metagenomeSeq/DESCRIPTION0000644000175200017520000000321514735615227016111 0ustar00biocbuildbiocbuildPackage: metagenomeSeq Title: Statistical analysis for sparse high-throughput sequencing Version: 1.48.1 Date: 2025-01-02 Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. License: Artistic-2.0 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay, IHW Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench VignetteBuilder: knitr URL: https://github.com/nosson/metagenomeSeq/ BugReports: https://github.com/nosson/metagenomeSeq/issues biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software RoxygenNote: 7.1.0 git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: RELEASE_3_20 git_last_commit: cb424ed git_last_commit_date: 2025-01-02 Repository: Bioconductor 3.20 Date/Publication: 2025-01-02 NeedsCompilation: no Packaged: 2025-01-02 23:00:39 UTC; biocbuild metagenomeSeq/MD50000644000175200017520000002157514735615227014724 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21e615bc616407bece667ada9bb13b6f *vignettes/metagenomeSeq.bib d8278b552cab2d0bf32652b99a96974f *vignettes/metagenomeSeq_figure1.png ebd30b8892f7867d2ce1bd7614718e3b *vignettes/metagenomeSeq_figure2.png 88866dace9b4330ac9cf728c55dceb88 *vignettes/overview.pdf metagenomeSeq/NAMESPACE0000644000175200017520000000405514710220170015603 0ustar00biocbuildbiocbuildimport(Biobase) import(RColorBrewer) import(limma) import(glmnet) import(methods) import(Wrench) importFrom(parallel,makeCluster) importFrom(parallel,stopCluster) importFrom(parallel,parRapply) importFrom(parallel,mclapply) importFrom(matrixStats,colQuantiles) importFrom(matrixStats,rowSds) importFrom(gplots,heatmap.2) importFrom(foreach,'%dopar%') importFrom(foreach,foreach) importFrom(Matrix,bdiag) importFrom("graphics", "abline", "axis", "lines", "plot", "points", "polygon") importFrom("grDevices", "col2rgb", "rgb") importFrom("stats", "approx", "approxfun", "binomial", "cmdscale", "coefficients", "cor", "cor.test", "density", "dist", "dnorm", "fisher.test", "glm.fit", "hclust", "lm.fit", "median", "model.matrix", "p.adjust", "plogis", "pnorm", "prcomp", "predict", "qlogis", "quantile", "residuals", "sd", "var") importFrom("utils", "packageVersion", "read.delim", "read.table", "tail") exportClasses( "MRexperiment", "fitFeatureModelResults", "fitZigResults") exportMethods( "[", "colSums", "rowSums", "colMeans", "rowMeans", "normFactors", "normFactors<-", "libSize", "libSize<-", "MRihw" ) export( aggregateByTaxonomy, aggTax, aggregateBySample, aggSamp, biom2MRexperiment, calculateEffectiveSamples, calcNormFactors, correlationTest, correctIndices, cumNorm, cumNormMat, cumNormStat, cumNormStatFast, expSummary, exportMat, exportStats, fitDO, fitMeta, fitFeatureModel, fitLogNormal, fitPA, fitMultipleTimeSeries, fitSSTimeSeries, fitTimeSeries, fitZig, filterData, load_biom, load_meta, load_metaQ, load_phenoData, loadBiom, loadMeta, loadMetaQ, loadPhenoData, makeLabels, mergeMRexperiments, MRcoefs, MRcounts, MRfulltable, MRtable, MRexperiment2biom, plotBubble, plotCorr, plotGenus, plotMRheatmap, plotOTU, plotOrd, plotRare, plotFeature, plotTimeSeries, plotClassTimeSeries, uniqueFeatures, returnAppropriateObj, ssFit, ssIntervalCandidate, ssPerm, ssPermAnalysis, ts2MRexperiment, trapz, zigControl, newMRexperiment, posteriorProbs, wrenchNorm ) metagenomeSeq/NEWS0000644000175200017520000001106114710220170015056 0ustar00biocbuildbiocbuildversion 1.25.xx (2019) + Added 'wrenchNorm' function + Added option to use IHW as p-value adustment method in 'MRcoefs' + Modified 'expSummary' slot in 'MRexperiment' object to be of class 'list' instead of 'environment' + Added results classes for 'fitZig' and 'fitFeatureModel' results version 1.21.xx (2018) + Numerous changes. Added greater flexibility to fitFeatureModel version 1.15.xx (2016) + Added 'mergeMRexperiment' function + Added 'normFactors' and 'libSize' generics + Added 'fitMultipleTimeSeries' function + Replaced RUnit with testthat library for unit testing + Adding multiple upgrades and changes throughout + Deprecated the load_* functions and created load* function. version 1.13.xx (2015) + Upgrade support for biom-format vs. 2.0 + Fixed issue - "MRtable, etc will report NA rows when user requests more features than available" + Fixed s2 miscalculation in calcZeroComponent version 1.11.xx (2015) + Adding fitFeatureModel - a feature based zero-inflated log-normal model. + Added MRcoefs,MRtable,MRfulltable support for fitFeatureModel output. + Added mention in vignette. + Added support for normalizing matrices instead of just MRexperiment objects. + Fixed cumNormStat's non-default qFlag option version 1.9.xx (2015) + Added flexibility in formula choice for fitTimeSeries + Added readability in ssPermAnalysis + Fixed default in plotClassTimeSeries (include = c("1",...)) + Added fitTimeSeries vignette + Removed interactiveDisplay to namespace - moved to suggests + Fixed ordering of MRtable,MRfulltable first four columns + modified df estimated through responsibilities + renamed fitMeta to fitLogNormal - a more appropriate name version 1.7.xx (2014-05-07) + Added function plotBubble + Added parallel (multi-core) options to fitPA, fitDO + Fixed bug for fitMeta when useCSSoffset=FALSE and model matrix ncol==2 + (1.7.10) Updated default quantile estimate (.5) for low estimates + (1.7.10) Added short description on how to do multiple group comparisons + (1.7.15) Output of fitZig (eb) is now a result of limma::eBayes instead of limma::ebayes + (1.7.16) plotMRheatmap allows for sorting by any stat (not just sd) + (1.7.18) fitTimeSeries Including times series method for differentially abundant time intervals + (1.7.20) Fixed minor bug for OTU level time series analyses and added plotClassTimeSeries + (1.7.26) Added warning / fix if any samples are empty in cumNormStat + (1.7.27) Added a few unit tests + (1.7.29) Added interactiveDisplay to namespace (display function allows interactive exploration / plots through browser) version 1.5.xx (2014-04-17) + Incorporating biom-format support with the biom2MRexperiment, MRexperiment2biom and load_biome function. + Added uniqueFeatures, filterData, aggregateByTaxonomy / aggTax, plotFeature and calculateEffectiveSamples functions. + Renamed MRfisher to fitPA (presence-absence fisher test). + Added warnings for normalization + Added fitDO (Discovery odds ratio test) and fitMeta (original metastats). + Added match.call() info to fitZig output + Fixed missing E-Step bounds version 1.2.xx (2013-08-20) + Our paper got accepted and is available! + Added methods for MRexperiment objects (colSums,colMeans,rowSums,rowMeans, usage is for example colSums(obj) or colSums(obj,norm=TRUE)) (09-25) + Added two new functions, plotOrd and plotRare - a function to plot PCA/MDS coordinates and rarefaction effect (09-04,09-18) + Updated MRfisher to include thresholding for presence-absence testing (08-19) + Updated comments (roxygen2) style for all the functions using the Rd2roxygen package (07-13) + Updated plotCorr and plotMRheatmap to allow various colors/not require trials(07-13) + Rewrote vignette (and switched to knitr) version 1.1.xx (last update 2013-06-25) + Rewrote load_meta and load_metaQ to be faster/use less memory + Modified cumNormStat to remove NA samples from calculations (example would be samples without any counts) + Re-added plotGenus' jitter + Fixed uniqueNames call in the MR tables + Changed thanks to Kasper Daniel Hansen's suggestions the following: plotOTU and plotGenus both have much better auto-generated axis MRtable, MRfulltable, MRcoefs have a sort by p-value option now MRtable, MRfulltable, MRcoefs now have an extra option to include unique numbers for OTU features (default would automatically add them previously) cumNorm.R - now returns the object as well - not just replacing the environment 0 Still need to turn the fitZig output to S3, consider subsetting function address low p-values version 1.0.0: (2013-03-29) + Release! metagenomeSeq/R/0000755000175200017520000000000014735552263014603 5ustar00biocbuildbiocbuildmetagenomeSeq/R/MRcoefs.R0000644000175200017520000001133014710220170016240 0ustar00biocbuildbiocbuild#' Table of top-ranked features from fitZig or fitFeatureModel #' #' Extract a table of the top-ranked features from a linear model fit. This #' function will be updated soon to provide better flexibility similar to #' limma's topTable. #' #' #' @param obj Output of fitFeatureModel or fitZig. #' @param by Column number or column name specifying which coefficient or #' contrast of the linear model is of interest. #' @param coef Column number(s) or column name(s) specifying which coefficient #' or contrast of the linear model to display. #' @param number The number of bacterial features to pick out. #' @param taxa Taxa list. #' @param uniqueNames Number the various taxa. #' @param adjustMethod Method to adjust p-values by. Default is "FDR". Options #' include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", #' "none". See \code{\link{p.adjust}} for more details. Additionally, options using #' independent hypothesis weighting (IHW) are available. See \code{\link{MRihw}} for more #' details. #' @param alpha Value for p-value significance threshold when running IHW. #' The default is set to 0.1 #' @param group One of five choices, 0,1,2,3,4. 0: the sort is ordered by a #' decreasing absolute value coefficient fit. 1: the sort is ordered by the raw #' coefficient fit in decreasing order. 2: the sort is ordered by the raw #' coefficient fit in increasing order. 