <- c('Species', 'Count') if (.do.unique) { .alpha <- .alpha[!duplicated(.alpha[,1]),] .beta <- .beta[!duplicated(.beta[,1]),] } .alpha[,2] <- as.numeric(.alpha[,2]) .beta[,2] <- as.numeric(.beta[,2]) merged <- merge(.alpha, .beta, by = 'Species', all = T) merged[is.na(merged)] <- 0 sum.alpha <- sum(.alpha[,2]) sum.beta <- sum(.beta[,2]) 2 * sum(merged[,2] * merged[,3] / sum.alpha) / sum.beta / ( (sum((.alpha[,2] / sum.alpha)^2) + sum((.beta[,2] / sum.beta)^2))) } horn.index <- function (.alpha, .beta, .do.unique = T) { .alpha[,1] <- as.character(.alpha[,1]) .beta[,1] <- as.character(.beta[,1]) colnames(.alpha) <- c('Species', 'Count') colnames(.beta) <- c('Species', 'Count') if (.do.unique) { .alpha <- .alpha[!duplicated(.alpha[,1]),] .beta <- .beta[!duplicated(.beta[,1]),] } .alpha[,2] <- as.numeric(.alpha[,2]) / sum(.alpha[,2]) .beta[,2] <- as.numeric(.beta[,2]) / sum(.beta[,2]) merged <- merge(.alpha, .beta, by = 'Species', all = T) merged[is.na(merged)] <- 0 rel.12 <- merged[,2] / merged[,3] rel.12[merged[,3] == 0] <- 0 rel.21 <- merged[,3] / merged[,2] rel.21[merged[,2] == 0] <- 0 1 / log(2) * sum(merged[,2] / 2 * log(1 + rel.21) + merged[,3] / 2 * log(1 + rel.12)) }tcR/R/diversity.R0000644000176200001440000002502313667026401013357 0ustar liggesusers#' Distribution evaluation. #' #' @aliases inverse.simpson diversity gini chao1 gini.simpson #' #' @description #' Functions for evaluating the diversity of species or objects in the given distribution. #' See the \code{repOverlap} function for working with clonesets and a general interface to #' all of this functions. #' #' Warning! #' Functions will check if .data is a distribution of a random variable (sum == 1) or not. #' To force normalisation and / or to prevent this, set .do.norm to TRUE (do normalisation) #' or FALSE (don't do normalisation), respectively. #' #' - True diversity, or the effective number of types, refers to the number #' of equally-abundant types needed for the average proportional abundance #' of the types to equal that observed in the dataset of interest #' where all types may not be equally abundant. #' #' - Inverse Simpson index is the effective number of types that is obtained when #' the weighted arithmetic mean is used to quantify average #' proportional abundance of types in the dataset of interest. #' #' - The Gini coefficient measures the inequality among values #' of a frequency distribution (for example levels of income). A Gini coefficient of zero #' expresses perfect equality, where all values are the same (for example, where everyone #' has the same income). A Gini coefficient of one (or 100 percents ) expresses maximal inequality #' among values (for example where only one person has all the income). #' #' - The Gini-Simpson index is the probability of interspecific encounter, i.e., probability that two entities #' represent different types. #' #' - Chao1 estimator is a nonparameteric asymptotic estimator of species richness (number of species in a population). #' #' @usage #' inverse.simpson(.data, .do.norm = NA, .laplace = 0) #' #' diversity(.data, .q = 5, .do.norm = NA, .laplace = 0) #' #' gini(.data, .do.norm = NA, .laplace = 0) #' #' gini.simpson(.data, .do.norm = NA, .laplace = 0) #' #' chao1(.data) #' #' @param .data Numeric vector of values for proportions or for numbers of individuals. #' @param .q q-parameter for the Diversity index. #' @param .do.norm One of the three values - NA, T or F. If NA than check for distrubution (sum(.data) == 1) #' and normalise if needed with the given laplace correction value. if T then do normalisation and laplace #' correction. If F than don't do normalisaton and laplace correction. #' @param .laplace Value for Laplace correction which will be added to every value in the .data. #' #' @return Numeric vector of length 1 with value for all functions except \code{chao1}, which returns 4 values: #' estimated number of species, standart deviation of this number and two 95% confidence intervals for the species number. #' #' @seealso \link{repOverlap}, \link{entropy}, \link{similarity} #' #' @examples #' data(twb) #' # Next two are equal calls: #' stopifnot(gini(twb[[1]]$Read.count, TRUE, 0) - 0.7609971 < 1e-07) #' stopifnot(gini(twb[[1]]$Read.proportion, FALSE) - 0.7609971 < 1e-07) #' stopifnot(chao1(twb[[1]]$Read.count)[1] == 1e+04) inverse.simpson <- function (.data, .do.norm = NA, .laplace = 0) { .data <- check.distribution(.data, .do.norm, .laplace) 1 / sum(.data ^ 2) } diversity <- function (.data, .q = 5, .do.norm = NA, .laplace = 0) { .data <- check.distribution(.data, .do.norm, .laplace) if (.q == 0) { length(.data) } else if (.q == 1) { 1 / prod(.data ^ .data) } else if (.q > 1) { 1 / (sum(.data ^ .q) ^ (1 / (.q - 1))) } else { NA } } gini <- function (.data, .do.norm = NA, .laplace = 0) { .data <- sort(check.distribution(.data, .do.norm, .laplace, .warn.sum = F)) n <- length(.data) 1 / n * (n + 1 - 2 * sum((n + 1 - 1:n) * .data) / sum(.data)) } gini.simpson <- function (.data, .do.norm = NA, .laplace = 0) { 1 - 1 / inverse.simpson(.data, .do.norm, .laplace) } chao1 <- function (.data) { counts <- table(.data) e <- NA v <- NA lo <- NA hi <- NA n <- sum(.data) D <- length(.data) f1 <- counts['1'] f2 <- counts['2'] # f1 == 0 && f2 == 0 if (is.na(f1) && is.na(f2)) { e <- D i <- 1:max(.data) i <- i[unique(.data)] v <- sum(sapply(i, function(i) sum(.data == i) * (exp(-i) - exp(-2 * i)))) - (sum(sapply(i, function(i) i * exp(-i) * sum(.data == i))))^2/n P <- sum(sapply(i, function(i) sum(.data == i) * exp(-i)/D)) lo <- max(D, D/(1 - P) - qnorm(1 - .05/2) * sqrt(v)/(1 - P)) hi <- D/(1 - P) + qnorm(1 - .05/2) * sqrt(v)/(1 - P) } # f1 != 0 && f2 == 0 else if (is.na(f2)) { e <- D + f1 * (f1 - 1) / 2 * (n - 1) / n v <- (n-1)/n * f1 * (f1 - 1) /2 + ((n -1)/n)^2 * f1 * (2 * f1 - 1)^2/4 - ((n-1)/n)^2*f1^4/4/e t <- e - D K <- exp(qnorm(1 - .05/2) * sqrt(log(1 + v/t^2))) lo <- D + t/K hi <- D + t*K } # f1 != && f2 != 0 else { const <- (n - 1) / n e <- D + f1^2 / (2 * f2) * const f12 <- f1 / f2 v <- f2 * (const * f12^2 / 2 + const^2 * f12^3 + const^2 * f12^4 / 4) t <- e - D K <- exp(qnorm(.975) * sqrt(log(1 + v/t^2))) lo <- D + t/K hi <- D + t*K } c(Estimator = e, SD = sqrt(v), Conf.95.lo = lo, Conf.95.hi = hi) } # hill.numbers <- function (.data, .max.q = 6, .min.q = 1) { # print(head(.data)) # print(sum(.data)) # # .data <- check.distribution(.data) # print(head(.data)) # # if (.min.q < 0) { .min.q <- 0 } # res <- c() # # if (.min.q == 0) { # res <- length(.data) # .min.q <- 1 # } # # if (.min.q == 1) { # res <- c(res, exp(-sum(.data * log(.data)))) # .min.q <- 2 # } # # if (.max.q >= 2) { # for (q in .min.q:.max.q) { # res <- c(res, sum(.data ^ q)^(1 / (1 - q))) # } # } # # res # } #' Diversity evaluation using rarefaction. #' #' @description #' Sequentially resample the given data with growing sample size the given data and compute mean number of unique clones. #' For more details on the procedure see "Details". #' #' @param .data Data frame or a list with data frames. #' @param .step Step's size. By default - minimal repertoire size divided by 50. #' @param .quantile Numeric vector of length 2 with quantiles for confidence intervals. #' @param .extrapolation If N > 0 than perform extrapolation of all samples to the size of the max one +N reads or UMIs. By default - 200000. #' @param .col Column's name from which choose frequency of each clone. #' @param .verbose if T then print progress bar. #' #' @return #' Data frame with first column for sizes, second columns for the first quantile, #' third column for the mean, fourth columns for the second quantile, fifth columns #' for the name of subject. #' #' @details #' This subroutine is designed for diversity evaluation of repertoires. On each step it computes a #' mean unique clones from sample of fixed size using bootstrapping. Unique clones for each sample from bootstrap computed #' as a number of non-zero elements in a vector from multinomial distribution with input vector of probabilities from the \code{.col} column #' using function \code{rmultinom} with parameters n = .n, size = i * .step, prob = .data[, .col] (i is an index of current iteration) #' and choosing for lower and upper bound \code{quantile} bounds of the computed distribution of unique clones. #' #' @seealso \link{vis.rarefaction} \link{rmultinom} #' #' @examples #' \dontrun{ #' rarefaction(immdata, .col = "Read.count") #' } rarefaction <- function (.data, .step = NA, .quantile = c(.025, .975), .extrapolation = 200000, .col = 'Umi.count', .verbose = T) { if (has.class(.data, 'data.frame')) { .data <- list(Sample = .data) } if (is.na(.step)) { .step = min(sapply(.data, function (x) sum(x[[.col]]))) / 50. } # multinom # .alpha <- function (n, Xi, m) { # k <- Xi # if (k <= n - m) { # prod((n - k):(n - m - k + 1) / n:(n - m + 1)) # } else { # 0 # } # } # poisson .alpha <- function (n, Xi, m) { k <- Xi return((1 - m / n)^Xi) } if (.verbose) { pb <- set.pb(sum(sapply(1:length(.data), function (i) { bc.vec <- .data[[i]][, .col] bc.sum <- sum(.data[[i]][, .col]) sizes <- seq(.step, bc.sum, .step) if (sizes[length(sizes)] != bc.sum) { sizes <- c(sizes, bc.sum) } length(sizes) } ))) } muc.list <- lapply(1:length(.data), function (i) { Sobs <- nrow(.data[[i]]) bc.vec <- .data[[i]][, .col] Sest <- chao1(bc.vec) n <- sum(bc.vec) sizes <- seq(.step, n, .step) if (sizes[length(sizes)] != n) { sizes <- c(sizes, n) } counts <- table(bc.vec) muc.res <- t(sapply(sizes, function (sz) { freqs <- as.numeric(names(counts)) # multinom alphas <- sapply(freqs, function (k) .alpha(n, k, sz)) # Sind <- Sobs - sum(sapply(freqs, function (k) .alpha(n, k, sz) * counts[as.character(freqs)])) # SD <- sqrt(sum(sapply(freqs, function (k) (1 - .alpha(n, k, sz))^2 * counts[as.character(freqs)])) - Sind^2/Sest[1]) # poisson Sind <- sum(sapply(1:length(freqs), function (k) (1 - alphas[k]) * counts[k])) if (Sest[1] == Sobs) { SD <- 0 } else { SD <- sqrt(sum(sapply(1:length(freqs), function (k) (1 - alphas[k])^2 * counts[k])) - Sind^2/Sest[1]) } t <- Sind - Sobs K <- exp(qnorm(.975) * sqrt(log(1 + (SD / t)^2))) lo <- Sobs + t*K hi <- Sobs + t/K res <- c(sz, Sind, Sind, Sind) names(res) <- c('Size', paste0('Q', .quantile[1]), 'Mean', paste0('Q', .quantile[2])) if (.verbose) add.pb(pb) res })) if (.extrapolation > 0) { sizes <- seq(sum(.data[[i]][, .col]), .extrapolation + max(sapply(.data, function (x) sum(x[, .col]))), .step) if (length(sizes) != 1) { ex.res <- t(sapply(sizes, function (sz) { f0 <- Sest[1] - Sobs f1 <- counts['1'] if (is.na(f1) || f0 == 0) { Sind <- Sobs } else { Sind <- Sobs + f0 * (1 - exp(-(sz - n)/n * f1 / f0)) } res <- c(sz, Sind, Sind, Sind) names(res) <- c('Size', paste0('Q', .quantile[1]), 'Mean', paste0('Q', .quantile[2])) if (.verbose) add.pb(pb) res })) df1 <- data.frame(muc.res, People = names(.data)[i], Type = 'interpolation', stringsAsFactors = F) df2 <- data.frame(ex.res, People = names(.data)[i], Type = 'extrapolation', stringsAsFactors = F) rbind(df1, df2) } else { df1 <- data.frame(muc.res, People = names(.data)[i], Type = 'interpolation', stringsAsFactors = F) } } else { data.frame(muc.res, People = names(.data)[i], stringsAsFactors = F) } }) if (.verbose) close(pb) do.call(rbind, muc.list) }tcR/R/segments.R0000644000176200001440000002111113667033701013155 0ustar liggesusers########## Statistics and analysis of Variable and Joining genes usage ########## if (getRversion() >= "2.15.1") { utils::globalVariables(c('PC1', 'PC2', "Subject", "n")) } #' Gene usage. #' #' @aliases geneUsage #' #' @description #' Compute frequencies or counts of gene segments ("V / J - usage"). #' #' @param .data Cloneset data frame or a list with clonesets. #' @param .genes Either one of the gene alphabet (e.g., HUMAN_TRBV, \link{genealphabets}) or list with two gene alphabets for computing #' joint distribution. #' @param .quant Which column to use for the quantity of clonotypes: NA for computing only number of genes without using clonotype counts, #' "read.count" for the "Read.count" column, "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, #' "umi.prop" for the "Umi.proportion" column. #' @param .norm If T then return proportions of resulting counting of genes. #' @param .ambig If F than remove from counting genes which are not presented in the given gene alphabet(s). #' #' @return #' If \code{.data} is a cloneset and \code{.genes} is NOT a list than return a data frame with first column "Gene" with genes and second with counts / proportions. #' #' If \code{.data} is a list with clonesets and \code{.genes} is NOT a list than return a data frame with first column "Gene" #' with genes and other columns with counts / proportions for each cloneset in the input list. #' #' If \code{.data} is a cloneset and \code{.genes} IS a list than return a matrix with gene segments for the first gene in \code{.genes} #' and column names for the second gene in \code{.genes}. See "Examples". #' #' If \code{.data} is a list with clonesets and \code{.genes} IS a list than return a list with matrices like in the previous case. #' #' @seealso \code{\link{genealphabets}}, \code{\link{vis.gene.usage}}, \code{\link{pca.segments}} #' #' @examples #' \dontrun{ #' # Load your data #' data(twb) #' # compute V-segments frequencies of human TCR beta. #' seg <- geneUsage(twb, HUMAN_TRBV, .norm = T) #' # plot V-segments frequencies as a heatmap #' vis.heatmap(seg, .labs = c("Sample", "V gene")) #' # plot V-segments frequencies directly from clonesets #' vis.gene.usage(twb, HUMAN_TRBV) #' # plot V-segments frequencies from the gene frequencies #' vis.gene.usage(seg, NA) #' # Compute V-J joint usage. #' geneUsage(twb, list(HUMAN_TRBV, HUMAN_TRBJ)) #' # for future: #' # geneUsage(twb, "human", "trbv") #' } geneUsage <- function (.data, .genes = HUMAN_TRBV_MITCR, .quant = c(NA, "read.count", "umi.count", "read.prop", "umi.prop"), .norm = F, .ambig = F #, .species = c("human", "mouse"), .genes = c("trbv", "trbd", "trbj") ) { message(" ======================================== !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !! The tcR package WILL SOON BE ORPHANED !! AND REMOVED FROM CRAN. !! !! A new package is available that is !! designed to replace tcR: !! immunarch -- https://immunarch.com/ !! !! We will be happy to help you to move !! to the new package. Feel free to contact us: !! http://github.com/immunomind/immunarch !! !! Sincerely, !! immunarch dev team and !! Vadim I. Nazarov, lead developer of tcR !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! =======================================") .process.df <- function (.df, .quant, .cols) { cast.fun <- dcast if (length(.cols) == 2) { cast.fun <- acast; len <- 2 } for (i in 1:length(.cols)) { .df[ which(!