3: the sort is ordered by the p-value #' of the coefficient fit in increasing order. 4: no sorting. #' @param eff Filter features to have at least a "eff" quantile or number of effective samples. #' @param numberEff Boolean, whether eff should represent quantile (default/FALSE) or number. #' @param counts Filter features to have at least 'counts' counts. #' @param file Name of output file, including location, to save the table. #' @return Table of the top-ranked features determined by the linear fit's #' coefficient. #' @seealso \code{\link{fitZig}} \code{\link{fitFeatureModel}} \code{\link{MRtable}} \code{\link{MRfulltable}} #' @examples #' #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' lungTrim=filterData(lungTrim,present=30) #' lungTrim=cumNorm(lungTrim,p=0.5) #' smokingStatus = pData(lungTrim)$SmokingStatus #' mod = model.matrix(~smokingStatus) #' fit = fitZig(obj = lungTrim,mod=mod) #' head(MRcoefs(fit)) #' #### #' fit = fitFeatureModel(obj = lungTrim,mod=mod) #' head(MRcoefs(fit)) #' MRcoefs<-function(obj,by=2,coef=NULL,number=10,taxa=obj@taxa, uniqueNames=FALSE,adjustMethod="fdr",alpha=0.1, group=0,eff=0,numberEff=FALSE,counts=0,file=NULL){ if(length(grep("fitFeatureModel",obj@call))){ groups = factor(obj@design[,by]) by = "logFC"; coef = 1:2; tb = data.frame(logFC=obj@fitZeroLogNormal$logFC,se=obj@fitZeroLogNormal$se) p = obj@pvalues } else { tb = obj@fit$coefficients if(is.null(coef)){ coef = 1:ncol(tb) } p=obj@eb$p.value[,by] groups = factor(obj@fit$design[,by]) if(eff>0){ effectiveSamples = calculateEffectiveSamples(obj) if(numberEff == FALSE){ valid = which(effectiveSamples>=quantile(effectiveSamples,p=eff,na.rm=TRUE)) } else { valid = which(effectiveSamples>=eff) } } } tx = as.character(taxa) if(uniqueNames==TRUE){ for (nm in unique(tx)) { ii=which(tx==nm) tx[ii]=paste(tx[ii],seq_along(ii),sep=":") } } # adding 'ihw' as pvalue adjustment method if (adjustMethod == "ihw-ubiquity" | adjustMethod == "ihw-abundance") { # use IHW to adjust pvalues padj = MRihw(obj, p, adjustMethod, alpha) } else { # use classic pvalue adjusment method padj = p.adjust(p, method = adjustMethod) } if(group==0){ srt = order(abs(tb[,by]),decreasing=TRUE) } else if(group==1){ srt = order((tb[,by]),decreasing=TRUE) } else if(group==2){ srt = order((tb[,by]),decreasing=FALSE) } else if(group==3){ srt = order(p,decreasing=FALSE) } else { srt = 1:length(padj); } valid = 1:length(padj); if(counts>0){ np=rowSums(obj@counts); valid = intersect(valid,which(np>=counts)); } srt = srt[which(srt%in%valid)][1:min(number,nrow(tb))]; mat = cbind(tb[,coef],p) mat = cbind(mat,padj) rownames(mat) = tx; mat = mat[srt,] nm = c(colnames(tb)[coef],"pvalues","adjPvalues") colnames(mat) = nm if(!is.null(file)){ nm = c("Taxa",nm) mat2 = cbind(rownames(mat),mat) mat2 = rbind(nm,mat2) write(t(mat2),ncolumns=ncol(mat2),file=file,sep="\t") } return(as.data.frame(mat)) } metagenomeSeq/R/MRexperiment2biom.R0000644000175200017520000000603114710220170020254 0ustar00biocbuildbiocbuild#' MRexperiment to biom objects #' #' Wrapper to convert MRexperiment objects to biom objects. #' #' @param obj The MRexperiment object. #' @param id Optional id for the biom matrix. #' @param norm normalize count table #' @param log log2 transform count table #' @param sl scaling factor for normalized counts. #' @param qiimeVersion Format fData according to QIIME specifications (assumes only taxonomy in fData). #' @return A biom object. #' @seealso \code{\link{loadMeta}} \code{\link{loadPhenoData}} \code{\link{newMRexperiment}} \code{\link{loadBiom}} \code{\link{biom2MRexperiment}} MRexperiment2biom <- function(obj,id=NULL,norm=FALSE,log=FALSE,sl=1000,qiimeVersion=TRUE){ requireNamespace("biomformat") id = id format = "Biological Observation Matrix 1.0.0-dev" format_url = "http://biom-format.org/documentation/format_versions/biom-1.0.html" type = "OTU table" generated_by = sprintf("metagenomeSeq %s",packageVersion("metagenomeSeq")) date = as.character(Sys.time()) matrix_type = "dense" matrix_element_type = "int" if( (norm==TRUE) | (log == TRUE) ) { matrix_element_type = "float" } data = MRcounts(obj,norm=norm,log=log,sl=sl) shape = dim(data) rows = metadata(fData(obj),qiimeVersion=qiimeVersion) columns= metadata(pData(obj)) data = as.list(as.data.frame(t(data))) names(data) <- NULL biomlist = list(id=id,format=format,format_url=format_url,type=type,generated_by=generated_by, date=date,matrix_type=matrix_type,matrix_element_type=matrix_element_type,shape=shape, rows=rows,columns=columns,data=data) biomformat::biom(biomlist) } metadata <- function(df,qiimeVersion=FALSE){ if(ncol(df)>0){ for(i in 1:ncol(df)){ df[,i] = as.character(df[,i]) } } if(qiimeVersion==TRUE){ if(ncol(df)==0){ meta = lapply(1:nrow(df),function(i){ ll = list(id=rownames(df)[i],metadata=NULL) ll }) } else { meta = lapply(1:nrow(df),function(i){ ll = list(id=rownames(df)[i], metadata=list("taxonomy" = paste(df[i,]))) NAvalues = grep("NA$",ll$metadata$taxonomy) if(length(NAvalues)>0){ k = NAvalues[1] ll$metadata$taxonomy = paste(df[i,1:(k-1)]) } ll }) } return(meta) } else { if(ncol(df)==0){ meta = lapply(1:nrow(df),function(i){ ll = list(id=rownames(df)[i],metadata=NULL) ll }) } else { meta = lapply(1:nrow(df),function(i){ ll = list(id=rownames(df)[i], metadata=lapply(1:ncol(df), function(j){as.character(df[i,j])})) names(ll$metadata) = colnames(df) ll }) } return(meta) } } metagenomeSeq/R/MRfulltable.R0000644000175200017520000001172514710220170017123 0ustar00biocbuildbiocbuild#' Table of top microbial marker gene from linear model fit including sequence #' information #' #' Extract a table of the top-ranked features from a linear model fit. This #' function will be updated soon to provide better flexibility similar to #' limma's topTable. This function differs from \code{link{MRcoefs}} in that it #' provides other information about the presence or absence of features to help #' ensure significant features called are moderately present. #' #' #' @param obj Output of fitFeatureModel or fitZig. #' @param by Column number or column name specifying which coefficient or #' contrast of the linear model is of interest. #' @param coef Column number(s) or column name(s) specifying which coefficient #' or contrast of the linear model to display. #' @param number The number of bacterial features to pick out. #' @param taxa Taxa list. #' @param uniqueNames Number the various taxa. #' @param adjustMethod Method to adjust p-values by. Default is "FDR". Options #' include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", #' "none". See \code{\link{p.adjust}} for more details. #' @param group One of five choices: 0,1,2,3,4. 0: the sort is ordered by a #' decreasing absolute value coefficient fit. 1: the sort is ordered by the raw #' coefficient fit in decreasing order. 2: the sort is ordered by the raw #' coefficient fit in increasing order. 3: the sort is ordered by the p-value #' of the coefficient fit in increasing order. 4: no sorting. #' @param eff Filter features to have at least a "eff" quantile or number of effective samples. #' @param numberEff Boolean, whether eff should represent quantile (default/FALSE) or number. #' @param ncounts Filter features to those with at least 'counts' counts. #' @param file Name of output file, including location, to save the table. #' @return Table of the top-ranked features determined by the linear fit's #' coefficient. #' @seealso \code{\link{fitZig}} \code{\link{fitFeatureModel}} \code{\link{MRcoefs}} \code{\link{MRtable}} #' \code{\link{fitPA}} #' @examples #' #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' lungTrim=filterData(lungTrim,present=30) #' lungTrim=cumNorm(lungTrim,p=0.5) #' smokingStatus = pData(lungTrim)$SmokingStatus #' mod = model.matrix(~smokingStatus) #' fit = fitZig(obj = lungTrim,mod=mod) #' head(MRfulltable(fit)) #' #### #' fit = fitFeatureModel(obj = lungTrim,mod=mod) #' head(MRfulltable(fit)) #' MRfulltable<-function(obj,by=2,coef=NULL,number=10,taxa=obj@taxa, uniqueNames=FALSE,adjustMethod="fdr",group=0,eff=0,numberEff=FALSE,ncounts=0,file=NULL){ if(length(grep("fitFeatureModel",obj@call))){ groups = factor(obj@design[,by]) by = "logFC"; coef = 1:2; tb = data.frame(logFC=obj@fitZeroLogNormal$logFC,se=obj@fitZeroLogNormal$se) p = obj@pvalues } else { tb = obj@fit$coefficients if(is.null(coef)){ coef = 1:ncol(tb) } p=obj@eb$p.value[,by] groups = factor(obj@fit$design[,by]) if(eff>0){ effectiveSamples = calculateEffectiveSamples(obj) if(numberEff == FALSE){ valid = which(effectiveSamples>=quantile(effectiveSamples,p=eff,na.rm=TRUE)) } else { valid = which(effectiveSamples>=eff) } } } tx = as.character(taxa) if(uniqueNames==TRUE){ for (nm in unique(tx)) { ii=which(tx==nm) tx[ii]=paste(tx[ii],seq_along(ii),sep=":") } } padj = p.adjust(p,method=adjustMethod) cnts = obj@counts yy = cnts>0 pa = matrix(unlist(fitPA(obj@counts,groups)),ncol=5) np0 = rowSums(yy[,groups==0]) np1 = rowSums(yy[,groups==1]) nc0 = rowSums(cnts[,groups==0]) nc1 = rowSums(cnts[,groups==1]) if(group==0){ srt = order(abs(tb[,by]),decreasing=TRUE) } else if(group==1){ srt = order((tb[,by]),decreasing=TRUE) } else if(group==2){ srt = order((tb[,by]),decreasing=FALSE) } else if(group==3){ srt = order(p,decreasing=FALSE) } else { srt = 1:length(padj) } valid = 1:length(padj) if(ncounts>0){ np=rowSums(cbind(np0,np1)) valid = intersect(valid,which(np>=ncounts)) } srt = srt[which(srt%in%valid)][1:min(number,nrow(tb))] mat = cbind(np0,np1) mat = cbind(mat,nc0) mat = cbind(mat,nc1) mat = cbind(mat,pa) mat = cbind(mat,tb[,coef]) mat = cbind(mat,p) mat = cbind(mat,padj) rownames(mat) = tx mat = mat[srt,] nm = c("+samples in group 0","+samples in group 1","counts in group 0", "counts in group 1",c("oddsRatio","lower","upper","fisherP","fisherAdjP"), colnames(tb)[coef],"pvalues","adjPvalues") colnames(mat) = nm if(!is.null(file)){ nm = c("Taxa",nm) mat2 = cbind(rownames(mat),mat) mat2 = rbind(nm,mat2) write(t(mat2),ncolumns=ncol(mat2),file=file,sep="\t") } return(as.data.frame(mat)) } metagenomeSeq/R/MRihw.R0000644000175200017520000000601514710220170015734 0ustar00biocbuildbiocbuild#' MRihw runs IHW within a MRcoefs() call #' #' Function used in MRcoefs() when "IHW" is set as the p value adjustment method #' #' @rdname MRihw #' @param obj Either a fitFeatureModelResults or fitZigResults object #' @param ... other parameters #' setGeneric("MRihw", function(obj, ...){standardGeneric("MRihw")}) #' MRihw runs IHW within a MRcoefs() call #' #' Function used in MRcoefs() when "IHW" is set as the p value adjustment method #' #' @rdname MRihw-fitFeatureModelResults #' @param obj Either a fitFeatureModelResults or fitZigResults object #' @param p a vector of pvalues extracted from obj #' @param adjustMethod Value specifying which adjustment method and which covariate to use for IHW pvalue adjustment. #' For obj of class \code{\link{fitFeatureModelResults-class}}, options are "ihw-abundance" (median feature count per row) #' and "ihw-ubiquity" (number of non-zero features per row). For obj of class \code{\link{fitZigResults-class}}, #' options are "ihw-abundance" (weighted mean per feature) and "ihw-ubiquity" (number of non-zero features per row). #' @param alpha pvalue significance level specified for IHW call. Default is 0.1 #' setMethod("MRihw", signature = "fitFeatureModelResults", function(obj, p, adjustMethod, alpha){ if (adjustMethod == "ihw-ubiquity") { # set covariate to be num of non-zero elements per row p <- obj@pvalues covariate <- rowSums(obj@counts != 0) ihwRes <- IHW::ihw(p, covariate, alpha) padj <- ihwRes@df$adj_pvalue } if (adjustMethod == "ihw-abundance"){ # use feature median count as covariate covariate <- rowMedians(obj@counts) ihwRes <- IHW::ihw(p, covariate, alpha) padj <- ihwRes@df$adj_pvalue } padj }) #' MRihw runs IHW within a MRcoefs() call #' #' Function used in MRcoefs() when "IHW" is set as the p value adjustment method #' #' @rdname MRihw-fitZigResults #' @param obj Either a fitFeatureModelResults or fitZigResults object #' @param p a vector of pvalues extracted from obj #' @param adjustMethod Value specifying which adjustment method and which covariate to use for IHW pvalue adjustment. #' For obj of class \code{\link{fitFeatureModelResults-class}}, options are "ihw-abundance" (median feature count per row) #' and "ihw-ubiquity" (number of non-zero features per row). For obj of class \code{\link{fitZigResults-class}}, #' options are "ihw-abundance" (weighted mean per feature) and "ihw-ubiquity" (number of non-zero features per row). #' @param alpha pvalue significance level specified for IHW call. Default is 0.1 #' setMethod("MRihw", signature = "fitZigResults", function(obj, p, adjustMethod, alpha){ if (adjustMethod == "ihw-ubiquity"){ #use number of non-zero features per row as the covariate in ihw() call covariate <- rowSums(obj@counts != 0) ihwRes <- IHW::ihw(p, covariate, alpha) padj <- ihwRes@df$adj_pvalue } if (adjustMethod == "ihw-abundance"){ # use Amean as covariate covariate <- obj@eb$Amean ihwRes <- IHW::ihw(p, covariate, alpha) padj <- ihwRes@df$adj_pvalue } padj })metagenomeSeq/R/MRtable.R0000644000175200017520000001147214710220170016237 0ustar00biocbuildbiocbuild#' Table of top microbial marker gene from linear model fit including sequence #' information #' #' Extract a table of the top-ranked features from a linear model fit. This #' function will be updated soon to provide better flexibility similar to #' limma's topTable. This function differs from \code{link{MRcoefs}} in that it #' provides other information about the presence or absence of features to help #' ensure significant features called are moderately present. #' #' #' @param obj Output of fitFeatureModel or fitZig. #' @param by Column number or column name specifying which coefficient or #' contrast of the linear model is of interest. #' @param coef Column number(s) or column name(s) specifying which coefficient #' or contrast of the linear model to display. #' @param number The number of bacterial features to pick out. #' @param taxa Taxa list. #' @param uniqueNames Number the various taxa. #' @param adjustMethod Method to adjust p-values by. Default is "FDR". Options #' include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", #' "none". See \code{\link{p.adjust}} for more details. #' @param group One of five choices, 0,1,2,3,4. 0: the sort is ordered by a #' decreasing absolute value coefficient fit. 1: the sort is ordered by the raw #' coefficient fit in decreasing order. 2: the sort is ordered by the raw #' coefficient fit in increasing order. 3: the sort is ordered by the p-value #' of the coefficient fit in increasing order. 4: no sorting. #' @param eff Filter features to have at least a "eff" quantile or number of effective samples. #' @param numberEff Boolean, whether eff should represent quantile (default/FALSE) or number. #' @param ncounts Filter features to have at least 'counts' of counts. #' @param file Name of file, including location, to save the table. #' @return Table of the top-ranked features determined by the linear fit's #' coefficient. #' @seealso \code{\link{fitZig}} \code{\link{fitFeatureModel}} \code{\link{MRcoefs}} \code{\link{MRfulltable}} #' @examples #' #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' lungTrim=filterData(lungTrim,present=30) #' lungTrim=cumNorm(lungTrim,p=0.5) #' smokingStatus = pData(lungTrim)$SmokingStatus #' mod = model.matrix(~smokingStatus) #' fit = fitZig(obj = lungTrim,mod=mod) #' head(MRtable(fit)) #' #### #' fit = fitFeatureModel(obj = lungTrim,mod=mod) #' head(MRtable(fit)) #' MRtable<-function(obj,by=2,coef=NULL,number=10,taxa=obj@taxa, uniqueNames=FALSE,adjustMethod="fdr",group=0,eff=0,numberEff=FALSE,ncounts=0,file=NULL){ if(length(grep("fitFeatureModel",obj@call))){ groups = factor(obj@design[,by]) by = "logFC"; coef = 1:2; tb = data.frame(logFC=obj@fitZeroLogNormal$logFC,se=obj@fitZeroLogNormal$se) p = obj@pvalues } else { tb = obj@fit$coefficients if(is.null(coef)){ coef = 1:ncol(tb) } p=obj@eb$p.value[,by] groups = factor(obj@fit$design[,by]) if(eff>0){ effectiveSamples = calculateEffectiveSamples(obj) if(numberEff == FALSE){ valid = which(effectiveSamples>=quantile(effectiveSamples,p=eff,na.rm=TRUE)) } else { valid = which(effectiveSamples>=eff) } } } tx = as.character(taxa) if(uniqueNames==TRUE){ for (nm in unique(tx)) { ii=which(tx==nm) tx[ii]=paste(tx[ii],seq_along(ii),sep=":") } } padj = p.adjust(p,method=adjustMethod) cnts = obj@counts posIndices = cnts>0 np0 = rowSums(posIndices[,groups==0]) np1 = rowSums(posIndices[,groups==1]) nc0 = rowSums(cnts[,groups==0]) nc1 = rowSums(cnts[,groups==1]) if(group==0){ srt = order(abs(tb[,by]),decreasing=TRUE) } else if(group==1){ srt = order((tb[,by]),decreasing=TRUE) } else if(group==2){ srt = order((tb[,by]),decreasing=FALSE) } else if(group==3){ srt = order(p,decreasing=FALSE) } else { srt = 1:length(padj) } valid = 1:length(padj) if(ncounts>0){ np=rowSums(cbind(np0,np1)) valid = intersect(valid,which(np>=ncounts)) } srt = srt[which(srt%in%valid)][1:min(number,nrow(tb))] mat = cbind(np0,np1) mat = cbind(mat,nc0) mat = cbind(mat,nc1) mat = cbind(mat,tb[,coef]) mat = cbind(mat,p) mat = cbind(mat,padj) rownames(mat) = tx mat = mat[srt,] nm = c("+samples in group 0","+samples in group 1","counts in group 0", "counts in group 1",colnames(tb)[coef],"pvalues","adjPvalues") colnames(mat) = nm if(!is.null(file)){ nm = c("Taxa",nm) mat2 = cbind(rownames(mat),mat) mat2 = rbind(nm,mat2) write(t(mat2),ncolumns=ncol(mat2),file=file,sep="\t") } return(as.data.frame(mat)) } metagenomeSeq/R/aggregateBySample.R0000644000175200017520000000446214710220170020275 0ustar00biocbuildbiocbuild#' Aggregates a MRexperiment object or counts matrix to by a factor. #' #' Using the phenoData information in the MRexperiment, calling aggregateBySample on a #' MRexperiment and a particular phenoData column (i.e. 'diet') will aggregate counts #' using the aggfun function (default rowMeans). Possible aggfun alternatives #' include rowMeans and rowMedians. #' #' @param obj A MRexperiment object or count matrix. #' @param fct phenoData column name from the MRexperiment object or if count matrix object a vector of labels. #' @param aggfun Aggregation function. #' @param out Either 'MRexperiment' or 'matrix' #' @return An aggregated count matrix or MRexperiment object where the new pData is a vector of `fct` levels. #' @aliases aggSamp #' @rdname aggregateBySample #' @export #' @examples #' #' data(mouseData) #' aggregateBySample(mouseData[1:100,],fct="diet",aggfun=rowSums) #' # not run #' # aggregateBySample(mouseData,fct="diet",aggfun=matrixStats::rowMedians) #' # aggSamp(mouseData,fct='diet',aggfun=rowMaxs) #' aggregateBySample<-function(obj,fct,aggfun=rowMeans,out="MRexperiment"){ if(class(obj)=="MRexperiment"){ mat = MRcounts(obj) if(length(fct)==1) factors = as.character(pData(obj)[,fct]) else factors = as.character(fct) } else { mat = obj factors = as.character(fct) if(length(factors)!=ncol(mat)) stop("If input is a count matrix, fct must be a vector of length = ncol(count matrix)") } if(!(out%in%c("MRexperiment","matrix"))){ stop("The variable out must either be 'MRexperiment' or 'matrix'") } grps = split(seq_along(factors),factors) newMat = array(NA,dim=c(nrow(obj),length(grps))) for(i in seq_along(grps)){ newMat[,i] = aggfun(mat[,grps[[i]],drop=FALSE]) } colnames(newMat) = names(grps) rownames(newMat) = rownames(obj) if(out=='matrix') return(newMat) if(out=='MRexperiment'){ pd = data.frame(names(grps)) colnames(pd) = "phenoData" rownames(pd) = names(grps) pd = as(pd,"AnnotatedDataFrame") if(class(obj)=="MRexperiment"){ fd = as(fData(obj),"AnnotatedDataFrame") newObj = newMRexperiment(newMat,featureData=fd,phenoData=pd) } else { newObj = newMRexperiment(newMat,phenoData=pd) } return(newObj) } } #' @rdname aggregateBySample #' @export aggSamp<-function(obj,fct,aggfun=rowMeans,out='MRexperiment'){ aggregateBySample(obj,fct,aggfun=aggfun,out=out) } metagenomeSeq/R/aggregateByTaxonomy.R0000644000175200017520000000716514710220170020675 0ustar00biocbuildbiocbuild#' Aggregates a MRexperiment object or counts matrix to a particular level. #' #' Using the featureData information in the MRexperiment, calling aggregateByTaxonomy on a #' MRexperiment and a particular featureData column (i.e. 'genus') will aggregate counts #' to the desired level using the aggfun function (default colSums). Possible aggfun alternatives #' include colMeans and colMedians. #' #' @param obj A MRexperiment object or count matrix. #' @param lvl featureData column name from the MRexperiment object or if count matrix object a vector of labels. #' @param alternate Use the rowname for undefined OTUs instead of aggregating to "no_match". #' @param norm Whether to aggregate normalized counts or not. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param aggfun Aggregation function. #' @param sl scaling value, default is 1000. #' @param out Either 'MRexperiment' or 'matrix' #' @param featureOrder Hierarchy of levels in taxonomy as fData colnames #' @param returnFullHierarchy Boolean value to indicate return single column of fData or all columns of hierarchy #' @return An aggregated count matrix. #' @aliases aggTax #' @rdname aggregateByTaxonomy #' @export #' @examples #' #' data(mouseData) #' aggregateByTaxonomy(mouseData[1:100,],lvl="class",norm=TRUE,aggfun=colSums) #' # not run #' # aggregateByTaxonomy(mouseData,lvl="class",norm=TRUE,aggfun=colMedians) #' # aggTax(mouseData,lvl='phylum',norm=FALSE,aggfun=colSums) #' aggregateByTaxonomy<-function(obj,lvl,alternate=FALSE,norm=FALSE,log=FALSE,aggfun = colSums,sl=1000,featureOrder=NULL,returnFullHierarchy=TRUE,out="MRexperiment"){ if(class(obj)=="MRexperiment"){ mat = MRcounts(obj,norm=norm,log=log,sl=sl) if(length(lvl)==1) levels = as.character(fData(obj)[,lvl]) else levels = as.character(lvl) } else { mat = obj levels = as.character(lvl) if(length(levels)!=nrow(mat)) stop("If input is a count matrix, lvl must be a vector of length = nrow(count matrix)") } if(!(out%in%c("MRexperiment","matrix"))){ stop("The variable out must either be 'MRexperiment' or 'matrix'") } nafeatures = is.na(levels) if(length(nafeatures)>0){ if(alternate==FALSE){ levels[nafeatures] = "no_match" } else { levels[nafeatures] = paste("OTU_",rownames(obj)[nafeatures],sep="") } } grps = split(seq_along(levels),levels) newMat = array(NA,dim=c(length(grps),ncol(obj))) for(i in seq_along(grps)){ newMat[i,] = aggfun(mat[grps[[i]],,drop=FALSE]) } rownames(newMat) = names(grps) colnames(newMat) = colnames(obj) if(out=='matrix') return(newMat) if(out=='MRexperiment'){ if(returnFullHierarchy){ if(is.null(featureOrder)){ featureOrder <- colnames(fData(obj)) } taxa = featureData(obj)[match(names(grps), fData(obj)[,lvl]),featureOrder[1:which(featureOrder == lvl)]] featureNames(taxa) = names(grps) } else{ taxa = data.frame(names(grps)) colnames(taxa) = "Taxa" rownames(taxa) = names(grps) taxa = as(taxa,"AnnotatedDataFrame") } if(class(obj)=="MRexperiment"){ pd = phenoData(obj) newObj = newMRexperiment(newMat,featureData=taxa,phenoData=pd) } else { newObj = newMRexperiment(newMat,featureData=taxa) } return(newObj) } } #' @rdname aggregateByTaxonomy #' @export aggTax<-function(obj,lvl,alternate=FALSE,norm=FALSE,log=FALSE,aggfun = colSums,sl=1000,featureOrder=NULL,returnFullHierarchy=TRUE,out='MRexperiment'){ aggregateByTaxonomy(obj,lvl,alternate=alternate,norm=norm,log=log,aggfun = aggfun,sl=sl,featureOrder=featureOrder,returnFullHierarchy=returnFullHierarchy,out=out) } metagenomeSeq/R/allClasses.R0000644000175200017520000003011614710220170016773 0ustar00biocbuildbiocbuildsetClass("MRexperiment", contains=c("eSet"), representation=representation(expSummary = "list"),prototype = prototype( new( "VersionedBiobase",versions = c(classVersion("eSet"),MRexperiment = "1.0.0" )))) setMethod("[", "MRexperiment", function (x, i, j, ..., drop = FALSE) { obj= callNextMethod() if(!missing(j)){ obj@expSummary = new("list",expSummary=as(expSummary(x)[j,1:2,...,drop=drop],"AnnotatedDataFrame"),cumNormStat=x@expSummary$cumNormStat) if(length(pData(obj))>0){ for(i in 1:length(pData(obj))){ if(is.factor(pData(obj)[,i])){ pData(obj)[,i] = factor(pData(obj)[,i]) } else { pData(obj)[,i] = pData(obj)[,i] } } } } obj }) setMethod("colSums", signature ="MRexperiment", function (x, ...) { callNextMethod(MRcounts(x),...) }) setMethod("rowSums", signature="MRexperiment", function (x, ...) { callNextMethod(MRcounts(x),...) }) setMethod("rowMeans", signature="MRexperiment", function (x, ...) { callNextMethod(MRcounts(x),...) }) setMethod("colMeans", signature="MRexperiment", function (x, ...) { callNextMethod(MRcounts(x),...) }) #' Access the normalization factors in a MRexperiment object #' #' Function to access the scaling factors, aka the normalization factors, of #' samples in a MRexperiment object. #' #' @name normFactors #' @docType methods #' @param object a \code{MRexperiment} object #' @return Normalization scaling factors #' @author Joseph N. Paulson #' @examples #' #' data(lungData) #' head(normFactors(lungData)) #' setGeneric("normFactors",function(object){standardGeneric("normFactors")}) setGeneric("normFactors<-",function(object,value){standardGeneric("normFactors<-")}) setMethod("normFactors", signature="MRexperiment",function(object) { nf <- expSummary(object)$normFactors nf <- unlist(nf) names(nf) <- sampleNames(object) nf }) #' Replace the normalization factors in a MRexperiment object #' #' Function to replace the scaling factors, aka the normalization factors, of #' samples in a MRexperiment object. #' #' @name normFactors<- #' @docType methods #' @aliases normFactors<-,MRexperiment,numeric-method normFactors<- #' @param object a \code{MRexperiment} object #' @param value vector of normalization scaling factors #' @return Normalization scaling factors #' @author Joseph N. Paulson #' @examples #' #' data(lungData) #' head(normFactors(lungData)<- rnorm(1)) #' setReplaceMethod("normFactors", signature=c(object="MRexperiment", value="numeric"), function( object, value ) { pData(object@expSummary$expSummary)$normFactors <- value validObject( object ) object }) #' Access sample depth of coverage from MRexperiment object #' #' Access the libSize vector represents the column (sample specific) sums of features, #' i.e. the total number of reads for a sample or depth of coverage. It is used by #' \code{\link{fitZig}}. #' #' @name libSize #' @docType methods #' @param object a \code{MRexperiment} object #' @return Library sizes #' @author Joseph N. Paulson #' @examples #' #' data(lungData) #' head(libSize(lungData)) #' setGeneric("libSize",function(object){standardGeneric("libSize")}) setGeneric("libSize<-",function(object,value){standardGeneric("libSize<-")}) setMethod("libSize", signature="MRexperiment",function(object) { ls <- expSummary(object)$libSize ls <- unlist(ls) names(ls) <- sampleNames(object) ls }) #' Replace the library sizes in a MRexperiment object #' #' Function to replace the scaling factors, aka the library sizes, of #' samples in a MRexperiment object. #' #' @name libSize<- #' @docType methods #' @aliases libSize<-,MRexperiment,numeric-method libSize<- #' @param object a \code{MRexperiment} object #' @param value vector of library sizes #' @return vector library sizes #' @author Joseph N. Paulson #' @examples #' #' data(lungData) #' head(libSize(lungData)<- rnorm(1)) #' setReplaceMethod("libSize", signature=c(object="MRexperiment", value="numeric"), function( object, value ) { pData(object@expSummary$expSummary)$libSize <- value validObject( object ) object }) #' Class "fitZigResults" -- a formal class for storing results from a fitZig call #' #' This class contains all of the same information expected from a fitZig call, #' but it is defined in the S4 style as opposed to being stored as a list. #' #' @slot call the call made to fitZig #' @slot fit 'MLArrayLM' Limma object of the weighted fit #' @slot countResiduals standardized residuals of the fit #' @slot z matrix of the posterior probabilities. It is defined as $z_ij = pr(delta_ij=1 | data)$ #' @slot zUsed used in \code{\link{getZ}} #' @slot eb output of eBayes, moderated t-statistics, moderated F-statistics, etc #' @slot taxa vector of the taxa names #' @slot counts the original count matrix input #' @slot zeroMod the zero model matrix #' @slot zeroCoef the zero model fitted results #' @slot stillActive convergence #' @slot stillActiveNLL nll at convergence #' @slot dupcor correlation of duplicates #' #' setClass("fitZigResults", slots = c(fit = "list", countResiduals = "matrix", z = "matrix", zUsed = "ANY", eb = "MArrayLM", zeroMod = "matrix", stillActive = "logical", stillActiveNLL = "numeric", zeroCoef = "list", dupcor = "ANY", call = "call", taxa = "character", counts = "matrix")) #' Class "fitFeatureModelResults" -- a formal class for storing results from a fitFeatureModel call #' #' This class contains all of the same information expected from a fitFeatureModel call, #' but it is defined in the S4 style as opposed to being stored as a list. #' #' @slot call the call made to fitFeatureModel #' @slot fitZeroLogNormal list of parameter estimates for the zero-inflated log normal model #' @slot design model matrix #' @slot taxa taxa names #' @slot counts count matrix #' @slot pvalues calculated p-values #' @slot permuttedFits permutted z-score estimates under the null #' #' setClass("fitFeatureModelResults", slots = c(call = "call", fitZeroLogNormal = "list", design = "matrix", taxa = "character", counts = "matrix", pvalues = "numeric", permuttedFits = "ANY")) #' Create a MRexperiment object #' #' This function creates a MRexperiment object from a matrix or data frame of #' count data. #' #' See \code{\link{MRexperiment-class}} and \code{eSet} (from the Biobase #' package) for the meaning of the various slots. #' #' @param counts A matrix or data frame of count data. The count data is #' representative of the number of reads annotated for a feature (be it gene, #' OTU, species, etc). Rows should correspond to features and columns to #' samples. #' @param phenoData An AnnotatedDataFrame with pertinent sample information. #' @param featureData An AnnotatedDataFrame with pertinent feature information. #' @param libSize libSize, library size, is the total number of reads for a #' particular sample. #' @param normFactors normFactors, the normalization factors used in either the #' model or as scaling factors of sample counts for each particular sample. #' @return an object of class MRexperiment #' @export #' @author Joseph N Paulson #' @examples #' #' cnts = matrix(abs(rnorm(1000)),nc=10) #' obj <- newMRexperiment(cnts) #' newMRexperiment <- function(counts, phenoData=NULL, featureData=NULL,libSize=NULL, normFactors=NULL) { counts= as.matrix(counts) if( is.null( featureData ) ){ featureData <- annotatedDataFrameFrom(counts, byrow=TRUE) } if( is.null( phenoData ) ){ phenoData <- annotatedDataFrameFrom(counts, byrow=FALSE) } if( is.null( libSize ) ){ libSize <- as.matrix(colSums(counts)) rownames(libSize) = colnames(counts) } if( is.null( normFactors ) ){ normFactors <- as.matrix(rep( NA_real_, length(libSize) )) rownames(normFactors) = rownames(libSize) } obj <-new("MRexperiment", assayData = assayDataNew("environment",counts=counts),phenoData = phenoData,featureData = featureData ,expSummary = new("list",expSummary=annotatedDataFrameFrom(counts,byrow=FALSE),cumNormStat=NULL)) obj@expSummary$expSummary$libSize = libSize; obj@expSummary$expSummary$normFactors=normFactors; validObject(obj) obj } setValidity( "MRexperiment", function( object ) { if( is.null(assayData(object)$counts)) return( "There are no counts!" ) # if( ncol(MRcounts(object)) != length(normFactors(object))) # return( "Experiment summary got hacked!" ) # if( ncol(MRcounts(object)) != length(libSize(object))) # return( "Experiment summary got hacked!" ) TRUE } ) #' Accessor for the counts slot of a MRexperiment object #' #' The counts slot holds the raw count data representing (along the rows) the #' number of reads annotated for a particular feature and (along the columns) #' the sample. #' #' #' @name MRcounts #' @aliases MRcounts,MRexperiment-method MRcounts #' @docType methods #' @param obj a \code{MRexperiment} object. #' @param norm logical indicating whether or not to return normalized counts. #' @param log TRUE/FALSE whether or not to log2 transform scale. #' @param sl The value to scale by (default=1000). #' @return Normalized or raw counts #' @author Joseph N. Paulson, jpaulson@@umiacs.umd.edu #' @examples #' #' data(lungData) #' head(MRcounts(lungData)) #' MRcounts <- function(obj,norm=FALSE,log=FALSE,sl=1000) { stopifnot( is( obj, "MRexperiment" ) ) if(!norm){ x=assayData(obj)[["counts"]] } else{ if(any(is.na(normFactors(obj)))){ x=cumNormMat(obj,sl=sl) } else{ x=sweep(assayData(obj)[["counts"]],2,as.vector(unlist(normFactors(obj)))/sl,"/") } } if(!log){ return(x) } else{ return(log2(x+1)) } } #' Access the posterior probabilities that results from analysis #' #' Accessing the posterior probabilities following a run through #' \code{\link{fitZig}} #' #' #' @name posteriorProbs #' @aliases posteriorProbs,MRexperiment-method posteriorProbs #' @docType methods #' @param obj a \code{MRexperiment} object. #' @return Matrix of posterior probabilities #' @author Joseph N. Paulson #' @examples #' #' # This is a simple demonstration #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' k = which(rowSums(MRcounts(lungTrim)>0)<30) #' lungTrim = cumNorm(lungTrim) #' lungTrim = lungTrim[-k,] #' smokingStatus = pData(lungTrim)$SmokingStatus #' mod = model.matrix(~smokingStatus) #' # The maxit is not meant to be 1 -- this is for demonstration/speed #' settings = zigControl(maxit=1,verbose=FALSE) #' fit = fitZig(obj = lungTrim,mod=mod,control=settings) #' head(posteriorProbs(lungTrim)) #' posteriorProbs <- function( obj ) { stopifnot( is( obj, "MRexperiment" ) ) assayData(obj)[["z"]] } #' Access MRexperiment object experiment data #' #' The expSummary vectors represent the column (sample specific) sums of #' features, i.e. the total number of reads for a sample, libSize and also the #' normalization factors, normFactor. #' #' #' @name expSummary #' @aliases expSummary,MRexperiment-method expSummary #' @docType methods #' @param obj a \code{MRexperiment} object. #' @return Experiment summary table #' @author Joseph N. Paulson, jpaulson@@umiacs.umd.edu #' @examples #' #' data(mouseData) #' expSummary(mouseData) #' expSummary<-function(obj){ stopifnot( is( obj, "MRexperiment" ) ) pData(obj@expSummary$expSummary) } #' Check if MRexperiment or matrix and return matrix #' #' Function to check if object is a MRexperiment #' class or matrix #' #' @name returnAppropriateObj #' @param obj a \code{MRexperiment} or \code{matrix} object #' @param norm return a normalized \code{MRexperiment} matrix #' @param log return a log transformed \code{MRexperiment} matrix #' @param sl scaling value #' @return Matrix #' @examples #' #' data(lungData) #' head(returnAppropriateObj(lungData,norm=FALSE,log=FALSE)) #' returnAppropriateObj <- function(obj,norm,log,sl=1000) { if (inherits(obj, "MRexperiment")) { mat = MRcounts(obj,norm=norm,log=log,sl=sl) } else if (inherits(obj, "matrix")) { mat = obj } else { stop("Object needs to be either a MRexperiment object or matrix") } mat } metagenomeSeq/R/biom2MRexperiment.R0000644000175200017520000000241214710220170020253 0ustar00biocbuildbiocbuild#' Biom to MRexperiment objects #' #' Wrapper to convert biom files to MRexperiment objects. #' #' @param obj The biom object file. #' @return A MRexperiment object. #' @seealso \code{\link{loadMeta}} \code{\link{loadPhenoData}} \code{\link{newMRexperiment}} \code{\link{loadBiom}} #' @examples #' #' library(biomformat) #' rich_dense_file = system.file("extdata", "rich_dense_otu_table.biom", package = "biomformat") #' x = biomformat::read_biom(rich_dense_file) #' biom2MRexperiment(x) #' biom2MRexperiment <- function(obj){ requireNamespace("biomformat") mat = as(biomformat::biom_data(obj),"matrix") if(! is.null(biomformat::observation_metadata(obj))){ len = max(sapply(biomformat::observation_metadata(obj),length)) taxa = as.matrix(sapply(biomformat::observation_metadata(obj),function(i){ i[1:len]})) if(dim(taxa)[1]!=dim(mat)[1]){ taxa = t(taxa) } rownames(taxa) = rownames(mat) colnames(taxa) = colnames(biomformat::observation_metadata(obj)) taxa = as(data.frame(taxa),"AnnotatedDataFrame") } else{ taxa = NULL } if(! is.null(biomformat::sample_metadata(obj))) { pd = as(biomformat::sample_metadata(obj),"AnnotatedDataFrame") } else{ pd = NULL } mrobj = newMRexperiment(counts = mat, phenoData = pd, featureData = taxa) return(mrobj) } metagenomeSeq/R/calculateEffectiveSamples.R0000644000175200017520000000117114710220170022007 0ustar00biocbuildbiocbuild#' Estimated effective samples per feature #' #' Calculates the number of estimated effective samples per feature from the output #' of a fitZig run. The estimated effective samples per feature is calculated as the #' sum_1^n (n = number of samples) 1-z_i where z_i is the posterior probability a feature #' belongs to the technical distribution. #' #' @param obj The output of fitZig run on a MRexperiment object. #' @return A list of the estimated effective samples per feature. #' @seealso \code{\link{fitZig}} \code{\link{MRcoefs}} \code{\link{MRfulltable}} #' calculateEffectiveSamples<-function(obj){ rowSums(1-obj@z) } metagenomeSeq/R/correlationTest.R0000644000175200017520000001026014710220170020064 0ustar00biocbuildbiocbuild#' Correlation of each row of a matrix or MRexperiment object #' #' Calculates the (pairwise) correlation statistics and associated p-values of a matrix #' or the correlation of each row with a vector. #' #' @param obj A MRexperiment object or count matrix. #' @param y Vector of length ncol(obj) to compare to. #' @param method One of 'pearson','spearman', or 'kendall'. #' @param alternative Indicates the alternative hypothesis and must be one of 'two.sided', 'greater' (positive) or 'less'(negative). You can specify just the initial letter. #' @param norm Whether to aggregate normalized counts or not - if MRexperiment object. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param cores Number of cores to use. #' @param override If the number of rows to test is over a thousand the test will not commence (unless override==TRUE). #' @param ... Extra parameters for mclapply. #' @return A matrix of size choose(number of rows, 2) by 2. The first column corresponds to the correlation value. The second column the p-value. #' @seealso \code{\link{correctIndices}} #' @aliases corTest #' @export #' @examples #' #' # Pairwise correlation of raw counts #' data(mouseData) #' cors = correlationTest(mouseData[1:10,],norm=FALSE,log=FALSE) #' head(cors) #' #' mat = MRcounts(mouseData)[1:10,] #' cormat = as.matrix(dist(mat)) # Creating a matrix #' cormat[cormat>0] = 0 # Creating an empty matrix #' ind = correctIndices(nrow(mat)) #' cormat[upper.tri(cormat)][ind] = cors[,1] #' table(cormat[1,-1] - cors[1:9,1]) #' #' # Correlation of raw counts with a vector (library size in this case) #' data(mouseData) #' cors = correlationTest(mouseData[1:10,],libSize(mouseData),norm=FALSE,log=FALSE) #' head(cors) #' correlationTest <- function(obj,y=NULL,method="pearson",alternative="two.sided",norm=TRUE,log=TRUE,cores=1,override=FALSE,...){ mat = returnAppropriateObj(obj,norm,log) nr = nrow(mat) if(nr > 1000){ if(override){ show("Good luck! This might take some time.") } else { stop("Many features being considered - to proceed set override to TRUE") } } if(is.null(rownames(mat))){ nm = as.character(1:nr) } else { nm = rownames(mat) } if(is.null(y)){ corrAndP = mclapply(1:(nr-1),function(i){ vals =(i+1):nr cp = array(NA,dim=c(length(vals),2)) rownames(cp) = paste(nm[i],nm[(i+1):nr],sep="-") colnames(cp) = c("correlation","pvalue") for(j in (i+1):nr){ x = as.numeric(mat[i,]) y = as.numeric(mat[j,]) res = cor.test(x,y,method=method, alternative=alternative) cp[j-i,1] = res$estimate cp[j-i,2] = res$p.value } cp },mc.cores=cores,...) } else { corrAndP = mclapply(1:nr,function(i){ res = cor.test(mat[i,],y,method=method, alternative=alternative) cbind(res$estimate,res$p.value) },mc.cores=cores,...) } correlation = unlist(sapply(corrAndP,function(i){i[,1]})) p = unlist(sapply(corrAndP,function(i){i[,2]})) results = cbind(correlation,p) if(is.null(y)) rownames(results)[nrow(results)] = rownames(corrAndP[[nr-1]]) if(!is.null(y)) rownames(results) = rownames(obj) return(results) } #' Calculate the correct indices for the output of correlationTest #' #' Consider the upper triangular portion of a matrix of size nxn. Results from the \code{correlationTest} are output #' as the combination of two vectors, correlation statistic and p-values. The order of the output is 1vs2, 1vs3, 1vs4, etc. #' The correctIndices returns the correct indices to fill a correlation matrix or correlation-pvalue matrix. #' #' @param n The number of features compared by correlationTest (nrow(mat)). #' @return A vector of the indices for an upper triangular matrix. #' @seealso \code{\link{correlationTest}} #' @export #' @examples #' #' data(mouseData) #' mat = MRcounts(mouseData)[55:60,] #' cors = correlationTest(mat) #' ind = correctIndices(nrow(mat)) #' #' cormat = as.matrix(dist(mat)) #' cormat[cormat>0] = 0 #' cormat[upper.tri(cormat)][ind] = cors[,1] #' table(cormat[1,-1] - cors[1:5,1]) #' correctIndices <- function(n){ if(n==1){ return(1) } if(n==2){ return(c(1,2)) } seq1 <- cumsum(1:(n-1)) - c(0,1:(n-2)) seq2 <- sapply(1:(n-2),function(i) { seq1[-c(1:i)]+1*i }) seq <- c(seq1,unlist(seq2)) return(seq) }metagenomeSeq/R/cumNorm.R0000644000175200017520000000314014710220170016322 0ustar00biocbuildbiocbuild#' Cumulative sum scaling normalization #' #' Calculates each column's quantile and calculates the sum up to and including #' that quantile. #' #' @param obj An MRexperiment object. #' @param p The pth quantile. #' @return Object with the normalization factors stored as #' a vector of the sum up to and including a sample's pth quantile. #' @seealso \code{\link{fitZig}} \code{\link{cumNormStat}} #' @examples #' #' data(mouseData) #' mouseData <- cumNorm(mouseData) #' head(normFactors(mouseData)) #' cumNorm <- function(obj,p=cumNormStatFast(obj)){ if(class(obj)=="MRexperiment"){ x = MRcounts(obj,norm=FALSE,log=FALSE) } else { stop("Object needs to be a MRexperiment object") } normFactors = calcNormFactors(obj=x,p=p) pData(obj@expSummary$expSummary)$normFactors = normFactors validObject(obj) return(obj) } #' Cumulative sum scaling (css) normalization factors #' #' Return a vector of the the sum up to and including a quantile. #' #' @param obj An MRexperiment object or matrix. #' @param p The pth quantile. #' @return Vector of the sum up to and including a sample's pth quantile. #' @seealso \code{\link{fitZig}} \code{\link{cumNormStatFast}} \code{\link{cumNorm}} #' @examples #' #' data(mouseData) #' head(calcNormFactors(mouseData)) #' calcNormFactors <- function(obj,p=cumNormStatFast(obj)){ x = returnAppropriateObj(obj,norm=FALSE,log=FALSE) xx = x xx[x == 0] <- NA qs = colQuantiles(xx, probs = p, na.rm = TRUE) normFactors <- sapply(1:ncol(xx), function(i) { xx = (x[, i] - .Machine$double.eps) sum(xx[xx <= qs[i]]) }) names(normFactors)<-colnames(x) as.data.frame(normFactors) } metagenomeSeq/R/cumNormMat.R0000644000175200017520000000213014710220170016762 0ustar00biocbuildbiocbuild#' Cumulative sum scaling factors. #' #' Calculates each column's quantile and calculates the sum up to and including #' that quantile. #' #' #' @param obj A matrix or MRexperiment object. #' @param p The pth quantile. #' @param sl The value to scale by (default=1000). #' @return Returns a matrix normalized by scaling counts up to and including #' the pth quantile. #' @seealso \code{\link{fitZig}} \code{\link{cumNorm}} #' @examples #' #' data(mouseData) #' head(cumNormMat(mouseData)) #' cumNormMat <- function(obj,p= cumNormStatFast(obj),sl = 1000){ #################################################################################### # Calculates each column's quantile # and calculated the sum up to and # including that quantile. #################################################################################### x = returnAppropriateObj(obj,FALSE,FALSE) xx=x xx[x==0] <- NA qs=colQuantiles(xx,probs=p,na.rm=TRUE) newMat<-sapply(1:ncol(xx), function(i) { xx=(x[,i]-.Machine$double.eps) sum(xx[xx<=qs[i]]) }) nmat<-sweep(x,2,newMat/sl,"/") return(nmat) } metagenomeSeq/R/cumNormStat.R0000644000175200017520000000414414710220170017163 0ustar00biocbuildbiocbuild#' Cumulative sum scaling percentile selection #' #' Calculates the percentile for which to sum counts up to and scale by. #' cumNormStat might be deprecated one day. Deviates from methods in Nature Methods paper #' by making use row means for generating reference. #' #' @param obj A matrix or MRexperiment object. #' @param qFlag Flag to either calculate the proper percentile using #' R's step-wise quantile function or approximate function. #' @param pFlag Plot the relative difference of the median deviance from the reference. #' @param rel Cutoff for the relative difference from one median difference #' from the reference to the next #' @param ... Applicable if pFlag == TRUE. Additional plotting parameters. #' @return Percentile for which to scale data #' @seealso \code{\link{fitZig}} \code{\link{cumNorm}} \code{\link{cumNormStatFast}} #' @examples #' #' data(mouseData) #' p = round(cumNormStat(mouseData,pFlag=FALSE),digits=2) #' cumNormStat <- function(obj,qFlag = TRUE,pFlag = FALSE,rel=.1,...){ mat = returnAppropriateObj(obj,FALSE,FALSE) if(any(colSums(mat)==0)) stop("Warning empty sample") smat = sapply(1:ncol(mat),function(i){sort(mat[,i],decreasing=FALSE)}) ref = rowMeans(smat); yy = mat; yy[yy==0]=NA; ncols = ncol(mat); refS = sort(ref); k = which(refS>0)[1] lo = (length(refS)-k+1) if(qFlag == TRUE){ diffr = sapply(1:ncols,function(i){ refS[k:length(refS)] - quantile(yy[,i],p=seq(0,1,length.out=lo),na.rm=TRUE) }) } if(qFlag == FALSE){ diffr = sapply(1:ncols,function(i){ refS[k:length(refS)] - approx(sort(yy[,i],decreasing=FALSE),n=lo)$y }) } diffr2 = matrixStats::rowMedians(abs(diffr),na.rm=TRUE) if(pFlag ==TRUE){ plot(abs(diff(diffr2[diffr2>0]))/diffr2[diffr2>0][-1],type="h",ylab="Relative difference for reference",xaxt="n",...) abline(h=rel) axis(1,at=seq(0,length(diffr2),length.out=5),labels = seq(0,1,length.out=5)) } x = which(abs(diff(diffr2))/diffr2[-1]>rel)[1] / length(diffr2) if(x<=0.50){ message("Default value being used.") x = 0.50 } if(class(obj)=="MRexperiment"){ obj@expSummary$cumNormStat = x; } return(x) } metagenomeSeq/R/cumNormStatFast.R0000644000175200017520000000357214710220170020005 0ustar00biocbuildbiocbuild#' Cumulative sum scaling percentile selection #' #' Calculates the percentile for which to sum counts up to and scale by. Faster #' version than available in cumNormStat. Deviates from methods described in Nature Methods by #' making use of ro means for reference. #' #' @param obj A matrix or MRexperiment object. #' @param pFlag Plot the median difference quantiles. #' @param rel Cutoff for the relative difference from one median difference #' from the reference to the next. #' @param ... Applicable if pFlag == TRUE. Additional plotting parameters. #' @return Percentile for which to scale data #' @seealso \code{\link{fitZig}} \code{\link{cumNorm}} \code{\link{cumNormStat}} #' @examples #' #' data(mouseData) #' p = round(cumNormStatFast(mouseData,pFlag=FALSE),digits=2) #' cumNormStatFast <-function(obj,pFlag = FALSE,rel=.1,...){ mat = returnAppropriateObj(obj,FALSE,FALSE) smat = lapply(1:ncol(mat), function(i) { sort(mat[which(mat[, i]>0),i], decreasing = TRUE) }) leng = max(sapply(smat,length)) if(any(sapply(smat,length)==1)) stop("Warning sample with one or zero features") smat2 = array(NA,dim=c(leng,ncol(mat))) for(i in 1:ncol(mat)){ smat2[leng:(leng-length(smat[[i]])+1),i] = smat[[i]] } rmat2 = sapply(1:ncol(smat2),function(i){ quantile(smat2[,i],p=seq(0,1,length.out=nrow(smat2)),na.rm=TRUE) }) smat2[is.na(smat2)] = 0 ref1 = rowMeans(smat2) ncols = ncol(rmat2) diffr = sapply(1:ncols, function(i) { ref1 - rmat2[,i] }) diffr1=matrixStats::rowMedians(abs(diffr)) if(pFlag==TRUE){ plot(abs(diff(diffr1))/diffr1[-1],type="h",...) abline(h=rel) axis(1,at=seq(0,length(diffr1),length.out=5),labels = seq(0,1,length.out=5)) } x= which(abs(diff(diffr1))/diffr1[-1] > rel)[1]/length(diffr1) if(x<=0.50){ message("Default value being used.") x = 0.50 } if(class(obj)=="MRexperiment"){ obj@expSummary$cumNormStat = x; } return(x) } metagenomeSeq/R/deprecated_metagenomeSeq_function.R0000644000175200017520000000251014710220170023561 0ustar00biocbuildbiocbuild#' Depcrecated functions in the metagenomeSeq package. #' #' These functions may be removed completely in the next release. #' #' @usage deprecated_metagenomeSeq_function(x, value, ...) #' @rdname metagenomeSeq-deprecated #' @name metagenomeSeq-deprecated #' @param x For assignment operators, the object that will undergo a replacement #' (object inside parenthesis). #' @param value For assignment operators, the value to replace with #' (the right side of the assignment). #' @param ... For functions other than assignment operators, #' parameters to be passed to the modern version of the function (see table). #' @docType package #' @export fitMeta #' @aliases deprecated_metagenomeSeq_function fitMeta load_phenoData load_meta load_biom load_metaQ #' deprecated_metagenomeSeq_function <- function(x, value, ...){return(NULL)} fitMeta <- function(...){.Deprecated("fitMeta",package="metagenomeSeq");return(fitLogNormal(...))} load_phenoData <- function(...){.Deprecated("load_phenoData",package="metagenomeSeq");return(loadPhenoData(...))} load_biom <- function(...){.Deprecated("load_biom",package="metagenomeSeq");return(loadBiom(...))} load_meta <- function(...){.Deprecated("load_meta",package="metagenomeSeq");return(loadMeta(...))} load_metaQ <- function(...){.Deprecated("load_metaQ",package="metagenomeSeq");return(loadMetaQ(...))} metagenomeSeq/R/doCountMStep.R0000644000175200017520000000520514710220170017272 0ustar00biocbuildbiocbuild#' Compute the Maximization step calculation for features still active. #' #' Maximization step is solved by weighted least squares. The function also #' computes counts residuals. #' #' Maximum-likelihood estimates are approximated using the EM algorithm where #' we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the #' zero point mass as latent indicator variables. The density is defined as #' $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * #' f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is #' $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log #' pi_j(s_j)+(1-delta_ij)log (1-pi_j (s_j))$. The responsibilities are defined #' as $z_ij = pr(delta_ij=1 | data)$. #' #' @param z Matrix (m x n) of estimate responsibilities (probabilities that a #' count comes from a spike distribution at 0). #' @param y Matrix (m x n) of count observations. #' @param mmCount Model matrix for the count distribution. #' @param stillActive Boolean vector of size M, indicating whether a feature #' converged or not. #' @param fit2 Previous fit of the count model. #' @param dfMethod Either 'default' or 'modified' (by responsibilities) #' @return Update matrix (m x n) of estimate responsibilities (probabilities #' that a count comes from a spike distribution at 0). #' @seealso \code{\link{fitZig}} doCountMStep <- function(z, y, mmCount, stillActive,fit2=NULL,dfMethod="modified"){ if (is.null(fit2)){ fit=limma::lmFit(y[stillActive,],mmCount,weights = (1-z[stillActive,])) if(dfMethod=="modified"){ df = rowSums(1-z[stillActive,,drop=FALSE]) - ncol(mmCount) fit$df[stillActive] = df fit$df.residual[stillActive] = df } countCoef = fit$coefficients countMu=tcrossprod(countCoef, mmCount) residuals=sweep((y[stillActive,,drop=FALSE]-countMu),1,fit$sigma,"/") dat = list(fit = fit, residuals = residuals) return(dat) } else { residuals = fit2$residuals fit2 = fit2$fit fit=limma::lmFit(y[stillActive,,drop=FALSE],mmCount,weights = (1-z[stillActive,,drop=FALSE])) fit2$coefficients[stillActive,] = fit$coefficients fit2$stdev.unscaled[stillActive,]=fit$stdev.unscaled fit2$sigma[stillActive] = fit$sigma fit2$Amean[stillActive] = fit$Amean if(dfMethod=="modified"){ df = rowSums(1-z[stillActive,,drop=FALSE]) - ncol(mmCount) fit$df = df fit$df.residual = df } fit2$df[stillActive] = fit$df fit2$df.residual[stillActive] = fit$df.residual countCoef = fit$coefficients countMu=tcrossprod(countCoef, mmCount) r=sweep((y[stillActive,,drop=FALSE]-countMu),1,fit$sigma,"/") residuals[stillActive,]=r dat = list(fit = fit2, residuals=residuals) return(dat) } } metagenomeSeq/R/doEStep.R0000644000175200017520000000251614710220170016253 0ustar00biocbuildbiocbuild#' Compute the Expectation step. #' #' Estimates the responsibilities $z_ij = fracpi_j cdot I_0(y_ijpi_j cdot #' I_0(y_ij + (1-pi_j) cdot f_count(y_ij #' #' Maximum-likelihood estimates are approximated using the EM algorithm where #' we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the #' zero point mass as latent indicator variables. The density is defined as #' $f_zig(y_ij = pi_j(S_j) cdot f_0(y_ij) +(1-pi_j (S_j))cdot #' f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is #' $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log #' pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined #' as $z_ij = pr(delta_ij=1 | data)$. #' #' @param countResiduals Residuals from the count model. #' @param zeroResiduals Residuals from the zero model. #' @param zeroIndices Index (matrix m x n) of counts that are zero/non-zero. #' @return Updated matrix (m x n) of estimate responsibilities (probabilities #' that a count comes from a spike distribution at 0). #' @seealso \code{\link{fitZig}} doEStep <- function(countResiduals, zeroResiduals, zeroIndices) { pi_prop=getPi(zeroResiduals) w1=sweep(zeroIndices, 2, pi_prop, FUN="*") countDensity=getCountDensity(countResiduals) w2=sweep(countDensity, 2, 1-pi_prop, FUN="*") z=w1/(w1+w2) z[z>1-1e-6]=1-1e-6 z[!zeroIndices]=0 z } metagenomeSeq/R/doZeroMStep.R0000644000175200017520000000335514710220170017125 0ustar00biocbuildbiocbuild#' Compute the zero Maximization step. #' #' Performs Maximization step calculation for the mixture components. Uses #' least squares to fit the parameters of the mean of the logistic #' distribution. $$ pi_j = sum_i^M frac1Mz_ij $$ Maximum-likelihood estimates #' are approximated using the EM algorithm where we treat mixture membership #' $delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent #' indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j) cdot #' f_0(y_ij) +(1-pi_j (S_j))cdot f_count(y_ij;mu_i,sigma_i^2)$. The #' log-likelihood in this extended model is $(1-delta_ij) log #' f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j #' (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. #' #' #' @param z Matrix (m x n) of estimate responsibilities (probabilities that a #' count comes from a spike distribution at 0). #' @param zeroIndices Index (matrix m x n) of counts that are zero/non-zero. #' @param mmZero The zero model, the model matrix to account for the change in #' the number of OTUs observed as a linear effect of the depth of coverage. #' @return List of the zero fit (zero mean model) coefficients, variance - #' scale parameter (scalar), and normalized residuals of length #' sum(zeroIndices). #' @seealso \code{\link{fitZig}} doZeroMStep <- function(z, zeroIndices, mmZero) { pi=sapply(1:ncol(zeroIndices), function(j) { if (sum(zeroIndices[,j])==0){ return(1e-8) } tmp=mean(z[zeroIndices[,j],j],na.rm=TRUE) ifelse(tmp<=1e-8, 1e-8, ifelse(tmp>=1-(1e-8),1-(1e-8),tmp)) }) zeroLM=lm.fit(mmZero, qlogis(pi)) zeroCoef=zeroLM$coef r=zeroLM$residuals sigma=sd(r)+(1e-3) list(zeroLM=zeroLM, zeroCoef=zeroCoef, sigma=sigma, residuals=r/sigma) } metagenomeSeq/R/exportMat.R0000644000175200017520000000233214710220170016667 0ustar00biocbuildbiocbuild#' Export the normalized MRexperiment dataset as a matrix. #' #' This function allows the user to take a dataset of counts and output the #' dataset to the user's workspace as a tab-delimited file, etc. #' #' #' @aliases exportMatrix exportMat #' @param obj A MRexperiment object or count matrix. #' @param log Whether or not to log transform the counts - if MRexperiment object. #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param sep Separator for writing out the count matrix. #' @param file Output file name. #' @return NA #' @seealso \code{\link{cumNorm}} #' @examples #' #' data(lungData) #' dataDirectory <- system.file("extdata", package="metagenomeSeq") #' exportMat(lungData[,1:5],file=file.path(dataDirectory,"tmp.tsv")) #' head(read.csv(file=file.path(dataDirectory,"tmp.tsv"),sep="\t")) #' exportMat <-function(obj,log=TRUE,norm=TRUE,sep="\t",file="~/Desktop/matrix.tsv"){ mat = returnAppropriateObj(obj,norm,log) oMat = array(NA,dim=c((nrow(mat)+1),(ncol(mat)+1))); oMat[1,2:ncol(oMat)] = colnames(mat); oMat[2:nrow(oMat),2:ncol(oMat)] = mat; oMat[2:nrow(oMat),1] = rownames(mat); oMat[1,1] = "Taxa