(.df[[.cols[i]]] %in% .genes[[i]])), .cols[i] ] <- "Ambiguous" } if (length(.cols) == 1) { .cols <- c(.cols, '.'); len <- 1} .df = .df %>% select(na.exclude(c(.quant, .cols[1:len]))) .df = .df %>% grouped_df(.cols[1:len]) if (!is.na(.quant)) { .df = .df %>% summarise(Freq = sum(.quant)) } else { .df = .df %>% summarise(Freq = n()) } .df } .fix.ambig <- function (.res, .ambig) { if (length(.genes) == 2) { .res <- lapply(.res, function (x) { x[row.names(x) != "Ambiguous", ][, colnames(x) != "Ambiguous"] }) if (length(.data) == 1) { .res <- .res[[1]] } .res } else { .res[.res[,1] != "Ambiguous", ] } } quant <- NA if (!is.na(.quant[1])) { quant <- .column.choice(.quant, T) } if (has.class(.data, 'data.frame')) { .data <- list(Sample = .data) } if (has.class(.genes, 'list')) { genecols <- c(paste0(substr(.genes[[1]][1], 4, 4), ".gene"), paste0(substr(.genes[[2]][1], 4, 4), ".gene")) } else { genecols <- paste0(substr(.genes[1], 4, 4), ".gene") .genes <- list(.genes) } tbls <- lapply(.data, .process.df, .quant = quant, .cols = genecols) # JOINT GENE DISTRIBUTION if (length(.genes) == 2) { tbls <- lapply(tbls, function (x) { genrows <- .genes[[1]][is.na(match(.genes[[1]], row.names(x)))] gencols <- .genes[[2]][is.na(match(.genes[[2]], colnames(x)))] if (length(genrows) > 0) { x = rbind(x, matrix(0, ncol = ncol(x), nrow = length(genrows))) row.names(x)[(nrow(x) - length(genrows) + 1):nrow(x)] <- genrows } if (length(gencols) > 0) { x = cbind(x, matrix(0, nrow = nrow(x), ncol = length(gencols))) colnames(x)[(ncol(x) - length(gencols) + 1):ncol(x)] <- gencols } x[is.na(x)] <- 0 # x = x[order(.genes[[1]]), ][, order(.genes[[2]])] # View(x, "y") x }) if (.norm) { tbls <- lapply(tbls, function (x) x / sum(x)) } return(.fix.ambig(tbls, .ambig)) } # SINGLE GENE DISTRIBUTION res <- tbls[[1]] colnames(res) <- c("Gene", names(.data)[1]) if (length(.data) > 1) { for (i in 2:length(.data)) { colnames(tbls[[i]]) <- c("Gene", names(.data)[i]) res <- merge(res, tbls[[i]], by = "Gene", all = T) } } res <- merge(res, data.frame(Gene = .genes[[1]], Something = 0, stringsAsFactors = F), by = "Gene", all = T) res <- res[, -ncol(res)] res[is.na(res)] <- 0 if (!.ambig) { res <- .fix.ambig(res, .ambig) } if (.norm) { if (length(.genes) == 1) { res[,-1] <- apply(as.matrix(res[,-1]), 2, function (col) col / sum(col)) } else { res <- res / sum(res) } } res } #' Perform PCA on segments frequency data. #' #' @aliases pca.segments pca.segments.2D #' #' @description #' Perform PCA on gene segments frequency data for V- and J-segments and either return pca object or plot the results. #' #' @usage #' pca.segments(.data, .cast.freq.seg = T, ..., .text = T, .do.plot = T) #' #' pca.segments.2D(.data, .cast.freq.seg = T, ..., .text = T, .do.plot = T) #' #' @param .data Either data.frame or a list of data.frame or a result obtained from the \code{geneUsage} function. #' @param .cast.freq.seg if T then apply code{geneUsage} to the supplied data. #' @param ... Further arguments passed to \code{prcomp} or \code{geneUsage}. #' @param .text If T then plot sample names in the resulting plot. #' @param .do.plot if T then plot a graphic, else return a pca object. #' #' @return If .do.plot is T than ggplot object; else pca object. #' #' @examples #' \dontrun{ #' # Load the twins data. #' data(twb) #' # Plot a plot of results of PCA on V-segments usage. #' pca.segments(twb, T, scale. = T) #' } pca.segments <- function(.data, .cast.freq.seg = T, ..., .text = T, .do.plot = T){ if (.cast.freq.seg) { .data <- geneUsage(.data, ...)[,-1] } pca.res <- prcomp(t(as.matrix(.data)), ...) if (.do.plot) { pca.res <- data.frame(PC1 = pca.res$x[,1], PC2 = pca.res$x[,2], Subject = names(.data)) p <- ggplot() + geom_point(aes(x = PC1, y = PC2, colour = Subject), size = 3, data = pca.res) if (.text) { p <- p + geom_text(aes(x = PC1, y = PC2, label = Subject), data = pca.res, hjust=.5, vjust=-.3) } p + theme_linedraw() + guides(size=F) + ggtitle("VJ-usage: Principal Components Analysis") + .colourblind.discrete(length(pca.res$Subject), T) } else { pca.res } } pca.segments.2D <- function(.data, .cast.freq.seg = T, ..., .text = T, .do.plot = T){ if (.cast.freq.seg) { .data <- lapply(geneUsage(.data, ...), function (x) as.vector(x)) } pca.res <- prcomp(do.call(rbind, .data), ...) if (.do.plot) { pca.res <- data.frame(PC1 = pca.res$x[,1], PC2 = pca.res$x[,2], Subject = names(.data)) p <- ggplot() + geom_point(aes(x = PC1, y = PC2, colour = Subject), size = 3, data = pca.res) if (.text) { p <- p + geom_text(aes(x = PC1, y = PC2, label = Subject), data = pca.res, hjust=.5, vjust=-.3) } p <- p + theme_linedraw() + guides(size=F) + ggtitle("VJ-usage: Principal Components Analysis") + .colourblind.discrete(length(pca.res$Subject), T) } else { pca.res } }tcR/R/graph.R0000644000176200001440000002520713667026401012442 0ustar liggesusers######### MUTATION NETWORK MANAGING ########## #' Make mutation network for the given repertoire. #' #' @description #' Mutation network (or a mutation graph) is a graph with vertices representing nucleotide or in-frame amino acid sequences (out-of-frame amino acid sequences #' will automatically filtered out) and edges are connecting pairs of sequences with hamming distance or edit distance between them #' no more than specified in the \code{.max.errors} function parameter. #' #' @param .data Either character vector of sequences, data frame with \code{.label.col} #' or shared repertoire (result from the \code{shared.repertoire} function) constructed based on \code{.label.col}. #' @param .method Either "hamm" (for hamming distance) or "lev" (for edit distance). Passed to the \code{find.similar.sequences} function. #' @param .max.errors Passed to the \code{find.similar.sequences} function. #' @param .label.col Name of the column with CDR3 sequences (vertex labels). #' @param .seg.col Name of the column with V gene segments. #' @param .prob.col Name of the column with clonotype probability. #' #' @return Mutation network, i.e. igraph object with input sequences as vertices labels, ??? #' #' @seealso \link{shared.repertoire}, \link{find.similar.sequences}, \link{set.people.vector}, \link{get.people.names} #' #' @examples #' \dontrun{ #' data(twb) #' twb.shared <- shared.repertoire(twb) #' G <- mutation.network(twb.shared) #' get.people.names(G, 300, T) # "Subj.A|Subj.B" #' get.people.names(G, 300, F) # list(c("Subj.A", "Subj.B")) #' } mutation.network <- function (.data, .method = c('hamm', 'lev'), .max.errors = 1, .label.col = 'CDR3.amino.acid.sequence', .seg.col = 'V.gene', .prob.col = 'Probability') { # Make vertices and edges. if (has.class(.data, 'character')) { .data <- data.frame(A = .data, stringsAsFactors = F) colnames(.data) <- .label.col } G <- graph.empty(n = nrow(.data), directed=F) G <- add.edges(G, t(find.similar.sequences(.data[[.label.col]], .method = .method[1], .max.errors = .max.errors))) G <- simplify(G) # Every label is a sequence. G <- set.vertex.attribute(G, 'label', V(G), .data[[.label.col]]) # Add V-segments to vertices. if (.seg.col %in% colnames(.data)) { G <- set.vertex.attribute(G, 'vseg', V(G), .data[[.seg.col]]) } else { G <- set.vertex.attribute(G, 'vseg', V(G), 'nosegment') } # Set sequences' indices as in the given shared repertoire. G <- set.vertex.attribute(G, 'repind', V(G), 1:vcount(G)) # Set probabilities for sequences. if (.prob.col %in% colnames(.data)) { G <- set.vertex.attribute(G, 'prob', V(G), .data[[.prob.col]]) } else { G <- set.vertex.attribute(G, 'prob', V(G), rep.int(-1, vcount(G))) } # Add people to vertices. if ('People' %in% colnames(.data)) { attr(G, 'people') <- colnames(shared.matrix(.data)) G <- set.people.vector(G, .data) } else { attr(G, 'people') <- "Individual" G <- set.vertex.attribute(G, 'people', V(G), 1) G <- set.vertex.attribute(G, 'npeople', V(G), 1) } G } #' Set and get attributes of a mutation network related to source people. #' #' @aliases set.people.vector get.people.names #' #' @description #' Set vertice attributes 'people' and 'npeople' for every vertex in the given graph. #' Attribute 'people' is a binary string indicating in which repertoire sequence are #' found. Attribute 'npeople' is a integer indicating number of repertoires, in which #' this sequence has been found. #' #' @usage #' set.people.vector(.G, .shared.rep) #' #' get.people.names(.G, .V = V(.G), .paste = T) #' #' @param .G Mutation network. #' @param .shared.rep Shared repertoire. #' @param .V Indices of vertices. #' @param .paste If TRUE than concatenate people names to one string, else get a character vector of names. #' #' @return New graph with 'people' and 'npeople' vertex attributes or character vector of length .V or list of length .V. #' #' @examples #' \dontrun{ #' data(twb) #' twb.shared <- shared.repertoire(twb) #' G <- mutation.network(twb.shared) #' get.people.names(G, 300, T) # "Subj.A|Subj.B" #' get.people.names(G, 300, F) # list(c("Subj.A", "Subj.B")) #' } set.people.vector <- function (.G, .shared.rep) { .shared.rep[is.na(.shared.rep)] <- 0 .G <- set.vertex.attribute(.G, 'people', V(.G), apply(as.matrix(.shared.rep[, -(1:(match('People', colnames(.shared.rep))))]), 1, function (row) { paste0(as.integer(row > 0), collapse='') })) .G <- set.vertex.attribute(.G, 'npeople', V(.G), apply(as.matrix(.shared.rep[, -(1:(match('People', colnames(.shared.rep))))]), 1, function (row) { sum(row > 0) })) } get.people.names <- function (.G, .V = V(.G), .paste = T) { ppl <- attr(.G, 'people') if (!.paste) { lapply(strsplit(get.vertex.attribute(.G, 'people', .V), '', fixed=T, useBytes=T), function (l) { ppl[l == '1'] }) } else { sapply(strsplit(get.vertex.attribute(.G, 'people', .V), '', fixed=T, useBytes=T), function (l) { paste0(ppl[l == '1'], collapse='|') }, USE.NAMES = F) } } #' Set group attribute for vertices of a mutation network #' #' @aliases set.group.vector get.group.names #' #' @description #' asdasd #' #' @usage #' set.group.vector(.G, .attr.name, .groups) #' #' get.group.names(.G, .attr.name, .V = V(.G), .paste = T) #' #' @param .G Mutation network. #' @param .attr.name Name of the new vertex attribute. #' @param .V Indices of vertices. #' @param .groups List with integer vector with indices of subjects for each group. #' @param .paste if T then return character string with concatenated group names, else return list with character vectors #' with group names. #' #' @return igraph object with new vertex attribute \code{.attr.name} with binary strings for \code{set.group.vector}. #' Return character vector for \code{get.group.names}. #' #' @examples #' \dontrun{ #' data(twb) #' twb.shared <- shared.repertoire(twb) #' G <- mutation.network(twb.shared) #' G <- set.group.vector(G, "twins", list(A = c(1,2), B = c(3,4))) # <= refactor this #' get.group.names(G, "twins", 1) # "A|B" #' get.group.names(G, "twins", 300) # "A" #' get.group.names(G, "twins", 1, F) # list(c("A", "B")) #' get.group.names(G, "twins", 300, F) # list(c("A")) #' # Because we have only two groups, we can assign more readable attribute. #' V(G)$twin.names <- get.group.names(G, "twins") #' V(G)$twin.names[1] # "A|B" #' V(G)$twin.names[300] # "A" #' } set.group.vector <- function (.G, .attr.name, .groups) { d <- get.vertex.attribute(.G, 'people', V(.G)) d <- do.call(rbind, lapply(strsplit(d, '', T, useBytes = T), as.integer)) group.vec <- rep('nogroup', times = max(unlist(.groups))) for (gr.i in 1:length(.groups)) { for (elem in .groups[[gr.i]]) { group.vec[elem] <- names(.groups)[gr.i] } } grnames <- names(.groups) .G <- set.vertex.attribute(.G, .attr.name, V(.G), apply(d, 1, function (row) { paste0(as.integer(grnames %in% sort(group.vec[row > 0])), collapse='') })) attr(.G, .attr.name) <- names(.groups) .G } get.group.names <- function (.G, .attr.name, .V = V(.G), .paste = T) { grs <- attr(.G, .attr.name) if (!.paste) { lapply(strsplit(get.vertex.attribute(.G, .attr.name, .V), '', fixed=T, useBytes=T), function (l) { grs[l == '1'] }) } else { sapply(strsplit(get.vertex.attribute(.G, .attr.name, .V), '', fixed=T, useBytes=T), function (l) { paste0(grs[l == '1'], collapse='|') }, USE.NAMES = F) } } #' Get vertex neighbours. #' #' @description #' Get all properties of neighbour vertices in a mutation network of specific vertices. #' #' @param .G Mutation network. #' @param .V Indices of vertices for which return neighbours. #' @param .order Neighbours of which order return. #' #' @return List of length \code{.V} with data frames with vertex properties. First row in each data frame #' is the vertex for which neighbours was returned. #' #' @examples #' \dontrun{ #' data(twb) #' twb.shared <- shared.repertoire(twb) #' G <- mutation.network(twb.shared) #' head(mutated.neighbours(G, 1)[[1]]) #' # label vseg repind prob people npeople #' # 1 CASSDRDTGELFF TRBV6-4 1 -1 1111 4 #' # 2 CASSDSDTGELFF TRBV6-4 69 -1 1100 2 #' # 3 CASSYRDTGELFF TRBV6-3, TRBV6-2 315 -1 1001 2 #' # 4 CASKDRDTGELFF TRBV6-3, TRBV6-2 2584 -1 0100 1 #' # 5 CASSDGDTGELFF TRBV6-4 5653 -1 0010 1 #' # 6 CASSDRETGELFF TRBV6-4 5950 -1 0100 1 #' } mutated.neighbours <- function (.G, .V, .order = 1) { neis <- neighborhood(.G, .order, .V, mode = 'all') lapply(neis, function (l) { res <- as.data.frame(lapply(list.vertex.attributes(.G), function (vattr) { get.vertex.attribute(.G, vattr, l) } ), stringsAsFactors = F) colnames(res) <- list.vertex.attributes(.G) res } ) } # srcppl.distribution <- function (.G) { # one.count <- sapply(strsplit(V(.G)$people, '', fixed = T, useBytes = T), function (x) sum(x == '1')) # # mean(V(.G)$npeople) # mean(one.count) # } # # # neippl.distribution <- function (.G, .exclude.zeros = F) { # if (.exclude.zeros) { # .G <- induced.subgraph(.G, degree(.G) != 0) # } # # one.count <- sapply(strsplit(V(.G)$people, '', fixed = T, useBytes = T), function (x) sum(x == '1')) # # one.count <- V(.G)$npeople # quantile(sapply(neighborhood(.G, 1), function (x) { # if (length(x) == 1) { # 0 # } else { # mean(one.count[x[-1]]) / (length(x) - 1) # } # }), prob = c(.025, .975)) # } # # # pplvar.distribution <- function (.G, .exclude.zeros = F) { # if (.exclude.zeros) { # .G <- induced.subgraph(.G, degree(.G) != 0) # } # # ppl.inds <- lapply(strsplit(V(.G)$people, '', fixed = T, useBytes = T), function (x) which(x == '1')) # c1 <- quantile(sapply(neighborhood(.G, 1), function (x) { # if (length(x) == 1) { # 0 # } else { # # length(unique(unlist(ppl.inds[x[-1]]))) / length(x[-1]) # length(unique( unlist(ppl.inds[x[-1]]) [ !(unlist(ppl.inds[x[-1]]) %in% unlist(ppl.inds[x[1]])) ] )) # } # }), prob = c(.25, .75)) # # c2 <- mean(sapply(neighborhood(.G, 1), function (x) { # if (length(x) == 1) { # 0 # } else { # # length(unique(unlist(ppl.inds[x[-1]]))) / length(x[-1]) # length(unique( unlist(ppl.inds[x[-1]]) [ !(unlist(ppl.inds[x[-1]]) %in% unlist(ppl.inds[x[1]])) ] )) # } # })) # # c(c1[1], Mean = c2, c1[2]) # }tcR/R/shared.R0000644000176200001440000002467713667036141012623 0ustar liggesusers########## Shared TCR repertoire managing and analysis ########## #' Shared TCR repertoire managing and analysis #' #' @aliases shared.repertoire shared.matrix #' #' @description #' Generate a repertoire of shared sequences - sequences presented in more than one subject. If sequence is appeared more than once in the one #' repertoire, than only the first appeared one will be choosed for a shared repertoire. #' #' \code{shared.repertoire} - make a shared repertoire of sequences from the given list of data frames. #' #' \code{shared.matrix} - leave columns, which related to the count of sequences in people, and return them as a matrix. #' I.e., this functions will remove such columns as 'CDR3.amino.acid.sequence', 'V.gene', 'People'. #' #' @usage #' shared.repertoire(.datalist, .type = 'avrc', .min.ppl = 1, .head = -1, #' .clear = T, .verbose = T, .by.col = '', .sum.col = '', #' .max.ppl = length(.datalist)) #' #' shared.matrix(.shared.rep) #' #' @param .datalist List with data frames. #' @param .type String of length 4 denotes how to create a shared repertoire. See "Details" for #' more information. If supplied, than parameters \code{.by.col} and \code{.sum.col} will be ignored. If not supplied, than columns #' in \code{.by.col} and \code{.sum.col} will be used. #' @param .min.ppl At least how many people must have a sequence to leave this sequence in the shared repertoire. #' @param .head Parameter for the \code{head} function, applied to all data frames before clearing. #' @param .clear if T then remove all sequences which have symbols "~" or "*" (i.e., out-of-frame sequences for amino acid sequences). #' @param .verbose if T then output progress. #' @param .by.col Character vector with names of columns with sequences and their parameters (like segment) for using for creating a shared repertoire. #' @param .sum.col Character vector of length 1 with names of the column with count, percentage or any other numeric chaaracteristic of sequences for using for creating a shared repertoire. #' @param .max.ppl At most how many people must have a sequence to leave this sequence in the shared repertoire. #' @param .shared.rep Shared repertoire. #' #' @details #' Parameter \code{.type} is a string of length 4, where: #' \enumerate{ #' \item First character stands either for the letter 'a' for taking the "CDR3.amino.acid.sequence" column or #' for the letter 'n' for taking the "CDR3.nucleotide.sequence" column. #' \item Second character stands whether or not take the V.gene column. Possible values are '0' (zero) stands #' for taking no additional columns, 'v' stands for taking the "V.gene" column. #' \item Third character stands for using either UMIs or reads in choosing the column with numeric characterisitc (see the next letter). #' \item Fourth character stands for name of the column to choose as numeric characteristic of sequences. It depends on the third letter. Possible values are #' "c" for the "Umi.count" (if 3rd character is "u") / "Read.count" column (if 3rd character is "r"), "p" for the "Umi.proportion" / "Read.proportion" column, "r" for the "Rank" column or "i" for the "Index" column. #' If "Rank" or "Index" isn't in the given repertoire, than it will be created using \code{set.rank} function using "Umi.count" / "Read.count" column. #' } #' #' @return #' Data frame for \code{shared.repertoire}, matrix for \code{shared.matrix}. #' #' @seealso \link{shared.representation}, \link{set.rank} #' #' @examples #' \dontrun{ #' # Set "Rank" column in data by "Read.count" column. #' # This is doing automatically in shared.repertoire() function #' # if the "Rank" column hasn't been found. #' immdata <- set.rank(immdata) #' # Generate shared repertoire using "CDR3.amino.acid.sequence" and #' # "V.gene" columns and with rank. #' imm.shared.av <- shared.repertoire(immdata, 'avrc') #' } shared.repertoire <- function (.datalist, .type = 'avrc', .min.ppl = 1, .head = -1, .clear = T, .verbose = T, .by.col = '', .sum.col = '', .max.ppl = length(.datalist)) { .process.df <- function (.data, .bc, .sc) { if (.head != -1) { .data <- head(.data, .head) } if (.clear) { .data <- .data[grep('[*, ~]', .data[, .bc[1]], invert = T), ] } # If .data$Rank or .data$Index is NULL, than generate this columns # using the "Read.count" column. if (is.null(.data[[.sc]])) { if (.sc == 'Read.rank') { .data <- set.rank(.data, 'Read.count') .sc <- "Rank" } else if (.sc == 'Umi.rank') { .data <- set.rank(.data, 'Umi.count') .sc <- "Rank" } else if (.sc == 'Read.rank') { .data <- set.index(.data, 'Read.count') .sc <- "Index" } else { .data <- set.index(.data, 'Umi.count') .sc <- "Index" } } # minidata <- as.data.table(.data[.bc]) minidata <- as.data.table(.data[.bc]) minidata$value <- .data[, .sc] res <- as.data.table(dplyr::summarise(grouped_df(minidata, .bc), value = value[1])) class(res) <- c('data.table', 'data.frame') setnames(res, c(.bc, .sc)) res } if (nchar(.by.col[1]) == 0) { if (substr(.type, 1, 1) == 'a') { .by.col <- 'CDR3.amino.acid.sequence' } else { .by.col <- 'CDR3.nucleotide.sequence' } if (substr(.type, 2, 2) == 'v') { .by.col <- c(.by.col, 'V.gene') } } if (nchar(.sum.col) == 0) { # barcode count if (substr(.type, 3, 3) == 'u') { if (substr(.type, 4, 4) == 'c') { .sum.col <- 'Umi.count' } else if (substr(.type, 4, 4) == 'p') { .sum.col <- 'Umi.proportion' } else if (substr(.type, 4, 4) == 'r') { .sum.col <- 'Umi.rank' } else if (substr(.type, 4, 4) == 'i') { .sum.col <- 'Umi.index' } else { # As a default option. .sum.col <- 'Umi.count' } } else { # read count if (substr(.type, 4, 4) == 'c') { .sum.col <- 'Read.count' } else if (substr(.type, 4, 4) == 'p') { .sum.col <- 'Read.proportion' } else if (substr(.type, 4, 4) == 'r') { .sum.col <- 'Read.rank' } else if (substr(.type, 4, 4) == 'i') { .sum.col <- 'Read.index' } else { # As a default option. .sum.col <- 'Read.count' } } } if (.verbose) { cat("Aggregating sequences...\n"); pb <- set.pb(length(.datalist)) } l <- list() for (i in 1:length(.datalist)) { l[[i]] <- .process.df(.datalist[[i]], .by.col, .sum.col) if (.verbose) add.pb(pb) } names(l) <- names(.datalist) if (.verbose) close(pb) # l <- lapply(.datalist, .process.df, .bc = .by.col, .sc = .sum.col) res <- l[[1]] setnames(res, c(.by.col, names(.datalist)[1])) if (.verbose) { cat("Merging data tables...\n"); pb <- set.pb(length(.datalist) - 1) } if (length(l) > 1) { for (i in 2:length(l)) { res <- merge(res, l[[i]], by = .by.col, all=T, allow.cartesian=T) setnames(res, c(colnames(res)[-ncol(res)], names(.datalist)[i])) if (.verbose) add.pb(pb) } } if (.verbose) { close(pb)} res$People <- rowSums(!is.na(as.matrix(res[, (ncol(res) - length(l) + 1) : ncol(res), with = F]))) res <- res[People >= .min.ppl & People <= .max.ppl][order(People, decreasing=T)][, c(1:(ncol(res) - length(l) - 1), ncol(res), (ncol(res) - length(l)) : (ncol(res) - 1)), with = F] setattr(res, 'by.col', .by.col) setattr(res, 'sum.col', .sum.col) as.data.frame(res, stringsAsFactors = F, row.names = F) } shared.matrix <- function (.shared.rep) { as.matrix(.shared.rep[, -(1:(match('People', colnames(.shared.rep))))]) } #' Shared repertoire analysis. #' #' @aliases cosine.sharing shared.representation shared.clones.count shared.summary #' #' @description #' Functions for computing statistics and analysis of shared repertoire of sequences. #' #' \code{cosine.sharing} - apply the cosine similarity measure to the vectors of sequences' counts or indices. #' #' \code{shared.representation} - for every repertoire in the shared repetoire get a number of sequences in this repertoire which are in the other repertoires. #' Row names of the input matrix is the number of people. #' #' \code{shared.clones.count} - get the number of shared clones for every number of people. #' #' \code{shared.summary} - get a matrix with counts of pairwise shared sequences (like a result from \code{cross} function, applied to a list of data frames). #' #' @usage #' cosine.sharing(.shared.rep, .log = T) #' #' shared.representation(.shared.rep) #' #' shared.clones.count(.shared.rep) #' #' shared.summary(.shared.rep, .min.ppl = min(.shared.rep$People), #' .max.ppl = max(.shared.rep$People)) #' #' @param .shared.rep Shared repertoire, obtained from the function \code{shared.repertoire}. #' @param .log if T then apply log to the after adding laplace correction equal to one. #' @param .min.ppl Filter: get sequences with # people >= .min.ppl. #' @param .max.ppl Filter: get sequences with # people <= .max.ppl. #' #' @return Plot or PCA resulr for the \code{shared.seq.pca} function or a matrix with cosine similarity values for the \code{cosine.sharing} function. #' #' @seealso \link{shared.repertoire} #' #' @examples #' \dontrun{ #' # Load the twb data. #' data(twb) #' # Create shared repertoire on the twins data using CDR3 amino acid sequences with CDR1-2. #' twb.shared <- shared.repertoire(twb, 'av', .verbose = T) #' sh.repr <- shared.representation(twb.shared) #' sh.repr #' # Get proportion of represented shared sequences. #' apply(sh.repr, 2, function (col) col / col[1]) #' } cosine.sharing <- function (.shared.rep, .log = T) { x <- shared.matrix(.shared.rep) d <- lapply(seq_len(ncol(x)), function(i) x[,i]) d <- lapply(d, function (l) { l <- unlist(l) l[is.na(l)] <- 0 l <- l + 1 if (.log) { l <- log(l) } l }) apply.symm(d, cosine.similarity, .do.norm = F, .verbose = F) } shared.representation <- function (.shared.rep) { mat <- shared.matrix(.shared.rep) ppl <- as.integer(.shared.rep$People) apply(mat, 2, function (col) { table(c(1:ncol(mat), ppl[!is.na(col) & (col > 0)])) - 1 }) } shared.clones.count <- function (.shared.rep) { table(as.integer(.shared.rep$People)) } shared.summary <- function (.shared.rep, .min.ppl = min(.shared.rep$People), .max.ppl = max(.shared.rep$People)) { x <- apply(shared.matrix(.shared.rep), 2, function (col) which(!is.na(col) & col != 0)) if (class(x) != 'list') { x <- lapply(seq_len(ncol(x)), function(i) x[,i]) } apply.symm(x, function (a, b) length(intersect(x = a, y = b))) }tcR/R/repoverlap.R0000644000176200001440000001473113667033116013521 0ustar liggesusers#' General function for the repertoire overlap evaluation. #' #' @description #' General interface to all cloneset overlap functions. #' #' @param .data List of clonesets. #' @param .method Which method to use for the overlap evaluation. See "Details" for methods. #' @param .seq Which clonotype sequences to use for the overlap: "nuc" for "CDR3.nucleotide.sequence", "aa" for #' "CDR3.amino.acid.sequence". #' @param .quant Which column to use for the quantity of clonotypes: "read.count" for the "Read.count" column, #' "umi.count" for the "Umi.count" column, "read.prop" for the "Read.proportion" column, "umi.prop" for #' the "Umi.proportion" column. Used in "morisita" and "horn". #' @param .vgene If T than use V genes in computing shared or similar clonotypes. Used in all methods. #' @param .norm If T than compute the normalised number of shared clonotypes. Used in "exact". #' @param .a,.b Alpha and beta parameters for "tversky". Default values gives the Jaccard index measure. #' @param .do.unique If T than remove duplicates from the input data, but add their quantities to their clones. #' @param .verbose If T than output the data processing progress bar. #' #' @details #' You can see a more detailed description for each overlap method at \link{intersectClonesets} and \link{similarity}. #' #' Parameter \code{.method} can have one of the following value each corresponding to the specific method: #' #' - "exact" for the shared number of clonotypes (basic function \code{intersectClonesets(..., .type = "..e")}). #' #' - "hamm" for the number of similar clonotypes by the Hamming distance (basic function \code{intersectClonesets(..., .type = "..h")}). #' #' - "lev" for the number of similar clonotypes by the Levenshtein distance (basic function \code{intersectClonesets(..., .type = "..l")}). #' #' - "jaccard" for the Jaccard index (basic function \code{jaccard.index}). #' #' - "morisita" for the Morisita's overlap index (basic function \code{morisita.index}). #' #' - "tversky" for the Tversky index (basic function \code{tversky.index}). #' #' - "overlap" for the overlap coefficient (basic function \code{overlap.coef}). #' #' - "horn" for the Horn's index (basic function \code{horn.index}). #' #' @seealso \link{intersectClonesets}, \link{similarity}, \link{repDiversity} #' #' @examples #' \dontrun{ #' data(twb) #' repOverlap(twb, "exact", .seq = "nuc", .vgene = F) #' repOverlap(twb, "morisita", .seq = "aa", .vgene = T, .quant = "umi.count") #' ov <- repOverlap(twb) #' ov[is.na(ov)] <- 0 #' vis.pca(prcomp(ov, scale. = T), list(A = c(1, 2), B = c(3, 4))) #' } repOverlap <- function (.data, .method = c("exact", "hamm", "lev", "jaccard", "morisita", "tversky", "overlap", "horn"), .seq = c("nuc", "aa"), .quant = c("read.count", "umi.count", "read.prop", "umi.prop"), .vgene = F, .norm = T, .a = .5, .b = .5, .do.unique = T, .verbose = T) { message(" ======================================== !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !! The tcR package WILL SOON BE ORPHANED !! AND REMOVED FROM CRAN. !! !! A new package is available that is !! designed to replace tcR: !! immunarch -- https://immunarch.com/ !! !! We will be happy to help you to move !! to the new package. Feel free to contact us: !! http://github.com/immunomind/immunarch !! !! Sincerely, !! immunarch dev team and !! Vadim I. Nazarov, lead developer of tcR !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! =======================================") .merge.with.v <- function (.data, .seqcol, .vgene) { if (.vgene) { lapply(.data, function (x) paste0(x[[.seqcol]], x$V.gene) ) } else { lapply(.data, function (x) x[[.seqcol]] ) } } .pair.fun <- function (.data, .fun, .verbose) { if (length(.data) == 2) { .fun(.data[[1]], .data[[2]]) } else { apply.symm(.data, .fun, .verbose = .verbose) } } quant <- .column.choice(.quant, .verbose) if (!has.class(.data, 'list')) { cat("Error! Input data MUST be a list of clonesets!\n"); return(NA); } if (length(.data) < 2) { cat("Error! Input data MUST be a list of clonesets of minimum length 2!\n"); return(NA); } .data <- .fix.listnames(.data) .seqcol <- "CDR3.nucleotide.sequence" if (.seq[1] == "aa") { .seqcol <- "CDR3.amino.acid.sequence" } if (.method[1] %in% c("exact", "hamm", "lev")) { let1 <- "n" if (.seq[1] == "aa") { let1 <- "a" } let2 <- "0" if (.vgene) { let2 <- "v" } let3 <- substr(.method[1], 1, 1) .type <- paste0(let1, let2, let3) if (length(.data) != 2) { intersectClonesets(.data, .type = .type, .norm = .norm, .verbose = .verbose) } else { params <- list(.type = .type, .norm = .norm, .verbose = .verbose) do.call(intersectClonesets, c(list(.data[[1]], .data[[2]]), params)) } } else if (.method[1] %in% c("morisita", "horn")) { .fun <- function (x, y) { horn.index(x, y, F) } if (.method[1] == "morisita") { .fun <- function (x, y) { morisitas.index(x, y, F) } } .verbose.msg("Preprocessing data...\t", .verbose) new.data <- .merge.with.v(.data, .seqcol, .vgene) new.reads <- lapply(.data, "[[", quant) if (.do.unique) { if (.verbose) { pb <- set.pb(length(.data)) } for (i in 1:length(.data)) { .data[[i]] <- as.data.frame(summarise(grouped_df(data.frame(Sequence = new.data[[i]], Count = new.reads[[i]], stringsAsFactors = F), "Sequence"), Count = sum(Count)), stringsAsFactors = F) if (.verbose) { add.pb(pb) } } if (.verbose) { close(pb) } } .pair.fun(.data, function (x, y) { .fun(x, y) }, .verbose) } else if (.method[1] == "jaccard") { .pair.fun(.merge.with.v(.data, .seqcol, .vgene), function (x, y) { jaccard.index(x, y) }, .verbose) } else if (.method[1] == "overlap") { .pair.fun(.merge.with.v(.data, .seqcol, .vgene), function (x, y) { overlap.coef(x, y) }, .verbose) } else if (.method[1] == "tversky") { .pair.fun(.merge.with.v(.data, .seqcol, .vgene), function (x, y) { tversky.index(x, y, .a = .a, .b = .b) }, .verbose) } else { .verbose.msg("You have specified an invalid method identifier. Please check your input arguments.\n", .verbose) return(NA) } }tcR/R/parsing.R0000644000176200001440000010643213667026401013004 0ustar liggesusers########## Data processing functions ########## #' Parse input table files with the immune receptor repertoire data. #' #' @description #' General parser for cloneset table files. Each column name has specific purpose (e.g., column for #' CDR3 nucleotide sequence or aligned gene segments), so you need to supply column names which has this #' purpose in your input data. #' #' @param .filename Path to the input file with cloneset data. #' @param .nuc.seq Name of the column with CDR3 nucleotide sequences. #' @param .aa.seq Name of the column with CDR3 amino acid sequences. #' @param .reads Name of the column with counts of reads for each clonotype. #' @param .barcodes Name of the column with counts of barcodes (UMI, events) for each clonotype. #' @param .vgenes Name of the column with names of aligned Variable gene segments. #' @param .jgenes Name of the column with names of aligned Joining gene segments. #' @param .dgenes Name of the column with names of aligned Diversity gene segments. #' @param .vend Name of the column with last positions of aligned V gene segments. #' @param .jstart Name of the column with first positions of aligned J gene segments. #' @param .dalignments Character vector of length two that names columns with D5' and D3' end positions. #' @param .vd.insertions Name of the column with VD insertions for each clonotype. #' @param .dj.insertions Name of the column with DJ insertions for each clonotype. #' @param .total.insertions Name of the column with total number of insertions for each clonotype. #' @param .skip How many lines from beginning to skip. #' @param .sep Separator character. #' #' @return Data frame with immune receptor repertoire data. See \link{parse.file} for more details. #' #' @seealso \link{parse.file} #' #' @examples #' \dontrun{ #' # Parse file in "~/mitcr/immdata1.txt" as a MiTCR file. #' immdata1 <- parse.file("~/mitcr/immdata1.txt", 'mitcr') #' } parse.cloneset <- function (.filename, .nuc.seq, .aa.seq, .reads, .barcodes, .vgenes, .jgenes, .dgenes, .vend, .jstart, .dalignments, .vd.insertions, .dj.insertions, .total.insertions, .skip = 0, .sep = '\t') { .make.names <- function (.char) { if (is.na(.char[1])) { NA } else { tolower(make.names(.char)) } } .nuc.seq <- .make.names(.nuc.seq) .aa.seq <- .make.names(.aa.seq) .reads <- .make.names(.reads) .barcodes <- .make.names(.barcodes) .vgenes <- .make.names(.vgenes) .jgenes <- .make.names(.jgenes) .dgenes <- .make.names(.dgenes) .vend <- .make.names(.vend) .jstart <- .make.names(.jstart) .vd.insertions <- .make.names(.vd.insertions) .dj.insertions <- .make.names(.dj.insertions) .total.insertions <- .make.names(.total.insertions) .dalignments1 <- .make.names(.dalignments[1]) .dalignments2 <- .make.names(.dalignments[2]) .dalignments <- .make.names(.dalignments) f <- file(.filename, "r") l <- readLines(f, 1) # Check for different levels of the MiTCR output if (length(grep("MiTCRFullExportV1.1", l, fixed = T))) { .skip <- 1 } # Check for different VDJtools outputs if (length(strsplit(l, "-", T)) > 0) { if (length(strsplit(l, "-", T)[[1]]) == 3) { if (strsplit(l, "-", T)[[1]][2] == "header") { .reads <- "count" .barcodes <- "count" .skip <- 1 } } else if (substr(l, 1, 1) == "#") { .reads <- "X.count" .barcodes <- "X.count" } } close(f) table.colnames <- tolower(make.names(read.table(gzfile(.filename), sep = .sep, skip = .skip, nrows = 1, stringsAsFactors = F, strip.white = T, comment.char = "", quote = "")[1,])) swlist <- list('character', 'character', 'integer', 'integer', 'character', 'character', 'character', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer') names(swlist) <- c(.nuc.seq, .aa.seq, .reads, .barcodes, .vgenes, .jgenes, .dgenes, .vend, .jstart, .dalignments, .vd.insertions, .dj.insertions, .total.insertions) swlist <- c(swlist, 'NULL') col.classes <- unlist(sapply(table.colnames, function (x) { do.call(switch, c(x, swlist)) }, USE.NAMES = F)) suppressWarnings(df <- read.table(file = gzfile(.filename), header = T, colClasses = col.classes, sep = .sep, skip = .skip, strip.white = T, comment.char = "", quote = "")) names(df) = tolower(names(df)) df$Read.proportion <- df[, make.names(.reads)] / sum(df[, make.names(.reads)]) .read.prop <- 'Read.proportion' if(is.na(.barcodes)) { .barcodes <- "Umi.count" df$Umi.count <- NA df$Umi.proportion <- NA } else { df$Umi.proportion <- df[, make.names(.barcodes)] / sum(df[, make.names(.barcodes)]) } .umi.prop <- 'Umi.proportion' if (is.na(.aa.seq)) { df$CDR3.amino.acid.sequence <- bunch.translate(df$CDR3.nucleotide.sequence) .aa.seq <- 'CDR3.amino.acid.sequence' } # check for VJ or VDJ recombination # VJ / VDJ / Undeterm recomb_type = "Undeterm" if (sum(substr(head(df)[[.vgenes]], 1, 4) %in% c("TCRA", "TRAV", "TRGV", "IGKV", "IGLV"))) { recomb_type = "VJ" } else if (sum(substr(head(df)[[.vgenes]], 1, 4) %in% c("TCRB", "TRBV", "TRDV", "IGHV"))) { recomb_type = "VDJ" } if (!(.vd.insertions %in% table.colnames)) { .vd.insertions <- "VD.insertions" if (!is.na(.vend) && !is.na(.dalignments)) { if (recomb_type == "VJ") { df$VD.insertions <- -1 } else if (recomb_type == "VDJ") { df$VD.insertions <- df[[.dalignments1]] - df[[.vend]] - 1 df$VD.insertions[df[[.dalignments1]] == -1] <- -1 df$VD.insertions[df[[.vend]] == -1] <- -1 } else { df$VD.insertions <- -1 } } else { df$VD.insertions <- -1 df$V.end <- -1 df$D5.end <- -1 df$D3.end <- -1 .vend <- "V.end" .dalignments <- c("D5.end", "D3.end") } } if (!(.dj.insertions %in% table.colnames)) { .dj.insertions <- "DJ.insertions" if (!is.na(.jstart) && !is.na(.dalignments)) { if (recomb_type == "VJ") { df$DJ.insertions <- -1 } else if (recomb_type == "VDJ") { df$DJ.insertions <- df[[.jstart]] - df[[.dalignments2]] - 1 df$DJ.insertions[df[[.dalignments2]] == -1] <- -1 df$DJ.insertions[df[[.jstart]] == -1] <- -1 } else { df$DJ.insertions <- -1 } } else { df$DJ.insertions <- -1 df$J.start <- -1 df$D5.end <- -1 df$D3.end <- -1 .jstart <- "J.start" .dalignments <- c("D5.end", "D3.end") } } if (!(.total.insertions %in% table.colnames)) { .total.insertions <- "Total.insertions" df$Total.insertions <- -1 if (recomb_type == "VJ") { df$Total.insertions <- df[[.jstart]] - df[[.vend]] - 1 df$Total.insertions[df$Total.insertions < 0] <- 0 df$Total.insertions[df[[.vend]] == -1] <- -1 df$Total.insertions[df[[.jstart]] == -1] <- -1 } else if (recomb_type == "VDJ" ) { df$Total.insertions <- df[[.vd.insertions]] + df[[.dj.insertions]] } } df$Total.insertions[df$Total.insertions < 0] <- -1 if (is.na(.dgenes)) { df$D.gene <- '' .dgenes <- "D.gene" } df <- df[, make.names(c(.barcodes, .umi.prop, .reads, .read.prop, .nuc.seq, .aa.seq, .vgenes, .jgenes, .dgenes, .vend, .jstart, .dalignments, .vd.insertions, .dj.insertions, .total.insertions))] colnames(df) <- c('Umi.count', 'Umi.proportion', 'Read.count', 'Read.proportion', 'CDR3.nucleotide.sequence', 'CDR3.amino.acid.sequence', 'V.gene', 'J.gene', 'D.gene', 'V.end', 'J.start', 'D5.end', 'D3.end', 'VD.insertions', 'DJ.insertions', 'Total.insertions') df } #' Parse input table files with immune receptor repertoire data. #' #' @aliases parse.folder parse.file.list parse.file parse.mitcr parse.mitcrbc parse.migec parse.vdjtools parse.immunoseq parse.immunoseq2 parse.immunoseq3 parse.tcr parse.mixcr parse.imseq parse.migmap #' #' @description #' Load the TCR data from the file with the given filename to a data frame or load all #' files from the given folder to a list of data frames. The folder must contain onky files with the specified format. #' Input files could be either text files or archived with gzip ("filename.txt.gz") or bzip2 ("filename.txt.bz2"). #' For a general parser see \code{\link{parse.cloneset}}. #' #' Parsers are available for: #' MiTCR ("mitcr"), MiTCR w/ UMIs ("mitcrbc"), MiGEC ("migec"), VDJtools ("vdjtools"), #' ImmunoSEQ ("immunoseq" or 'immunoseq2' for old and new formats respectively), #' MiXCR ("mixcr"), IMSEQ ("imseq") and tcR ("tcr", data frames saved with the `repSave()` function). #' #' Output of MiXCR should contain either all hits or best hits for each gene segment. #' #' Output of IMSEQ should be generated with parameter "-on". In this case there will be no positions of aligned gene segments in the output data frame #' due to restrictions of IMSEQ output. #' #' tcR's data frames should be saved with the `repSave()` function. #' #' @usage #' parse.file(.filename, #' .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', #' 'mixcr', 'imseq', 'tcr'), ...) #' #' parse.file.list(.filenames, #' .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', #' 'mixcr', 'imseq', 'tcr'), .namelist = NA) #' #' parse.folder(.folderpath, #' .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', #' 'mixcr', 'imseq', 'tcr'), ...) #' #' parse.mitcr(.filename) #' #' parse.mitcrbc(.filename) #' #' parse.migec(.filename) #' #' parse.vdjtools(.filename) #' #' parse.immunoseq(.filename) #' #' parse.immunoseq2(.filename) #' #' parse.immunoseq3(.filename) #' #' parse.mixcr(.filename) #' #' parse.imseq(.filename) #' #' parse.tcr(.filename) #' #' parse.migmap(.filename) #' #' @param .filename Path to the input file with cloneset data. #' @param .filenames Vector or list with paths to files with cloneset data. #' @param .folderpath Path to the folder with text cloneset files. #' @param .format String that specifies the input format. #' @param .namelist Either NA or character vector of length \code{.filenames} with names for output data frames. #' @param ... Parameters passed to \code{parse.cloneset}. #' #' @return Data frame with immune receptor repertoire data. Each row in this data frame corresponds to a clonotype. #' The data frame has following columns: #' #' - "Umi.count" - number of barcodes (events, UMIs); #' #' - "Umi.proportion" - proportion of barcodes (events, UMIs); #' #' - "Read.count" - number of reads; #' #' - "Read.proportion" - proportion of reads; #' #' - "CDR3.nucleotide.sequence" - CDR3 nucleotide sequence; #' #' - "CDR3.amino.acid.sequence" - CDR3 amino acid sequence; #' #' - "V.gene" - names of aligned Variable gene segments; #' #' - "J.gene" - names of aligned Joining gene segments; #' #' - "D.gene" - names of aligned Diversity gene segments; #' #' - "V.end" - last positions of aligned V gene segments (1-based); #' #' - "J.start" - first positions of aligned J gene segments (1-based); #' #' - "D5.end" - positions of D'5 end of aligned D gene segments (1-based); #' #' - "D3.end" - positions of D'3 end of aligned D gene segments (1-based); #' #' - "VD.insertions" - number of inserted nucleotides (N-nucleotides) at V-D junction (-1 for receptors with VJ recombination); #' #' - "DJ.insertions" - number of inserted nucleotides (N-nucleotides) at D-J junction (-1 for receptors with VJ recombination); #' #' - "Total.insertions" - total number of inserted nucleotides (number of N-nucleotides at V-J junction for receptors with VJ recombination). #' #' @seealso \link{parse.cloneset}, \link{repSave}, \link{repLoad} #' #' @examples #' \dontrun{ #' # Parse file in "~/mitcr/immdata1.txt" as a MiTCR file. #' immdata1 <- parse.file("~/mitcr/immdata1.txt", 'mitcr') #' # Parse VDJtools file archive as .gz file. #' immdata1 <- parse.file("~/mitcr/immdata3.txt.gz", 'vdjtools') #' # Parse files "~/data/immdata1.txt" and "~/data/immdat2.txt" as MiGEC files. #' immdata12 <- parse.file.list(c("~/data/immdata1.txt", #' "~/data/immdata2.txt"), 'migec') #' # Parse all files in "~/data/" as MiGEC files. #' immdata <- parse.folder("~/data/", 'migec') #' } parse.folder <- function (.folderpath, .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', 'mixcr', 'imseq', 'tcr'), ...) { parse.file.list(list.files(.folderpath, full.names = T), .format) } parse.file.list <- function (.filenames, .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', 'mixcr', 'imseq', 'tcr'), .namelist = NA) { # Remove full paths and extension from the given string. .remove.ext <- function (.str) { gsub(pattern = '.*/|[.].*$', replacement = '', x = .str) # .str } .filenames <- as.list(.filenames) datalist <- list() for (i in 1:length(.filenames)) { cat(i, "/", length(.filenames), ' Parsing "', .filenames[[i]], '"\n\t', sep = "") datalist[[i]] <- parse.file(.filenames[[i]], .format) cat("Done. Cloneset with", nrow(datalist[[i]]), "clonotypes.\n") flush.console() } if (is.na(.namelist)) { namelist <- lapply(X = .filenames, FUN = .remove.ext) names(datalist) <- unlist(namelist) } datalist } parse.file <- function(.filename, .format = c('mitcr', 'mitcrbc', 'migec', 'vdjtools', 'immunoseq', 'mixcr', 'imseq', 'tcr'), ...) { parse.fun <- switch(.format[1], mitcr = parse.mitcr, mitcrbc = parse.mitcrbc, migec = parse.migec, vdjtools = parse.vdjtools, immunoseq = parse.immunoseq, immunoseq2 = parse.immunoseq2, immunoseq3 = parse.immunoseq3, mixcr = parse.mixcr, imseq = parse.imseq, tcr = parse.tcr, parse.cloneset) parse.fun(.filename, ...) } parse.mitcr <- function (.filename) { filename <- .filename nuc.seq <- 'CDR3 nucleotide sequence' aa.seq <- 'CDR3 amino acid sequence' reads <- 'Read count' barcodes <- NA vgenes <- 'V segments' jgenes <- 'J segments' dgenes <- 'D segments' vend <- 'Last V nucleotide position' jstart <- 'First J nucleotide position' dalignments <- c('First D nucleotide position', 'Last D nucleotide position') vd.insertions <- 'VD insertions' dj.insertions <- 'DJ insertions' total.insertions <- 'Total insertions' .skip = 0 .sep = '\t' parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep) } parse.mitcrbc <- function (.filename) { filename <- .filename nuc.seq <- 'CDR3 nucleotide sequence' aa.seq <- 'CDR3 amino acid sequence' reads <- 'Count' barcodes <- 'NNNs' vgenes <- 'V segments' jgenes <- 'J segments' dgenes <- 'D segments' vend <- 'Last V nucleotide position' jstart <- 'First J nucleotide position' dalignments <- c('First D nucleotide position', 'Last D nucleotide position') vd.insertions <- 'VD insertions' dj.insertions <- 'DJ insertions' total.insertions <- 'Total insertions' .skip = 0 .sep = '\t' fix.genes(parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep)) } parse.migec <- function (.filename) { filename <- .filename nuc.seq <- 'CDR3 nucleotide sequence' aa.seq <- 'CDR3 amino acid sequence' reads <- 'Good reads' barcodes <- 'Good events' vgenes <- 'V segments' jgenes <- 'J segments' dgenes <- 'D segments' vend <- 'Last V nucleotide position' jstart <- 'First J nucleotide position' dalignments <- c('First D nucleotide position', 'Last D nucleotide position') vd.insertions <- 'VD insertions' dj.insertions <- 'DJ insertions' total.insertions <- 'Total insertions' .skip = 0 .sep = '\t' fix.genes(parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep)) } parse.vdjtools <- function (.filename) { filename <- .filename nuc.seq <- 'cdr3nt' aa.seq <- 'CDR3aa' reads <- 'count' barcodes <- 'count' vgenes <- 'V' jgenes <- 'J' dgenes <- 'D' vend <- 'Vend' jstart <- 'Jstart' dalignments <- c('Dstart', 'Dend') vd.insertions <- "NO SUCH COLUMN AT ALL 1" dj.insertions <- "NO SUCH COLUMN AT ALL 2" total.insertions <- "NO SUCH COLUMN AT ALL 3" .skip = 0 .sep = '\t' parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep) } parse.immunoseq <- function (.filename) { filename <- .filename nuc.seq <- 'nucleotide' aa.seq <- 'aminoAcid' reads <- 'count' barcodes <- 'vIndex' vgenes <- 'vGeneName' jgenes <- 'jGeneName' dgenes <- 'dFamilyName' vend <- 'n1Index' jstart <- 'jIndex' dalignments <- c('dIndex', 'n2Index') vd.insertions <- "n1Insertion" dj.insertions <- "n2Insertion" total.insertions <- "NO SUCH COLUMN AT ALL 3" .skip = 0 .sep = '\t' df <- parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep) # fix nucleotide sequences df$CDR3.nucleotide.sequence <- substr(df$CDR3.nucleotide.sequence, df$Umi.count + 1, nchar(df$CDR3.nucleotide.sequence) - 6) # add out-of-frame amino acid sequences df$CDR3.amino.acid.sequence <- bunch.translate(df$CDR3.nucleotide.sequence) # df <- fix.alleles(df) # fix genes names and "," .fix.genes <- function (.col) { # fix "," .col <- gsub(",", ", ", .col, fixed = T, useBytes = T) # fix forward zeros .col <- gsub("([0])([0-9])", "\\2", .col, useBytes = T) # fix gene names .col <- gsub("TCR", "TR", .col, fixed = T, useBytes = T) .col } df$V.gene <- .fix.genes(df$V.gene) df$D.gene <- .fix.genes(df$D.gene) df$J.gene <- .fix.genes(df$J.gene) # update V, D and J positions .fix.poses <- function (.col) { df[df[[.col]] != -1, .col] <- df[df[[.col]] != -1, .col] - df$Umi.count[df[[.col]] != -1] df } df <- .fix.poses("V.end") df <- .fix.poses("D3.end") df <- .fix.poses("D5.end") df <- .fix.poses("J.start") # nullify barcodes df$Umi.count <- NA df$Umi.proportion <- NA df } parse.immunoseq2 <- function (.filename) { filename <- .filename nuc.seq <- 'nucleotide' aa.seq <- 'aminoAcid' reads <- 'count (templates)' barcodes <- 'vIndex' vgenes <- 'vGeneName' jgenes <- 'jGeneName' dgenes <- 'dFamilyName' vend <- 'n1Index' jstart <- 'jIndex' dalignments <- c('dIndex', 'n2Index') vd.insertions <- "n1Insertion" dj.insertions <- "n2Insertion" total.insertions <- "NO SUCH COLUMN AT ALL 3" .skip = 0 .sep = '\t' df <- parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep) # fix nucleotide sequences df$CDR3.nucleotide.sequence <- substr(df$CDR3.nucleotide.sequence, df$Umi.count + 1, nchar(df$CDR3.nucleotide.sequence) - 6) # add out-of-frame amino acid sequences df$CDR3.amino.acid.sequence <- bunch.translate(df$CDR3.nucleotide.sequence) # df <- fix.alleles(df) # fix genes names and "," .fix.genes <- function (.col) { # fix "," .col <- gsub(",", ", ", .col, fixed = T, useBytes = T) # fix forward zeros .col <- gsub("([0])([0-9])", "\\2", .col, useBytes = T) # fix gene names .col <- gsub("TCR", "TR", .col, fixed = T, useBytes = T) .col } df$V.gene <- .fix.genes(df$V.gene) df$D.gene <- .fix.genes(df$D.gene) df$J.gene <- .fix.genes(df$J.gene) # update V, D and J positions .fix.poses <- function (.col) { df[df[[.col]] != -1, .col] <- df[df[[.col]] != -1, .col] - df$Umi.count[df[[.col]] != -1] df } df <- .fix.poses("V.end") df <- .fix.poses("D3.end") df <- .fix.poses("D5.end") df <- .fix.poses("J.start") # nullify barcodes df$Umi.count <- NA df$Umi.proportion <- NA df } parse.immunoseq3 <- function (.filename) { filename <- .filename nuc.seq <- 'nucleotide' aa.seq <- 'aminoAcid' reads <- 'count (reads)' barcodes <- 'vIndex' vgenes <- 'vGeneName' jgenes <- 'jGeneName' dgenes <- 'dFamilyName' vend <- 'n1Index' jstart <- 'jIndex' dalignments <- c('dIndex', 'n2Index') vd.insertions <- "n1Insertion" dj.insertions <- "n2Insertion" total.insertions <- "NO SUCH COLUMN AT ALL 3" .skip = 0 .sep = '\t' df <- parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep) # fix nucleotide sequences df$CDR3.nucleotide.sequence <- substr(df$CDR3.nucleotide.sequence, df$Umi.count + 1, nchar(df$CDR3.nucleotide.sequence) - 6) # add out-of-frame amino acid sequences df$CDR3.amino.acid.sequence <- bunch.translate(df$CDR3.nucleotide.sequence) # df <- fix.alleles(df) # fix genes names and "," .fix.genes <- function (.col) { # fix "," .col <- gsub(",", ", ", .col, fixed = T, useBytes = T) # fix forward zeros .col <- gsub("([0])([0-9])", "\\2", .col, useBytes = T) # fix gene names .col <- gsub("TCR", "TR", .col, fixed = T, useBytes = T) .col } df$V.gene <- .fix.genes(df$V.gene) df$D.gene <- .fix.genes(df$D.gene) df$J.gene <- .fix.genes(df$J.gene) # update V, D and J positions .fix.poses <- function (.col) { df[df[[.col]] != -1, .col] <- df[df[[.col]] != -1, .col] - df$Umi.count[df[[.col]] != -1] df } df <- .fix.poses("V.end") df <- .fix.poses("D3.end") df <- .fix.poses("D5.end") df <- .fix.poses("J.start") # nullify barcodes df$Umi.count <- NA df$Umi.proportion <- NA df } parse.mixcr <- function (.filename) { .filename <- .filename .nuc.seq <- 'nseqcdr3' .aa.seq <- 'aaseqcdr3' .reads <- 'clonecount' .barcodes <- 'clonecount' .sep = '\t' .vend <- "allvalignments" .jstart <- "alljalignments" .dalignments <- "alldalignments" .vd.insertions <- "VD.insertions" .dj.insertions <- "DJ.insertions" .total.insertions <- "Total.insertions" table.colnames <- tolower(make.names(read.table(gzfile(.filename), sep = .sep, skip = 0, nrows = 1, stringsAsFactors = F, strip.white = T, comment.char = "", quote = "")[1,])) table.colnames <- gsub(".", "", table.colnames, fixed = T) if ("bestvhit" %in% table.colnames) { .vgenes <- 'bestvhit' } else if ('allvhits' %in% table.colnames) { .vgenes <- 'allvhits' } else if ('vhits' %in% table.colnames) { .vgenes <- 'vhits' } else if ('allvhitswithscore' %in% table.colnames) { .vgenes <- 'allvhitswithscore' } else { cat("Error: can't find a column with V genes\n") } if ("bestjhit" %in% table.colnames) { .jgenes <- 'bestjhit' } else if ('alljhits' %in% table.colnames) { .jgenes <- 'alljhits' } else if ('jhits' %in% table.colnames) { .jgenes <- 'jhits' } else if ('alljhitswithscore' %in% table.colnames) { .jgenes <- 'alljhitswithscore' } else { cat("Error: can't find a column with J genes\n") } if ("bestdhit" %in% table.colnames) { .dgenes <- 'bestdhit' } else if ('alldhits' %in% table.colnames) { .dgenes <- 'alldhits' } else if ('dhits' %in% table.colnames) { .dgenes <- 'dhits' } else if ('alldhitswithscore' %in% table.colnames) { .dgenes <- 'alldhitswithscore' } else { cat("Error: can't find a column with D genes\n") } swlist <- list('character', 'character', 'integer', 'integer', 'character', 'character', 'character', 'character', 'character', 'character', 'character', 'character', 'character') names(swlist) <- c(.nuc.seq, .aa.seq, .reads, .barcodes, .vgenes, .jgenes, .dgenes, .vend, .jstart, .dalignments, .vd.insertions, .dj.insertions, .total.insertions) swlist <- c(swlist, 'NULL') col.classes <- unlist(sapply(table.colnames, function (x) { do.call(switch, c(x, swlist)) }, USE.NAMES = F)) suppressWarnings(df <- read.table(file = gzfile(.filename), header = T, colClasses = col.classes, sep = .sep, skip = 0, strip.white = T, comment.char = "", quote = "", fill = T)) names(df) <- tolower(gsub(".", "", names(df), fixed = T)) df$Read.proportion <- df[, make.names(.reads)] / sum(df[, make.names(.reads)]) .read.prop <- 'Read.proportion' df$Umi.count <- df[, .reads] df$Umi.proportion <- df$Umi.count / sum(df$Umi.count) .barcodes <- 'Umi.count' .umi.prop <- 'Umi.proportion' df$CDR3.amino.acid.sequence <- bunch.translate(df[[.nuc.seq]]) .aa.seq <- 'CDR3.amino.acid.sequence' # check for VJ or VDJ recombination # VJ / VDJ / Undeterm recomb_type = "Undeterm" if (sum(substr(head(df)[[.vgenes]], 1, 4) %in% c("TCRA", "TRAV", "TRGV", "IGKV", "IGLV"))) { recomb_type = "VJ" } else if (sum(substr(head(df)[[.vgenes]], 1, 4) %in% c("TCRB", "TRBV", "TRDV", "IGHV"))) { recomb_type = "VDJ" } .vd.insertions <- "VD.insertions" df$VD.insertions <- -1 if (recomb_type == "VJ") { df$VD.insertions <- -1 } else if (recomb_type == "VDJ") { logic <- sapply(strsplit(df[[.dalignments]], "|", T, F, T), length) >= 4 & sapply(strsplit(df[[.vend]], "|", T, F, T), length) >= 5 df$VD.insertions[logic] <- as.numeric(sapply(strsplit(df[[.dalignments]][logic], "|", T, F, T), "[[", 4)) - as.numeric(sapply(strsplit(df[[.vend]][logic], "|", T, F, T), "[[", 5)) - 1 } .dj.insertions <- "DJ.insertions" df$DJ.insertions <- -1 if (recomb_type == "VJ") { df$DJ.insertions <- -1 } else if (recomb_type == "VDJ") { logic <- sapply(strsplit(df[[.jstart]], "|", T, F, T), length) >= 4 & sapply(strsplit(df[[.dalignments]], "|", T, F, T), length) >= 5 df$DJ.insertions[logic] <- as.numeric(sapply(strsplit(df[[.jstart]][logic], "|", T, F, T), "[[", 4)) - as.numeric(sapply(strsplit(df[[.dalignments]][logic], "|", T, F, T), "[[", 5)) - 1 } logic <- (sapply(strsplit(df[[.vend]], "|", T, F, T), length) > 4) & (sapply(strsplit(df[[.jstart]], "|", T, F, T), length) >= 4) .total.insertions <- "Total.insertions" if (recomb_type == "VJ") { df$Total.insertions <- -1 if (length(which(logic)) > 0) { df$Total.insertions[logic] <- as.numeric(sapply(strsplit(df[[.jstart]][logic], "|", T, F, T), "[[", 4)) - as.numeric(sapply(strsplit(df[[.vend]][logic], "|", T, F, T), "[[", 5)) - 1 } } else if (recomb_type == "VDJ") { df$Total.insertions <- df[[.vd.insertions]] + df[[.dj.insertions]] } else { df$Total.insertions <- -1 } df$Total.insertions[df$Total.insertions < 0] <- -1 df$V.end <- -1 df$J.start <- -1 df[[.vend]] = gsub(";", "", df[[.vend]], fixed = T) logic = sapply(strsplit(df[[.vend]], "|", T, F, T), length) >= 5 df$V.end[logic] <- sapply(strsplit(df[[.vend]][logic], "|", T, F, T), "[[", 5) logic = sapply(strsplit(df[[.jstart]], "|", T, F, T), length) >= 4 df$J.start[logic] <- sapply(strsplit(df[[.jstart]][logic], "|", T, F, T), "[[", 4) .vend <- "V.end" .jstart <- "J.start" logic <- sapply(strsplit(df[[.dalignments]], "|", T, F, T), length) >= 5 df$D5.end <- -1 df$D3.end <- -1 df$D5.end[logic] <- sapply(strsplit(df[[.dalignments]][logic], "|", T, F, T), "[[", 4) df$D3.end[logic] <- sapply(strsplit(df[[.dalignments]][logic], "|", T, F, T), "[[", 5) .dalignments <- c('D5.end', 'D3.end') df <- df[, make.names(c(.barcodes, .umi.prop, .reads, .read.prop, .nuc.seq, .aa.seq, .vgenes, .jgenes, .dgenes, .vend, .jstart, .dalignments, .vd.insertions, .dj.insertions, .total.insertions))] colnames(df) <- c('Umi.count', 'Umi.proportion', 'Read.count', 'Read.proportion', 'CDR3.nucleotide.sequence', 'CDR3.amino.acid.sequence', 'V.gene', 'J.gene', 'D.gene', 'V.end', 'J.start', 'D5.end', 'D3.end', 'VD.insertions', 'DJ.insertions', 'Total.insertions') df$V.gene <- gsub("([*][[:digit:]]*)([(][[:digit:]]*[.]*[[:digit:]]*[)])", "", df$V.gene) df$V.gene <- gsub(",", ", ", df$V.gene) df$D.gene <- gsub("([*][[:digit:]]*)([(][[:digit:]]*[.]*[[:digit:]]*[)])", "", df$D.gene) df$D.gene <- gsub(",", ", ", df$D.gene) df$J.gene <- gsub("([*][[:digit:]]*)([(][[:digit:]]*[.]*[[:digit:]]*[)])", "", df$J.gene) df$J.gene <- gsub(",", ", ", df$J.gene) fix.alleles(df) } parse.imseq <- function (.filename) { f <- gzfile(.filename) all.lines <- strsplit(readLines(f), ":", T, useBytes = T) close(f) df <- data.frame(Umi.count = NA, Umi.proportion = NA, Read.count = as.integer(sapply(all.lines, function (x) strsplit(x[3], "\t", T, useBytes = T)[[1]][2])), CDR3.nucleotide.sequence = sapply(all.lines, "[[", 2), CDR3.amino.acid.sequence = bunch.translate(sapply(all.lines, "[[", 2)), V.gene = sapply(all.lines, "[[", 1), J.gene = sapply(all.lines, function (x) strsplit(x[3], "\t", T, useBytes = T)[[1]][1]), D.gene = "", V.end = -1, J.start = -1, D5.end = -1, D3.end = -1, VD.insertions = -1, DJ.insertions = -1, Total.insertions = -1, stringsAsFactors = F) df$Read.proportion <- df$Read.count / sum(df$Read.count) df <- df[, c('Umi.count', 'Umi.proportion', 'Read.count', 'Read.proportion', 'CDR3.nucleotide.sequence', 'CDR3.amino.acid.sequence', 'V.gene', 'J.gene', 'D.gene', 'V.end', 'J.start', 'D5.end', 'D3.end', 'VD.insertions', 'DJ.insertions', 'Total.insertions')] cls <- c("as.integer", "as.numeric", "as.integer", 'as.numeric', "as.character", "as.character", "as.character", "as.character", "as.character", "as.integer", "as.integer", "as.integer", "as.integer", "as.integer", "as.integer", "as.integer") for (i in 1:ncol(df)) { df[[i]] <- do.call(cls[i], list(df[[i]])) } df } parse.tcr <- function (.filename) { suppressWarnings(df <- read.table(file = gzfile(.filename), header = T, sep = "\t", skip = 0, strip.white = T, comment.char = "", quote = "", fill = T, stringsAsFactors = F)) df } parse.migmap <- function (.filename) { filename <- .filename nuc.seq <- 'cdr3nt' aa.seq <- 'cdr3aa' reads <- 'count' barcodes <- 'count' vgenes <- 'v' jgenes <- 'j' dgenes <- 'd' vend <- 'v.end.in.cdr3' jstart <- 'j.start.in.cdr3' dalignments <- c('d.start.in.cdr3', 'd.end.in.cdr3') vd.insertions <- "NONE" dj.insertions <- "NONE" total.insertions <- "NONE" .skip = 0 .sep = '\t' parse.cloneset(.filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .reads = reads, .barcodes = barcodes, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dalignments = dalignments, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep) }tcR/MD50000644000176200001440000001546713667667012011345 0ustar 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*src/neighbour.search.cpp 6b5ddc9d7590c7c0e16b88f16d6c722c *vignettes/tcrvignette.Rmd tcR/inst/0000755000176200001440000000000013667551325011774 5ustar liggesuserstcR/inst/crossanalysis.report.Rmd0000644000176200001440000001173313667026401016644 0ustar liggesusers--- title: "TCR beta repertoire intergroup analysis" output: html_document: theme: spacelab toc: yes --- ## Before analysis Before running this pipeline you should do next steps: 1. Save your parsed MiTCR data to the `immdata` variable (**it must be a list with mitcr data frames**). ``` immdata <- parse.folder('/home/username/mitcrdata/') ``` 2. Save the `immdata` variable to the some folder as the `.rda` file. ``` save(immdata, file = '/home/username/immdata.rda') ``` 3. In the code block below change the path string to the path to yours `immdata.rda` file. After that click the **Knit HTML** button to start analysis and make an output .html file with it's results. ```{r loaddata,warning=FALSE,message=F} load('../data/twb.rda') immdata <- twb library(tcR) ``` 4. Friendly advice: run the pipeline on first N top sequences first and then set up the size of figures. ```{r lapplyhead,warning=FALSE,message=F} N <- 10000 immdata <- lapply(immdata, head, N) ``` ## Number of shared clones and clonotypes ### Number of shared clones (CDR3 nucleotide sequences) #### -- without and with normalisation ```{r nuc0crosses, fig.width=13,warning=FALSE,message=F} crs1 <- repOverlap(immdata, .norm=F, .verbose=F) crs2 <- repOverlap(immdata, .norm=T, .verbose=F) do.call(grid.arrange, list(vis.heatmap(crs1, .title = 'Number of shared clones', .legend = 'Shared clones'), vis.heatmap(crs2, .title = 'Number of shared clones', .legend = 'Shared clones'), nrow = 1)) ``` ### Number of shared clonotypes (CDR3 amino acid sequences) #### -- without and with normalisation ```{r aa0crosses, fig.width=13,warning=FALSE,message=F} crs1 <- repOverlap(immdata, .seq = "aa", .norm=F, .verbose=F) crs2 <- repOverlap(immdata, .seq = "aa", .norm=T, .verbose=F) do.call(grid.arrange, list(vis.heatmap(crs1), vis.heatmap(crs2), nrow = 1)) ``` ### Number of shared clones using V-segments #### -- without and with normalisation ```{r nucvcrosses, fig.width=13,warning=FALSE,message=F} crs1 <- repOverlap(immdata, .vgene = T, .norm=F, .verbose=F) crs2 <- repOverlap(immdata, .vgene = T, .norm=T, .verbose=F) do.call(grid.arrange, list(vis.heatmap(crs1, .title = 'Number of shared clones + V', .legend = 'Shared clones'), vis.heatmap(crs2, .title = 'Number of shared clones + V'), nrow = 1)) ``` ### Number of shared clonotypes using V-segments #### -- without and with normalisation ```{r aavcrosses, fig.width=13,warning=FALSE,message=F} crs1 <- repOverlap(immdata, .seq = "aa", .vgene = T, .norm=F, .verbose=F) crs2 <- repOverlap(immdata, .seq = "aa", .vgene = T, .norm=T, .verbose=F) do.call(grid.arrange, list(vis.heatmap(crs1, .title = 'Number of shared clonotypes + V'), vis.heatmap(crs2, .title = 'Number of shared clonotypes + V'), nrow = 1)) ``` ## Segments statistics ### V-segments usage ```{r vusagehist, fig.width=16, fig.height=10,warning=FALSE,message=F} vis.gene.usage(immdata, HUMAN_TRBV, .ncol = 2, .coord.flip = F) ``` ### V-segments summary statistics ```{r vseboxplot, fig.width = 13, fig.height=10,warning=FALSE,message=F} # Change the groups variable for plotting V-usage boxplot for groups. groups <- list(Group.A = names(immdata)[1:(length(immdata) / 2)], Group.B = names(immdata)[(length(immdata) / 2 + 1) : length(immdata)]) vis.group.boxplot(geneUsage(immdata, HUMAN_TRBV, .norm = T), groups, .rotate.x = T) ``` ### J-segments usage ```{r jusagehist, fig.width=16, fig.height=10,warning=FALSE,message=F} vis.gene.usage(immdata, HUMAN_TRBJ, .coord.flip=F, .ncol = 2) ``` ## Jennsen - Shannon Divergence applied to the segments frequency of supplied data frames ### V-segments Jennsen - Shannon Divergence among repertoires ```{r vdiv, fig.width=13,warning=FALSE,message=F,prompt=FALSE} res <- js.div.seg(immdata, HUMAN_TRBV, .frame='all', .verbose = F) vis.heatmap(round(res, 5)) ``` ### V-segments Jennsen - Shannon Divergence radarplot ```{r radars, fig.width=13,warning=FALSE,message=F,prompt=FALSE} res <- js.div.seg(immdata, HUMAN_TRBV, .frame='all', .verbose = F) vis.radarlike(res, .ncol = 2) ``` ## PCA on segments' frequencies ### PCA on V-segments statistics ```{r pcav,warning=FALSE,message=F} pca.segments(immdata) ``` ### PCA on V-J segments statistics ```{r pcavj,warning=FALSE,message=F} pca.segments.2D(immdata, .genes = list(HUMAN_TRBV, HUMAN_TRBJ)) ``` ## Top cross ```{r topcross, fig.width=16, fig.height=16,warning=FALSE,message=F} top.cross.plot(top.cross(permutedf(immdata), seq(500, min(sapply(immdata, nrow)), 500), .verbose = F)) ``` ## Shared repertoire statistics by clonotypes using V-segments ```{r shared,warning=FALSE,message=F} imm.sh <- shared.repertoire(immdata, 'av', .verbose = F) shared.clones.count(imm.sh) shared.representation(imm.sh) ``` ## Rarefaction group analysis ```{r muc, fig.width=11,warning=FALSE,message=F} clmn <- 'Read.count' if (!is.na(immdata[[1]]$Umi.count[1])) { clmn <- 'Umi.count' } vis.rarefaction(rarefaction(immdata, .col = clmn, .verbose = F), list(A = c("Subj.A", "Subj.B"), B = c("Subj.C", "Subj.D")), .log = T) ```tcR/inst/doc/0000755000176200001440000000000013667551325012541 5ustar liggesuserstcR/inst/doc/tcrvignette.R0000644000176200001440000003200313667551324015217 0ustar liggesusers## ----eval=TRUE,echo=FALSE,warning=FALSE,message=FALSE------------------------- library(tcR) data(twa) data(twb) ## ----eval=FALSE,echo=TRUE----------------------------------------------------- # # Load the package and load the data. # library(tcR) # data(twa) # "twa" - list of length 4 # data(twb) # "twb" - list of length 4 # # # Explore the data. # head(twa[[1]]) # head(twb[[1]]) ## ----eval=FALSE,echo=TRUE----------------------------------------------------- # # Run help to see available alphabets. # ?genealphabets # ?genesegments # data(genesegments) ## ----eval=FALSE,echo=TRUE----------------------------------------------------- # # Parse file in "~/mitcr/immdata1.txt" as a MiTCR file. # immdata1 <- parse.file("~/mitcr_data/immdata1.txt", 'mitcr') # # equivalent to # immdata1.eq <- parse.mitcr("~/mitcr_data/immdata1.txt") # # # Parse folder with MiGEC files. # immdata <- parse.folder("~/migec_data/", 'migec') ## ----eval=TRUE, echo=TRUE----------------------------------------------------- # No D genes is available here hence "" at "D.genes" and "-1" at positions. str(twa[[1]]) str(twb[[1]]) ## ----eval=TRUE,echo=TRUE------------------------------------------------------ cloneset.stats(twb) ## ----eval=TRUE,echo=TRUE------------------------------------------------------ repseq.stats(twb) ## ----eval=TRUE,echo=TRUE------------------------------------------------------ # How many clonotypes fill up approximately clonal.proportion(twb, 25) # the 25% of the sum of values in 'Read.count'? ## ----echo=TRUE, eval=TRUE, fig=TRUE, fig.height=4, fig.width=5.5, message=FALSE, fig.align='center'---- # What accounts a proportion of the top-10 clonotypes' reads top.proportion(twb, 10) # to the overall number of reads? vis.top.proportions(twb) # Plot this proportions. ## ----eval=TRUE,echo=TRUE------------------------------------------------------ # What is a proportion of sequences which # have 'Read.count' <= 100 to the tailbound.proportion(twb, 100) # overall number of reads? ## ----eval=TRUE, echo=TRUE, fig.height=4, fig.width=6.5, fig.align='center'---- # data(twb) # Compute summary space of clones, that occupy # [0, .05) and [.05, 1] proportion. clonal.space.homeostasis(twb, c(Low = .05, High = 1)) # Use default arguments: clonal.space.homeostasis(twb[[1]]) twb.space <- clonal.space.homeostasis(twb) vis.clonal.space(twb.space) ## ----eval=TRUE,echo=TRUE------------------------------------------------------ imm.in <- get.inframes(twb) # Return all in-frame sequences from the 'twb'. # Count the number of out-of-frame sequences count.outframes(twb, 5000) # from the first 5000 sequences. ## ----eval=TRUE,echo=TRUE------------------------------------------------------ imm.in <- get.frames(twb, 'in') # Similar to 'get.inframes(twb)'. count.frames(twb[[1]], 'all') # Just return number of rows. flag <- 'out' count.frames(twb, flag, 5000) # Similar to 'count.outframes(twb, 5000)'. ## ----eval=TRUE,echo=TRUE------------------------------------------------------ cmv <- data.frame(CDR3.amino.acid.sequence = c('CASSSANYGYTF', 'CSVGRAQNEQFF', 'CASSLTGNTEAFF', 'CASSALGGAGTGELFF', 'CASSLIGVSSYNEQFF'), V.genes = c('TRBV4-1', 'TRBV4-1', 'TRBV4-1', 'TRBV4-1', 'TRBV4-1'), stringsAsFactors = F) cmv ## ----eval=TRUE,echo=TRUE------------------------------------------------------ twb <- set.rank(twb) # Case 1. cmv.imm.ex <- find.clonotypes(.data = twb[1:2], .targets = cmv[,1], .method = 'exact', .col.name = c('Read.count', 'Total.insertions'), .verbose = F) head(cmv.imm.ex) # Case 2. # Search for CDR3 sequences with hamming distance <= 1 # to the one of the cmv$CDR3.amino.acid.sequence with # matching V genes. Return ranks of found sequences. cmv.imm.hamm.v <- find.clonotypes(twb[1:3], cmv, 'hamm', 'Rank', .target.col = c('CDR3.amino.acid.sequence', 'V.gene'), .verbose = F) head(cmv.imm.hamm.v) # Case 3. # Similar to the previous example, except # using levenshtein distance and the "Read.count" column. cmv.imm.lev.v <- find.clonotypes(twb[1:3], cmv, 'lev', .target.col = c('CDR3.amino.acid.sequence', 'V.gene'), .verbose = F) head(cmv.imm.lev.v) ## ----eval=TRUE,echo=TRUE------------------------------------------------------ imm1.vs <- geneUsage(twb[[1]], HUMAN_TRBV) head(imm1.vs) imm.vs.all <- geneUsage(twb, HUMAN_TRBV) imm.vs.all[1:10, 1:4] ## ----eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7---- # Put ".dodge = F" to get distinct plot for every data frame in the given list. vis.gene.usage(twb, HUMAN_TRBJ, .main = 'twb J-usage dodge', .dodge = T) ## ----eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=6, fig.width=9---- vis.gene.usage(twb, HUMAN_TRBJ, .main = 'twb J-usage column', .dodge = F, .ncol = 2) ## ----eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7---- vis.gene.usage(imm1.vs, NA, .main = 'twb[[1]] V-usage', .coord.flip = F) ## ----eval=T, echo=TRUE, fig.align='center'------------------------------------ # Transform "0:100" to distribution with Laplace correction entropy(0:100, .laplace = 1) # (i.e., add "1" to every value before transformation). entropy.seg(twb, HUMAN_TRBV) # Compute entropy of V-segment usage for each data frame. js.div.seg(twb[1:2], HUMAN_TRBV, .verbose = F) imm.js <- js.div.seg(twb, HUMAN_TRBV, .verbose = F) vis.radarlike(imm.js, .ncol = 2) ## ----eval=TRUE, echo=TRUE, fig.align='center', fig.height=4.5, fig.width=6---- pca.segments(twb, .genes = HUMAN_TRBV) # Plot PCA results of V-segment usage. # Return object of class "prcomp" class(pca.segments(twb, .do.plot = F, .genes = HUMAN_TRBV)) ## ----eval=TRUE, echo=T, fig.align='center', warning=FALSE--------------------- # Equivalent to intersect(twb[[1]]$CDR3.nucleotide.sequence, # twb[[2]]$CDR3.nucleotide.sequence) repOverlap(twb[1:2], 'exact', 'nuc', .verbose = F) # Equivalent to intersectClonesets(twb, "n0e", .norm = T) repOverlap(twb, 'exact', 'nuc', .norm = T, .verbose = F) # Intersect by amino acid clonotypes + V genes repOverlap(twb, 'exact', 'aa', .vgene = T, .verbose = F) # Plot a heatmap of the number of shared clonotypes. vis.heatmap(repOverlap(twb, 'exact', 'aa', .vgene = T, .verbose = F), .title = 'twb - (ave)-intersection', .labs = '') ## ----eval=TRUE, echo=TRUE----------------------------------------------------- # Get logic vector of shared elements, where # elements are tuples of CDR3 nucleotide sequence and corresponding V-segment imm.1.2 <- intersectLogic(twb[[1]], twb[[2]], .col = c('CDR3.amino.acid.sequence', 'V.gene')) # Get elements which are in both twb[[1]] and twb[[2]]. head(twb[[1]][imm.1.2, c('CDR3.amino.acid.sequence', 'V.gene')]) ## ----eval=TRUE, echo=T, fig.align='center', fig.height=6.5, fig.width=10, warning=FALSE---- twb.top <- top.cross(.data = twb, .n = seq(500, 10000, 500), .verbose = F, .norm = T) top.cross.plot(twb.top) ## ----eval=TRUE, echo=TRUE, results='hold'------------------------------------- # Apply the Morisitas overlap index to the each pair of repertoires. # Use information about V genes (i.e. one CDR3 clonotype is equal to another # if and only if their CDR3 aa sequences are equal and their V genes are equal) repOverlap(twb, 'morisita', 'aa', 'read.count', .vgene = T, .verbose = F) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- # Compute shared repertoire of amino acid CDR3 sequences and V genes # which has been found in two or more people and return the Read.count column # of such clonotypes from each data frame in the input list. imm.shared <- shared.repertoire(.data = twb, .type = 'avrc', .min.ppl = 2, .verbose = F) head(imm.shared) shared.representation(imm.shared) # Number of shared sequences. ## ----eval=TRUE, echo=TRUE, results='hold'------------------------------------- # Evaluate the diversity of clones by the ecological diversity index. repDiversity(twb, 'div', 'read.count') sapply(twb, function (x) diversity(x$Read.count)) ## ----eval=TRUE, echo=TRUE, results='hold'------------------------------------- # Compute the diversity as the inverse probability of choosing two similar clonotypes. repDiversity(twb, 'inv.simp', 'read.prop') sapply(twb, function (x) inverse.simpson(x$Read.proportion)) ## ----eval=TRUE, echo=TRUE, results='hold'------------------------------------- # Evaluate the skewness of clonal distribution. repDiversity(twb, 'gini.simp', 'read.prop') sapply(twb, function (x) gini.simpson(x$Read.proportion)) ## ----eval=TRUE, echo=TRUE, results='hold'------------------------------------- # Compute diversity of repertoire using Chao index. repDiversity(twb, 'chao1', 'read.count') sapply(twb, function (x) chao1(x$Read.count)) ## ----eval=TRUE, echo=TRUE, fig.height=4, fig.width=5.5, fig.align='center'---- vis.count.len(twb[[1]], .name = "twb[[1]] CDR3 lengths", .col = "Read.count") ## ----eval=TRUE, echo=TRUE, fig.height=4, fig.width=5.5, fig.align='center', warning=FALSE, message=FALSE---- # I comment this to avoid a strange bug in ggplot2. Will uncomment later. # vis.number.count(twb[[1]], .name = "twb[[1]] count distribution") ## ----echo=TRUE, eval=TRUE, fig.height=4, fig.width=5.5, message=FALSE, fig.align='center'---- vis.top.proportions(twb, c(10, 500, 3000, 10000), .col = "Read.count") ## ----eval=TRUE, echo=TRUE, fig.height=4, fig.width=6.5, fig.align='center'---- twb.space <- clonal.space.homeostasis(twb) vis.clonal.space(twb.space) ## ----eval=TRUE, echo=TRUE, fig.align='center', warning=FALSE, message=FALSE---- twb.shared <- repOverlap(twb, "exact", .norm = F, .verbose = F) vis.heatmap(twb.shared, .title = "Twins shared nuc clonotypes", .labs = c("Sample in x", "Sample in y"), .legend = "# clonotypes") ## ----eval=T, echo=TRUE, fig.align='center'------------------------------------ twb.js <- js.div.seg(twb, HUMAN_TRBV, .verbose = F) vis.radarlike(twb.js, .ncol = 2) ## ----eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7---- vis.gene.usage(twb[[1]], HUMAN_TRBV, .main = 'Sample I V-usage') ## ----eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=7, fig.width=5---- vis.gene.usage(twb[[2]], HUMAN_TRBV, .main = 'Sample II V-usage', .coord.flip = T) ## ----eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7---- twb.jusage <- geneUsage(twb, HUMAN_TRBJ) vis.gene.usage(twb.jusage, .main = 'Twins J-usage', .dodge = T) ## ----eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=6, fig.width=9---- vis.gene.usage(twb, HUMAN_TRBJ, .main = 'Twins J-usage', .dodge = F, .ncol = 2) ## ----eval=TRUE, echo=TRUE, fig.align='center', fig.height=4.5, fig.width=6---- twb.pca <- pca.segments(twb, .do.plot = F) vis.pca(pca.segments(twb, .do.plot = F, .genes = HUMAN_TRBV), .groups = list(GroupA = c(1,2), GroupB = c(3,4))) ## ----eval=TRUE, echo=TRUE, fig.align='center', fig.width=6, fig.height=5.5, warning=FALSE, message=FALSE---- km <- get.kmers(twb[[1]]$CDR3.amino.acid.sequence, .head = 100, .k = 7, .verbose = F) d <- kmer.profile(km) vis.logo(d) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- # data(twb) twb.shared <- shared.repertoire(twb, .head = 1000, .verbose = F) G <- mutation.network(twb.shared) G ## ----eval=TRUE, echo=TRUE----------------------------------------------------- # data(twb) # twb.shared <- shared.repertoire(twb, .head = 1000) # G <- mutation.network(twb.shared) G <- set.group.vector(G, "twins", list(A = c(1,2), B = c(3,4))) # <= refactor this get.group.names(G, "twins", 1) get.group.names(G, "twins", 300) get.group.names(G, "twins", c(1,2,3), F) get.group.names(G, "twins", 300, F) # Because we have only two groups, we can assign more readable attribute. V(G)$twin.names <- get.group.names(G, "twins") V(G)$twin.names[1] V(G)$twin.names[300] ## ----eval=TRUE, echo=TRUE----------------------------------------------------- # data(twb) # twb.shared <- shared.repertoire(twb, .head = 1000) # G <- mutation.network(twb.shared) head(mutated.neighbours(G, 1)[[1]]) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- head(get.kmers(twb[[1]]$CDR3.amino.acid.sequence, 100, .meat = F, .verbose = F)) head(get.kmers(twb[[1]], .meat = T, .verbose = F)) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- revcomp(c('AAATTT', 'ACGTTTGGA')) cbind(bunch.translate(twb[[1]]$CDR3.nucleotide.sequence[1:10]), twb[[1]]$CDR3.amino.acid.sequence[1:10]) gc.content(twb[[1]]$CDR3.nucleotide.sequence[1:10]) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- codon.variants('LQ') translated.nucl.sequences(c('LQ', 'CASSLQ')) reverse.translation('LQ') translated.nucl.sequences('LQ', 'XXXXXG') codon.variants('LQ', 'XXXXXG') reverse.translation('LQ', 'XXXXXG') tcR/inst/doc/tcrvignette.Rmd0000644000176200001440000010676113667232050015545 0ustar liggesusers--- title: '

tcR: a package for T cell receptor and Immunoglobulin repertoires advanced data analysis

Vadim I. Nazarov

' author:

Laboratory of Comparative and Functional Genomics, IBCH RAS, Moscow, Russia

output: html_document: theme: spacelab toc: yes toc_depth: 4 pdf_document: toc: yes toc_depth: 4 word_document: default --- # IMPORTANT INFORMATION The tcR package WILL SOON BE ORPHANED AND REMOVED FROM CRAN. A new package is available that is designed to replace tcR: immunarch -- http://immunarch.com/ We will be happy to help you to move to the new package. Feel free to contact us: http://github.com/immunomind/immunarch Sincerely, immunarch dev team and Vadim I. Nazarov, lead developer of tcR ## Introduction The *tcR* package designed to help researchers in the immunology field to analyse T cell receptor (`TCR`) and immunoglobulin (`Ig`) repertoires. In this vignette, I will cover procedures for immune receptor repertoire analysis provided with the package. Terms: - Clonotype: a group of T / B cell clones with equal CDR3 nucleotide sequences and equal Variable genes. - Cloneset / repertoire: a set of clonotypes. Represented as a data frame in which each row corresponds to a unique clonotype. - UMI: Unique Molecular Identifier (see this [paper](http://www.nature.com/nmeth/journal/v9/n1/full/nmeth.1778.html) for details) ```{r eval=TRUE,echo=FALSE,warning=FALSE,message=FALSE} library(tcR) data(twa) data(twb) ``` ### Package features - Parsers for outputs of various tools for CDR3 extraction and genes alignment *(currently implemented parsers for MiTCR, MiGEC, VDJtools, ImmunoSEQ, IMSEQ and MiXCR)* - Data manipulation *(in-frame / out-of-frame sequences subsetting, clonotype motif search)* - Descriptive statistics *(number of reads, number of clonotypes, gene segment usage)* - Shared clonotypes statistics *(number of shared clonotypes, using V genes or not; sequential intersection among the most abundant clonotype ("top-cross"))* - Repertoire comparison *(Jaccard index, Morisita's overlap index, Horn's index, Tversky index, overlap coefficient)* - V- and J genes usage and it's analysis *(PCA, Shannon Entropy, Jensen-Shannon Divergence)* - Diversity evaluation *(ecological diversity index, Gini index, inverse Simpson index, rarefaction analysis)* - Artificial repertoire generation (beta chain only, for now) - Spectratyping - Various visualisation procedures - Mutation networks *(graphs, in which vertices represent CDR3 nucleotide / amino acid sequences and edges are connecting similar sequences with low hamming or edit distance between them)* ### Data in the package There are two datasets provided with the package - twins data and V(D)J recombination genes data. #### Downsampled twins data `twa.rda`, `twb.rda` - two lists with 4 data frames in each list. Every data frame is a sample downsampled to the 10000 most abundant clonotypes of twins data (alpha and beta chains). Full data is available here: [Twins TCR data at Laboratory of Comparative and Functional Genomics](http://labcfg.ibch.ru/tcr.html) Explore the data: ```{r eval=FALSE,echo=TRUE} # Load the package and load the data. library(tcR) data(twa) # "twa" - list of length 4 data(twb) # "twb" - list of length 4 # Explore the data. head(twa[[1]]) head(twb[[1]]) ``` #### Gene alphabets Gene alphabets - character vectors with names of genes for TCR and Ig. ```{r eval=FALSE,echo=TRUE} # Run help to see available alphabets. ?genealphabets ?genesegments data(genesegments) ``` ### Quick start / automatic report generation For the exploratory analysis of a single repertoire, use the RMarkdown report file at `"/inst/library.report.Rmd"` Analysis in the file include statistics and visualisation of number of clones, clonotypes, in- and out-of-frame sequences, unique amino acid CDR3 sequences, V- and J-usage, most frequent k-mers, rarefaction analysis. For the analysis of a group of repertoires ("cross-analysis"), use the RMarkdown report file at: `"/inst/crossanalysis.report.Rmd}"` Analysis in this file include statistics and visualisation of number of shared clones and clonotypes, V-usage for individuals and groups, J-usage for individuals, Jensen-Shannon divergence among V-usages of repertoires and top-cross. You need the *knitr* package installed in order to generate reports from default pipelines. In RStudio you can run a pipeline file as follows: `Run RStudio -> load the pipeline .Rmd files -> press the knitr button` ### Input parsing Currently in *tcR* there are implemented parser for the next software: - MiTCR - `parse.mitcr`; - MiTCR w/ UMIs - `parse.mitcrbc`; - MiGEC - `parse.migec`; - VDJtools - `parse.vdjtools`; - ImmunoSEQ - `parse.immunoseq`; - MiXCR - `parse.mixcr`; - IMSEQ - `parse.imseq`. Also a general parser `parse.cloneset` for a text table files is implemented. General wrapper for parsers is `parse.file`. User can also parse a list of files or the entire folder. Run `?parse.folder` to see a help on parsing input files and a list of functions for parsing a specific input format. ```{r eval=FALSE,echo=TRUE} # Parse file in "~/mitcr/immdata1.txt" as a MiTCR file. immdata1 <- parse.file("~/mitcr_data/immdata1.txt", 'mitcr') # equivalent to immdata1.eq <- parse.mitcr("~/mitcr_data/immdata1.txt") # Parse folder with MiGEC files. immdata <- parse.folder("~/migec_data/", 'migec') ``` ### Cloneset representation Clonesets represented in *tcR* as data frames with each row corresponding to the one nucleotide clonotype and with specific column names: - *Umi.count* - number of UMIs; - *Umi.proportion* - proportion of UMIs; - *Read.count* - number of reads; - *Read.proportion* - proportion of reads; - *CDR3.nucleotide.sequence* - CDR3 nucleotide sequence; - *CDR3.amino.acid.sequence* - CDR3 amino acid sequence; - *V.gene* - names of aligned Variable genes; - *J.gene* - names of aligned Joining genes; - *D.gene* - names of aligned Diversity genes; - *V.end* - last positions of aligned V genes (1-based); - *J.start* - first positions of aligned J genes (1-based); - *D5.end* - positions of D'5 end of aligned D genes (1-based); - *D3.end* - positions of D'3 end of aligned D genes (1-based); - *VD.insertions* - number of inserted nucleotides (N-nucleotides) at V-D junction (-1 for receptors with VJ recombination); - *DJ.insertions* - number of inserted nucleotides (N-nucleotides) at D-J junction (-1 for receptors with VJ recombination); - *Total.insertions* - total number of inserted nucleotides (number of N-nucleotides at V-J junction for receptors with VJ recombination). Any data frame with this columns and of this class is suitable for processing with the package, hence user can generate their own table files and load them for the further analysis using `read.csv`, `read.table` and other `base` R functions. Please note that *tcR* internally expects all strings to be of class "character", not "factor". Therefore you should use R parsing functions with parameter *stringsAsFactors=FALSE*. ```{r eval=TRUE, echo=TRUE} # No D genes is available here hence "" at "D.genes" and "-1" at positions. str(twa[[1]]) str(twb[[1]]) ``` ## Repertoire descriptive statistics For the exploratory analysis *tcR* provides various functions for computing descriptive statistics. ### Cloneset summary To get a general view of a subject's repertoire (overall count of sequences, in- and out-of-frames numbers and proportions) use the `cloneset.stats` function. It returns a `summary` of counts of nucleotide and amino acid clonotypes, as well as summary of read counts: ```{r eval=TRUE,echo=TRUE} cloneset.stats(twb) ``` For characterisation of a library use the `repseq.stats` function: ```{r eval=TRUE,echo=TRUE} repseq.stats(twb) ``` ### Most abundant clonotypes statistics Function `clonal.proportion` is used to get the number of most abundant by the count of reads clonotypes. E.g., compute number of clonotypes which fill up (approx.) the 25% from total repertoire's "Read.count": ```{r eval=TRUE,echo=TRUE} # How many clonotypes fill up approximately clonal.proportion(twb, 25) # the 25% of the sum of values in 'Read.count'? ``` To get a proportion of the most abundant clonotypes' sum of reads to the overall number of reads in a repertoire, use `top.proportion`, i.e. get ($\sum$ reads of top clonotypes)$/$($\sum$ reads for all clonotypes). E.g., get a proportion of the top-10 clonotypes' reads to the overall number of reads: ```{r echo=TRUE, eval=TRUE, fig=TRUE, fig.height=4, fig.width=5.5, message=FALSE, fig.align='center'} # What accounts a proportion of the top-10 clonotypes' reads top.proportion(twb, 10) # to the overall number of reads? vis.top.proportions(twb) # Plot this proportions. ``` Function `tailbound.proportion` with two arguments *.col* and *.bound* gets subset of the given data frame with clonotypes which have column *.col* with value $\leq$ *.bound* and computes the ratio of sums of count reads of such subset to the overall data frame. E.g., get proportion of sum of reads of sequences which has "Read.count" <= 100 to the overall number of reads: ```{r eval=TRUE,echo=TRUE} # What is a proportion of sequences which # have 'Read.count' <= 100 to the tailbound.proportion(twb, 100) # overall number of reads? ``` ### Clonal space homeostasis Clonal space homeostasis is a useful statistics of how many space occupied by clonotypes with specific proportions. ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=6.5, fig.align='center'} # data(twb) # Compute summary space of clones, that occupy # [0, .05) and [.05, 1] proportion. clonal.space.homeostasis(twb, c(Low = .05, High = 1)) # Use default arguments: clonal.space.homeostasis(twb[[1]]) twb.space <- clonal.space.homeostasis(twb) vis.clonal.space(twb.space) ``` ### In-frame and out-of-frame sequences Functions for performing subsetting and counting number of in-frame and out-of-frame clonotypes are: `count.inframes`, `count.outframes`, `get.inframes`, `get.outframes`. Parameter *.head* for this functions is a parameter to the *.head* function, that applied to the input data frame or an input list of data frames before subsetting. Functions accept both data frames and list of data frames as parameters. E.g., get data frame with only in-frame sequences and count out-of-frame sequences in the first 5000 rows for this data frame: ```{r eval=TRUE,echo=TRUE} imm.in <- get.inframes(twb) # Return all in-frame sequences from the 'twb'. # Count the number of out-of-frame sequences count.outframes(twb, 5000) # from the first 5000 sequences. ``` General functions with parameter stands for 'all' (all sequences), 'in' (only in-frame sequences) or 'out' (only out-of-frame sequences) are *get.frames* and *count.frames*: ```{r eval=TRUE,echo=TRUE} imm.in <- get.frames(twb, 'in') # Similar to 'get.inframes(twb)'. count.frames(twb[[1]], 'all') # Just return number of rows. flag <- 'out' count.frames(twb, flag, 5000) # Similar to 'count.outframes(twb, 5000)'. ``` ### Search for a target CDR3 sequences For exact or fuzzy search of sequences the package employed a function `find.clonotypes`. Input arguments for this function are a data frame or a list of data frames, targets (a character vector or data frame having one column with sequences and additional columns with, e.g., V genes), a value of which column or columns to return, a method to be used to compare sequences among each other (either "exact" for exact matching, "hamm" for matching sequences by Hamming distance (two sequences are matched if H $\leq$ 1) or "lev" for matching sequences by Levenshtein distance (two sequences are matched if L $\leq$ 1)), and column name from which sequences for matching are obtained. Sounds very complex, but in practice it's very easy, therefore let's go to examples. Suppose we want to search for some CDR3 sequences in a number of repertoires: ```{r eval=TRUE,echo=TRUE} cmv <- data.frame(CDR3.amino.acid.sequence = c('CASSSANYGYTF', 'CSVGRAQNEQFF', 'CASSLTGNTEAFF', 'CASSALGGAGTGELFF', 'CASSLIGVSSYNEQFF'), V.genes = c('TRBV4-1', 'TRBV4-1', 'TRBV4-1', 'TRBV4-1', 'TRBV4-1'), stringsAsFactors = F) cmv ``` We will search for them using all methods of matching (exact, hamming or levenshtein) and with and without matching by V-segment. Also, for the first case (exact matching and without V gene) we return "Total.insertions" column along with the "Read.count" column, and for the second case output will be a "Rank" - rank (generated by `set.rank`) of a clone or a clonotype in a data frame. ```{r eval=TRUE,echo=TRUE} twb <- set.rank(twb) # Case 1. cmv.imm.ex <- find.clonotypes(.data = twb[1:2], .targets = cmv[,1], .method = 'exact', .col.name = c('Read.count', 'Total.insertions'), .verbose = F) head(cmv.imm.ex) # Case 2. # Search for CDR3 sequences with hamming distance <= 1 # to the one of the cmv$CDR3.amino.acid.sequence with # matching V genes. Return ranks of found sequences. cmv.imm.hamm.v <- find.clonotypes(twb[1:3], cmv, 'hamm', 'Rank', .target.col = c('CDR3.amino.acid.sequence', 'V.gene'), .verbose = F) head(cmv.imm.hamm.v) # Case 3. # Similar to the previous example, except # using levenshtein distance and the "Read.count" column. cmv.imm.lev.v <- find.clonotypes(twb[1:3], cmv, 'lev', .target.col = c('CDR3.amino.acid.sequence', 'V.gene'), .verbose = F) head(cmv.imm.lev.v) ``` ## Gene usage Variable and Joining gene usage (V-usage and J-usage) are important characteristics of repertoires. To access and compare them among repertoires *tcR* provides a few useful functions. ### Gene usage computing To access V- and J-usage of a repertoire *tcR* provides functions `geneUsage`. Function `geneUsage`, depending on parameters, computes frequencies or counts of the given elements (e.g., V genes) of the input data frame or the input list of data frames. V and J gene names for humans for TCR and Ig are stored in the .rda file `genesegments.rda` (they are identical to those form IMGT: \href{http://www.imgt.org/IMGTrepertoire/index.php?section=LocusGenes&repertoire=nomenclatures&species=human&group=TRBV}{link to beta genes (red ones)} and \href{http://www.imgt.org/IMGTrepertoire/index.php?section=LocusGenes&repertoire=nomenclatures&species=human&group=TRAV}{link to alpha genes (red ones)}). All of the mentioned functions are accept data frames as well as list of data frames. Output for those functions are data frames with the first column stands for a gene and the other for frequencies. ```{r eval=TRUE,echo=TRUE} imm1.vs <- geneUsage(twb[[1]], HUMAN_TRBV) head(imm1.vs) imm.vs.all <- geneUsage(twb, HUMAN_TRBV) imm.vs.all[1:10, 1:4] ``` You can also directly visualise gene usage with the function `vis.gene.usage` (if you pass the gene alphabet as a second argument): ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} # Put ".dodge = F" to get distinct plot for every data frame in the given list. vis.gene.usage(twb, HUMAN_TRBJ, .main = 'twb J-usage dodge', .dodge = T) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=6, fig.width=9} vis.gene.usage(twb, HUMAN_TRBJ, .main = 'twb J-usage column', .dodge = F, .ncol = 2) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} vis.gene.usage(imm1.vs, NA, .main = 'twb[[1]] V-usage', .coord.flip = F) ``` ### Gene usage comparing To evaluate V- and J genes usage of repertoires, the package implements subroutines for two approaches to the analysis: measures from the information theory and PCA (Principal Component Analysis). #### Shannon entropy and Jensen-Shannon divergence To assess the diversity of genes usage user can use the `entropy` function. Kullback-Leibler assymetric measure (function `kl.div`) and Jensen-Shannon symmetric measure (functions `js.div` for computing JS-divergence between the given distributions and `js.div.seg` for computing JS-divergence between genes distributions of two clonesets or a list with data frames) are provided to estimate distance among gene usage of different repertoires. To visualise distances *tcR* employed the `vis.radarlike` function, see Section "Plots" for more detailed information. ```{r eval=T, echo=TRUE, fig.align='center'} # Transform "0:100" to distribution with Laplace correction entropy(0:100, .laplace = 1) # (i.e., add "1" to every value before transformation). entropy.seg(twb, HUMAN_TRBV) # Compute entropy of V-segment usage for each data frame. js.div.seg(twb[1:2], HUMAN_TRBV, .verbose = F) imm.js <- js.div.seg(twb, HUMAN_TRBV, .verbose = F) vis.radarlike(imm.js, .ncol = 2) ``` #### Principal Component Analysis (PCA) Principal component analysis (PCA) is a statistical procedure for transforming a set of observations to a set of special values for analysis. In *tcR* implemented functions `pca.segments` for performing PCA on V- or J-usage, and `pca.segments.2D` for performing PCA on VJ-usage. For plotting the PCA results see the `vis.pca` function. ```{r eval=TRUE, echo=TRUE, fig.align='center', fig.height=4.5, fig.width=6} pca.segments(twb, .genes = HUMAN_TRBV) # Plot PCA results of V-segment usage. # Return object of class "prcomp" class(pca.segments(twb, .do.plot = F, .genes = HUMAN_TRBV)) ``` ## Repertoire overlap analysis *tcR* provides a number of functions for evaluating similarity of clonesets based on shared among clonesets clonotypes and working with data frames with shared clonotypes. ### Overlap quantification The general interface to all functions for computing cloneset overlap coefficients is the `repOverlap` function. #### Number of shared clonotypes The most straightforward yet a quite effective way to evaluate similarity of two clonesets is compute the number of shared clonotypes. *tcR* adds the new function `intersectClonesets` (`repOverlap(your_data, 'exact')`) which is by default computes the number of shared clonotypes using the "CDR3.nucleotide.sequence" columns of the given data frames, but user can change target columns by using arguments *.type* or *.col*. As in the `find.clonotypes`, user can choose which method apply to the elements: exact match of elements, match by Hamming distance or match by Levenshtein distance. Logical argument *.norm* is used to perform normalisation of the number of shared clonotypes by dividing this number by multiplication of clonesets' sizes (**strongly** recommended otherwise your results will be correlating with clonesets' sizes). ```{r eval=TRUE, echo=T, fig.align='center', warning=FALSE} # Equivalent to intersect(twb[[1]]$CDR3.nucleotide.sequence, # twb[[2]]$CDR3.nucleotide.sequence) repOverlap(twb[1:2], 'exact', 'nuc', .verbose = F) # Equivalent to intersectClonesets(twb, "n0e", .norm = T) repOverlap(twb, 'exact', 'nuc', .norm = T, .verbose = F) # Intersect by amino acid clonotypes + V genes repOverlap(twb, 'exact', 'aa', .vgene = T, .verbose = F) # Plot a heatmap of the number of shared clonotypes. vis.heatmap(repOverlap(twb, 'exact', 'aa', .vgene = T, .verbose = F), .title = 'twb - (ave)-intersection', .labs = '') ``` See the `vis.heatmap` function in the Section "Visualisation" for the visualisation of the intersection results. Functions `intersectCount`, `intersectLogic` and `intersectIndices` are more flexible in terms of choosing which columns to match. They all have parameter *.col* that specifies names of columns which will used in computing intersection. Function `intersectCount` returns number of similar elements; `intersectIndices(x, y)` returns 2-column matrix with the first column stands for an index of an element in the given *x*, and the second column stands for an index of that element of *y* which is similar to a relative element in *x*; `intersectLogic(x, y)` returns a logical vector of *length(x)* or *nrow(x)*, where TRUE at position *i* means that element with index {i} has been found in the *y*. ```{r eval=TRUE, echo=TRUE} # Get logic vector of shared elements, where # elements are tuples of CDR3 nucleotide sequence and corresponding V-segment imm.1.2 <- intersectLogic(twb[[1]], twb[[2]], .col = c('CDR3.amino.acid.sequence', 'V.gene')) # Get elements which are in both twb[[1]] and twb[[2]]. head(twb[[1]][imm.1.2, c('CDR3.amino.acid.sequence', 'V.gene')]) ``` #### "Top cross" Number of shared clonotypes among the most abundant clonotypes may differ signigicantly from those with lesses count. To support research *tcR* offers the `top.cross` function, that apply `tcR::intersectClonesets`, e.g., to the first 1000 clonotypes, 2000, 3000 and so on up to the first 100000 clones, if supplied `.n == seq(1000, 100000, 1000)`. ```{r eval=TRUE, echo=T, fig.align='center', fig.height=6.5, fig.width=10, warning=FALSE} twb.top <- top.cross(.data = twb, .n = seq(500, 10000, 500), .verbose = F, .norm = T) top.cross.plot(twb.top) ``` #### More complex similarity measures *tcR* also provides more complex similarity measures for evaluating the similarity of sets. - Tversky index (`repOverlap(your_data, 'tversky')` for clonesets or `tversky.index` for vectors) is an asymmetric similarity measure on sets that compares a variant to a prototype. If using default arguments, it's similar to Dice's coefficient. - Overlap coefficient (`repOverlap(your_data, 'overlap')` for clonesets or `overlap.coef` for vectors) is a similarity measure that measures the overlap between two sets, and is defined as the size of the intersection divided by the smaller of the size of the two sets. - Jaccard index (`repOverlap(your_data, 'jaccard')` for clonesets or `jaccard.index` for vectors) is a statistic used for comparing the similarity and diversity of sample sets. - Morisita's overlap index (`repOverlap(your_data, 'morisita')` for clonesets or `morisitas.index` for other data) is a statistical measure of dispersion of individuals in a population and is used to compare overlap among samples. The formula is based on the assumption that increasing the size of the samples will increase the diversity because it will include different habitats (i.e. different faunas) (Morisita, 1959). To visualise similarity among repertoires the `vis.heatmap` function is appropriate. ```{r eval=TRUE, echo=TRUE, results='hold'} # Apply the Morisitas overlap index to the each pair of repertoires. # Use information about V genes (i.e. one CDR3 clonotype is equal to another # if and only if their CDR3 aa sequences are equal and their V genes are equal) repOverlap(twb, 'morisita', 'aa', 'read.count', .vgene = T, .verbose = F) ``` ### Overlap statistics and tests #### Overlap Z-score (OZ-score) - a measure for ???abnormality??? in overlaps `ozScore` #### Monte Carlo permutation test for pairwise and one-vs-all-wise within- and inter-group differences in a set of repertoires `permutDistTest` `pca2euclid` ### Shared repertoire To investigate a shared among a several repertoires clonotypes ("shared repertoire") the package provided the `shared.repertoire` function along with functions for computing the shared repertoire statistics. The `shared.representation` function computes the number of shared clonotypes for each repertoire for each degree of sharing (i.e., number of people, in which indicated amount of clones have been found). The function `shared.summary` is equivalent to `repOverlap(, 'exact')` but applies to the shared repertoire data frame. Measuring distances among repertoires using the cosine similarity on vector of counts of shared sequences is also possible with the `cosine.sharing` function. ```{r eval=TRUE, echo=TRUE} # Compute shared repertoire of amino acid CDR3 sequences and V genes # which has been found in two or more people and return the Read.count column # of such clonotypes from each data frame in the input list. imm.shared <- shared.repertoire(.data = twb, .type = 'avrc', .min.ppl = 2, .verbose = F) head(imm.shared) shared.representation(imm.shared) # Number of shared sequences. ``` ## Diversity evaluation For assessing the distribution of clonotypes in the given repertoire, *tcR* provides functions for evaluating the diversity (functions `diversity` and `inverse.simpson`) and the skewness of the clonal distribution (functions `gini` and `gini.simpson`), and a general interface to all of this functions `repDiversity`, which user should use to estimate the diversity of clonesets. Function `diversity` (`repDiversity(your_clonesets, "div")`) computes the ecological diversity index (with parameter `.q` for penalties for clones with large count). Function `inverse.simpson` (`repDiversity(your_clonesets, "inv.simp")`) computes the Inverse Simpson Index (i.e., inverse probability of choosing two similar clonotypes). Function `gini` (`repDiversity(your_clonesets, "gini")`) computes the economical Gini index of clonal distribution. Function `gini.simpson` (`repDiversity(your_clonesets, "gini.simp")`) computes the Gini-Simpson index. Function `chao1` (`repDiversity(your_clonesets, "chao1")`) computes the Chao1 index, its SD and two 95 perc CI. Function `repDiversity` accepts single clonesets as well as a list of clonesets. Parameter `.quant` specifies which column to use for computing the diversity (print `?repDiversity` to see more information about input arguments). ```{r eval=TRUE, echo=TRUE, results='hold'} # Evaluate the diversity of clones by the ecological diversity index. repDiversity(twb, 'div', 'read.count') sapply(twb, function (x) diversity(x$Read.count)) ``` ```{r eval=TRUE, echo=TRUE, results='hold'} # Compute the diversity as the inverse probability of choosing two similar clonotypes. repDiversity(twb, 'inv.simp', 'read.prop') sapply(twb, function (x) inverse.simpson(x$Read.proportion)) ``` ```{r eval=TRUE, echo=TRUE, results='hold'} # Evaluate the skewness of clonal distribution. repDiversity(twb, 'gini.simp', 'read.prop') sapply(twb, function (x) gini.simpson(x$Read.proportion)) ``` ```{r eval=TRUE, echo=TRUE, results='hold'} # Compute diversity of repertoire using Chao index. repDiversity(twb, 'chao1', 'read.count') sapply(twb, function (x) chao1(x$Read.count)) ``` ## Visualisation ### CDR3 length and read count distributions plot Plots of the distribution of CDR3 nucleotide sequences length (function `vis.count.len`) and the histogram of counts (function `vis.number.count`). Input data is either a data frame or a list with data frames. Argument *.col* specifies column's name with clonotype counts. Argument *.ncol* specifies a number of columns in a plot with multiple distribution, i.e., if the input data is a list with data frames. ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=5.5, fig.align='center'} vis.count.len(twb[[1]], .name = "twb[[1]] CDR3 lengths", .col = "Read.count") ``` ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=5.5, fig.align='center', warning=FALSE, message=FALSE} # I comment this to avoid a strange bug in ggplot2. Will uncomment later. # vis.number.count(twb[[1]], .name = "twb[[1]] count distribution") ``` ### Top proportions bar plot For the visualisation of proportions of the most abundant clonotypes in a repertoire *tcR* offers the `vis.top.proportions` function. As input the function receives either data frame or a list with data frames (argument *.data*), an integer vector with number of clonotypes for computing proportions of count for this clonotypes (argument *.head*), and a column's name with clonotype counts (argument *.col*). ```{r echo=TRUE, eval=TRUE, fig.height=4, fig.width=5.5, message=FALSE, fig.align='center'} vis.top.proportions(twb, c(10, 500, 3000, 10000), .col = "Read.count") ``` ### Clonal space homeostasis bar plot For the visualisation of how much space occupied each group of clonotypes, divided into groups by their proportions in the data, use the `vis.clonal.space` function. As an input it receives the output of the `clonal.space.homeostasis` function. ```{r eval=TRUE, echo=TRUE, fig.height=4, fig.width=6.5, fig.align='center'} twb.space <- clonal.space.homeostasis(twb) vis.clonal.space(twb.space) ``` ### Heat map Pairwise distances or similarity of repertoires can be represented as qudratic matrices, in which each row and column represented a cloneset, and each value in every cell (i, j) is a distance between repertoires with indices i and j. One way to visalise such matrices is using "heatmaps". For plotting heatmaps in *tcR* implemented the `vis.heatmap` function. With changing input arguments user can change names of labs, title and legend. ```{r eval=TRUE, echo=TRUE, fig.align='center', warning=FALSE, message=FALSE} twb.shared <- repOverlap(twb, "exact", .norm = F, .verbose = F) vis.heatmap(twb.shared, .title = "Twins shared nuc clonotypes", .labs = c("Sample in x", "Sample in y"), .legend = "# clonotypes") ``` ### Radar-like plot Another way to repsent distances among objects is "radar-like" plots (because this plots is not exactly radar plots) realised in *tcR* throught the `vis.radarlike` function. Argument *.ncol* specifies a number of columns of radar-like plots in a viewport. ```{r eval=T, echo=TRUE, fig.align='center'} twb.js <- js.div.seg(twb, HUMAN_TRBV, .verbose = F) vis.radarlike(twb.js, .ncol = 2) ``` ### Gene usage histogram For the visualisation of gene usage *tcR* employes subroutines for making classical histograms using the `vis.gene.usage` function. The function accept clonesets, lists of clonesets or output from the `geneUsage` function. If input is a cloneset(s), then user should specify a gene alphabet (e.g., `HUMAN_TRBV`) in order to compute the gene usage. Using a parameter \code{.dodge}, user can change type of the output between an output as histograms for each cloneset in the input list (`.dodge = F`) or an output as an one histogram for all data, which is very useful for comparing distribution of genes (`.dodge = T`). If `.dodge=F` and input are lists of clonesets or a gene usage of a few clonesets, than user with argument `.ncol` can specify how many columns of histograms will be outputted. With `.coord.flip` user can flip coordinates so genes will be at the left side of the plot. ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} vis.gene.usage(twb[[1]], HUMAN_TRBV, .main = 'Sample I V-usage') ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=7, fig.width=5} vis.gene.usage(twb[[2]], HUMAN_TRBV, .main = 'Sample II V-usage', .coord.flip = T) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=5, fig.width=7} twb.jusage <- geneUsage(twb, HUMAN_TRBJ) vis.gene.usage(twb.jusage, .main = 'Twins J-usage', .dodge = T) ``` ```{r eval=TRUE, echo=TRUE, message=FALSE, fig.align='center', fig.height=6, fig.width=9} vis.gene.usage(twb, HUMAN_TRBJ, .main = 'Twins J-usage', .dodge = F, .ncol = 2) ``` ### PCA visualisation For the visualisation of results from the `prcomp` function (i.e., objects of class `prcomp`), *tcR* provides the `vis.pca` function. Input arguments for the function are an object of class `prcomp` and a (if needed) list with groups (vectors of indices of samples) for colouring points in the plot. ```{r eval=TRUE, echo=TRUE, fig.align='center', fig.height=4.5, fig.width=6} twb.pca <- pca.segments(twb, .do.plot = F) vis.pca(pca.segments(twb, .do.plot = F, .genes = HUMAN_TRBV), .groups = list(GroupA = c(1,2), GroupB = c(3,4))) ``` ### Logo-like plot Logo-like graphs for visualisation of nucleotide or amino acid motif sequences / profiles. ```{r eval=TRUE, echo=TRUE, fig.align='center', fig.width=6, fig.height=5.5, warning=FALSE, message=FALSE} km <- get.kmers(twb[[1]]$CDR3.amino.acid.sequence, .head = 100, .k = 7, .verbose = F) d <- kmer.profile(km) vis.logo(d) ``` ## Mutation networks Mutation network (or a mutation graph) is a graph with vertices representing nucleotide or in-frame amino acid sequences (out-of-frame amino acid sequences will be automatically filtered out by *tcR* functions for mutation network creating) and edges which connecting pairs of sequences with hamming distance (parameter *.method* = 'hamm') or edit distance (parameter *.method* = 'lev') between them no more than specified in the *.max.errors* function parameter of the `mutation.network` function. To create a mutation network first what you need is to make a shared repertoires and then apply the `mutation.network` function to this shared repertoire: ```{r eval=TRUE, echo=TRUE} # data(twb) twb.shared <- shared.repertoire(twb, .head = 1000, .verbose = F) G <- mutation.network(twb.shared) G ``` To manipulate vertex attributes functions \code{set.group.vector} and \code{get.group.names} are provided. ```{r eval=TRUE, echo=TRUE} # data(twb) # twb.shared <- shared.repertoire(twb, .head = 1000) # G <- mutation.network(twb.shared) G <- set.group.vector(G, "twins", list(A = c(1,2), B = c(3,4))) # <= refactor this get.group.names(G, "twins", 1) get.group.names(G, "twins", 300) get.group.names(G, "twins", c(1,2,3), F) get.group.names(G, "twins", 300, F) # Because we have only two groups, we can assign more readable attribute. V(G)$twin.names <- get.group.names(G, "twins") V(G)$twin.names[1] V(G)$twin.names[300] ``` To access neighbour vertices of vertices ("ego-network") use the \code{mutation.neighbours} function: ```{r eval=TRUE, echo=TRUE} # data(twb) # twb.shared <- shared.repertoire(twb, .head = 1000) # G <- mutation.network(twb.shared) head(mutated.neighbours(G, 1)[[1]]) ``` ## Conclusion Feel free to contact me for the package-related or immunoinformatics research-related questions. ## Appendix A: Kmers manipulation and processing In the package implemented functions for working with k-mers. Function `get.kmers` generates k-mers from the given chatacter vector or a data frame with columns for sequences and a count for each sequence. ```{r eval=TRUE, echo=TRUE} head(get.kmers(twb[[1]]$CDR3.amino.acid.sequence, 100, .meat = F, .verbose = F)) head(get.kmers(twb[[1]], .meat = T, .verbose = F)) ``` ## Appendix B: Nucleotide and amino acid sequences manipulation The package also provides a several number of functions for performing classic bioinformatics tasks on strings. For more powerful subroutines see the Bioconductor's *Biostrings* package. ### Nucleotide sequence manipulation Functions for basic nucleotide sequences manipulations: reverse-complement, translation and GC-content computation. All functions are vectorised. ```{r eval=TRUE, echo=TRUE} revcomp(c('AAATTT', 'ACGTTTGGA')) cbind(bunch.translate(twb[[1]]$CDR3.nucleotide.sequence[1:10]), twb[[1]]$CDR3.amino.acid.sequence[1:10]) gc.content(twb[[1]]$CDR3.nucleotide.sequence[1:10]) ``` ### Reverse translation subroutines Function `codon.variants` returns a list of vectors of nucleotide codons for each letter for each input amino acid sequence. Function `translated.nucl.sequences` returns the number of nucleotide sequences, which, when translated, will result in the given amino acid sequence(s). Function `reverse.translation` return all nucleotide sequences, which is translated to the given amino acid sequences. Optional argument `.nucseq` for each of this function provides restriction for nucleotides, which cannot be changed. All functions are vectorised. ```{r eval=TRUE, echo=TRUE} codon.variants('LQ') translated.nucl.sequences(c('LQ', 'CASSLQ')) reverse.translation('LQ') translated.nucl.sequences('LQ', 'XXXXXG') codon.variants('LQ', 'XXXXXG') reverse.translation('LQ', 'XXXXXG') ``` tcR/inst/CITATION0000644000176200001440000000110113667026401013112 0ustar liggesusersbibentry('Article', title = 'tcR: an R package for T-cell receptor repertoire data analysis', author = as.person("Vadim I. Nazarov [aut], Mikhail V. Pogorelyy [aut], Ekaterina A. Komech [aut], Ivan V. Zvyagin [aut], Dmitry A. Bolotin [aut], Mikhail Shugay [aut], Dmitry M. Chudakov [aut], Yury B. Lebedev and Ilgar Z. Mamedov [aut]"), year = 2015, url = "http://www.biomedcentral.com/1471-2105/16/175", journal = "BMC Bioinformatics", pages = 175, volume = 16, doi = "10.1186/s12859-015-0613-1" )tcR/inst/library.report.Rmd0000644000176200001440000000573213667026401015415 0ustar liggesusers--- title: "TCR beta repertoire exploratory analysis" output: html_document: theme: spacelab toc: yes --- ## Before analysis Before running this pipeline you should do next steps: 1. Save your parsed MiTCR data to the `immdata` variable (it could be **a mitcr data frame or a list**). ``` immdata <- parse.folder('/home/username/mitcrdata/') ``` 2. Save the `immdata` variable to the some folder as the `.rda` file. ``` save(immdata, file = '/home/username/immdata.rda') ``` 3. In the code block below change the path string to the path to yours `immdata.rda` file. After that click the **Knit HTML** button to start analysis and the script will make a html report. ```{r loaddata,warning=FALSE,message=F} load('../data/twb.rda') immdata <- twb[1:2] library(tcR) ``` 4. Friendly advice: run the pipeline on the top N sequences first and configure sizes of figures. ```{r lapplyhead,warning=FALSE,message=F} N <- 50000 immdata <- head(immdata, N) ``` ## Data statistics ```{r seqstats,warning=FALSE,message=F} cloneset.stats(immdata) repseq.stats(immdata) ``` ## Segments' statistics ### V-segment usage ```{r vusagehist, fig.width=16, fig.height=5,warning=FALSE,message=F} if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.gene.usage(immdata[[i]], HUMAN_TRBV, .main = paste0(names(immdata)[i], ' ', 'V-usage'))) } } else { vis.gene.usage(immdata, HUMAN_TRBV, .coord.flip=F) } ``` ### J-segment usage ```{r jusagehist, fig.width=14, fig.height=5,warning=FALSE,message=F} if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.gene.usage(immdata[[i]], HUMAN_TRBJ, .main = paste0(names(immdata)[i], ' ', 'J-usage'))) } } else { vis.gene.usage(immdata, HUMAN_TRBJ, .coord.flip=F) } ``` ## CDR3 length and read distributions ```{r cdr3len, fig.width=10, fig.height=5,warning=FALSE,message=F} if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.count.len(immdata[[i]], .name = paste0(names(immdata)[i], ' ', 'CDR3 length'))) } } else { vis.count.len(immdata) } ``` ```{r readdistr, fig.width=10, fig.height=5,warning=FALSE,message=F} if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.number.count(immdata[[i]], .name = paste0(names(immdata)[i], ' ', 'read histogram'))) } } else { vis.number.count(immdata) } ``` ## Proportions of the most abundant clones ```{r topprop, fig.width=13,warning=FALSE,message=F} vis.top.proportions(immdata) ``` ## Most frequent kmers ```{r kmers, fig.width=14,warning=FALSE,message=F} kms <- get.kmers(immdata, .verbose = F) vis.kmer.histogram(kms, .position = 'fill') ``` ## Rarefaction analysis ```{r muc, fig.width=11,warning=FALSE,message=F} clmn <- 'Read.count' if (has.class(immdata, 'list')) { if (!is.na(immdata[[1]]$Umi.count[1])) { clmn <- 'Umi.count' } } else { if (!is.na(immdata$Umi.count[1])) { clmn <- 'Umi.count' } } vis.rarefaction(rarefaction(immdata, .col = clmn, .verbose = F), .log = T) ```