and Samples"; write(t(oMat),file=file,sep=sep,ncolumns=ncol(oMat)); } metagenomeSeq/R/exportStats.R0000644000175200017520000000261714710220170017252 0ustar00biocbuildbiocbuild#' Various statistics of the count data. #' #' A matrix of values for each sample. The matrix consists of sample ids, the #' sample scaling factor, quantile value, the number identified features, and library size (depth of coverage). #' #' #' @param obj A MRexperiment object with count data. #' @param p Quantile value to calculate the scaling factor and quantiles for #' the various samples. #' @param file Output file name. #' @return None. #' @seealso \code{\link{cumNorm}} \code{\link{quantile}} #' @examples #' #' data(lungData) #' dataDirectory <- system.file("extdata", package="metagenomeSeq") #' exportStats(lungData[,1:5],file=file.path(dataDirectory,"tmp.tsv")) #' head(read.csv(file=file.path(dataDirectory,"tmp.tsv"),sep="\t")) #' exportStats <-function(obj,p= cumNormStat(obj),file="~/Desktop/res.stats.tsv"){ xx=MRcounts(obj) xx[xx==0]=NA qs=colQuantiles(xx,probs=p,na.rm=TRUE) xx[xx>0] = 1; xx[is.na(xx)]=0 newMat <- array(NA,dim=c(5,ncol(xx)+1)); newMat[1,1] = "Subject" newMat[2,1] = "Scaling factor" newMat[3,1] = "Quantile value" newMat[4,1] = "Number of identified features" newMat[5,1] = "Library size" newMat[1,2:ncol(newMat)]<-sampleNames(obj); newMat[2,2:ncol(newMat)]<-unlist(normFactors(obj)); newMat[3,2:ncol(newMat)]<-qs; newMat[4,2:ncol(newMat)]<-colSums(xx); newMat[5,2:ncol(newMat)]<-unlist(libSize(obj)); write((newMat),file = file,sep = "\t",ncolumns = 5); } metagenomeSeq/R/filterData.R0000644000175200017520000000143414710220170016765 0ustar00biocbuildbiocbuild#' Filter datasets according to no. features present in features with at least a certain depth. #' #' Filter the data based on the number of present features after filtering samples by depth of coverage. #' There are many ways to filter the object, this is just one way. #' #' @param obj A MRexperiment object or count matrix. #' @param present Features with at least 'present' postive samples. #' @param depth Sampls with at least this much depth of coverage #' @return A MRexperiment object. #' @export #' @examples #' #' data(mouseData) #' filterData(mouseData) #' filterData <- function(obj,present=1,depth=1000){ mat = returnAppropriateObj(obj,norm=FALSE,log=FALSE)>0 cols = which(colSums(MRcounts(obj))>=depth) rows = which(rowSums(mat[,cols])>=present) return(obj[rows,cols]) } metagenomeSeq/R/fitDO.R0000644000175200017520000000545414710220170015721 0ustar00biocbuildbiocbuild#' Wrapper to calculate Discovery Odds Ratios on feature values. #' #' This function returns a data frame of p-values, odds ratios, lower and upper #' confidence limits for every row of a matrix. The discovery odds ratio is calculated #' as using Fisher's exact test on actual counts. The test's hypothesis is whether #' or not the discovery of counts for a feature (of all counts) is found in greater proportion #' in a particular group. #' #' #' @param obj A MRexperiment object with a count matrix, or a simple count #' matrix. #' @param cl Group comparison #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param adjust.method Method to adjust p-values by. Default is "FDR". Options #' include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", #' "none". See \code{\link{p.adjust}} for more details. #' @param cores Number of cores to use. #' @param ... Extra options for makeCluster #' @return Matrix of odds ratios, p-values, lower and upper confidence intervals #' @seealso \code{\link{cumNorm}} \code{\link{fitZig}} \code{\link{fitPA}} \code{\link{fitMeta}} #' @examples #' #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' lungTrim = lungTrim[-which(rowSums(MRcounts(lungTrim)>0)<20),] #' res = fitDO(lungTrim,pData(lungTrim)$SmokingStatus); #' head(res) #' fitDO<-function(obj,cl,norm=TRUE,log=TRUE,adjust.method='fdr',cores=1,...){ x = returnAppropriateObj(obj,norm,log) nrows= nrow(x); if(is.null(rownames(x))){rownames(x)=1:nrows} sumClass1 = round(sum(x[,cl==levels(cl)[1]])) sumClass2 = round(sum(x[,cl==levels(cl)[2]])) cores <- makeCluster(getOption("cl.cores", cores),...) res = parRapply(cl=cores,x,function(i){ tbl = table(1-i,cl) if(sum(dim(tbl))!=4){ tbl = array(0,dim=c(2,2)); tbl[1,1] = round(sum(i[cl==levels(cl)[1]])) tbl[1,2] = round(sum(i[cl==levels(cl)[2]])) tbl[2,1] = sumClass1-tbl[1,1] tbl[2,2] = sumClass2-tbl[1,2] } ft <- fisher.test(tbl,workspace=8e6,alternative="two.sided",conf.int=TRUE) cbind(p=ft$p.value,o=ft$estimate,cl=ft$conf.int[1],cu=ft$conf.int[2]) }) stopCluster(cores) nres = nrows*4 seqs = seq(1,nres,by=4) p = res[seqs] adjp = p.adjust(p,method=adjust.method) o = res[seqs+1] cl = res[seqs+2] cu = res[seqs+3] res = data.frame(cbind(o,cl,cu,p,adjp)) colnames(res) = c("oddsRatio","lower","upper","pvalues","adjPvalues") rownames(res) = rownames(x) return(res) } metagenomeSeq/R/fitFeatureModel.R0000644000175200017520000000673414710220170017775 0ustar00biocbuildbiocbuild#' Computes differential abundance analysis using a zero-inflated log-normal model #' #' Wrapper to actually run zero-inflated log-normal model given a MRexperiment object #' and model matrix. User can decide to shrink parameter estimates. #' #' @param obj A MRexperiment object with count data. #' @param mod The model for the count distribution. #' @param coef Coefficient of interest to grab log fold-changes. #' @param B Number of bootstraps to perform if >1. If >1 performs permutation test. #' @param szero TRUE/FALSE, shrink zero component parameters. #' @param spos TRUE/FALSE, shrink positive component parameters. #' @return A list of objects including: #' \itemize{ #' \item{call - the call made to fitFeatureModel} #' \item{fitZeroLogNormal - list of parameter estimates for the zero-inflated log normal model} #' \item{design - model matrix} #' \item{taxa - taxa names} #' \item{counts - count matrix} #' \item{pvalues - calculated p-values} #' \item{permuttedfits - permutted z-score estimates under the null} #' } #' @seealso \code{\link{cumNorm}} #' @examples #' #' data(lungData) #' lungData = lungData[,-which(is.na(pData(lungData)$SmokingStatus))] #' lungData=filterData(lungData,present=30,depth=1) #' lungData <- cumNorm(lungData, p=.5) #' s <- normFactors(lungData) #' pd <- pData(lungData) #' mod <- model.matrix(~1+SmokingStatus, data=pd) #' lungres1 = fitFeatureModel(lungData,mod) #' fitFeatureModel<-function(obj,mod,coef=2,B=1,szero=FALSE,spos=TRUE){ stopifnot(is(obj, "MRexperiment")) if (any(is.na(normFactors(obj)))) stop("At least one NA normalization factors") if (any(is.na(libSize(obj)))) stop("Calculate the library size first!") if (any(is.na(normFactors(obj)))) { stop("Calculate the normalization factors first!") } nf = normFactors(obj) mmCount = cbind(mod, log(nf/median(nf))) colnames(mmCount)[ncol(mmCount)] = "scalingFactor" if(ncol(mmCount)>3){ stop("Can't analyze currently.") } i = permuttedFits = NULL # These pieces get to be a part of the new zero-ln model! fitzeroln = fitZeroLogNormal(obj,mmCount,coef=coef,szero=szero,spos=spos) if(any(is.na(fitzeroln$logFC))){ feats = which(is.na(fitzeroln$logFC)) mat = MRcounts(obj[feats,], norm=TRUE, log=FALSE,sl=median(nf)) fit = lmFit(log(mat+1),mmCount) fit = eBayes(fit) fitzeroln$logFC[feats] = coefficients(fit)[,coef] fitzeroln$se[feats] = (sqrt(fit$s2.post)*fit$stdev.unscaled)[,coef] } zscore = fitzeroln$logFC/fitzeroln$se if(B>1){ permutations = replicate(B,sample(mmCount[,coef])) mmCountPerm = mmCount permuttedFits = foreach(i = seq(B),.errorhandling="remove", .packages=c("metagenomeSeq","glmnet")) %dopar% { mmCountPerm[,coef] = permutations[,i] permFit = fitZeroLogNormal(obj,mmCountPerm,coef=coef,szero=szero,spos=spos) permFit$logFC/permFit$se } zperm = abs(sapply(permuttedFits,function(i)i)) pvals = rowMeans(zperm>=abs(zscore),na.rm=TRUE) } else { pvals = 2*(1-pnorm(abs(zscore))) } # old way of creating results object # res = list(call=match.call(),fitZeroLogNormal=fitzeroln,design=mmCount, # taxa=rownames(obj),counts=MRcounts(obj),pvalues=pvals,permuttedFits=permuttedFits) # new way with defined results class res = new("fitFeatureModelResults", call = match.call(), fitZeroLogNormal=fitzeroln, design = mmCount, taxa = rownames(obj), counts = MRcounts(obj), pvalues = pvals, permuttedFits = permuttedFits) res }metagenomeSeq/R/fitLogNormal.R0000644000175200017520000000466414710220170017313 0ustar00biocbuildbiocbuild#' Computes a log-normal linear model and permutation based p-values. #' #' Wrapper to perform the permutation test on the t-statistic. This is the original #' method employed by metastats (for non-sparse large samples). We include CSS normalization #' though (optional) and log2 transform the data. In this method the null distribution is not assumed to be a t-dist. #' #' #' @param obj A MRexperiment object with count data. #' @param mod The model for the count distribution. #' @param useCSSoffset Boolean, whether to include the default scaling #' parameters in the model or not. #' @param B Number of permutations. #' @param coef The coefficient of interest. #' @param sl The value to scale by (default=1000). #' #' @return Call made, fit object from lmFit, t-statistics and p-values for each feature. #' @export #' @examples #' #' # This is a simple demonstration #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' k = which(rowSums(MRcounts(lungTrim)>0)<30) #' lungTrim = cumNorm(lungTrim) #' lungTrim = lungTrim[-k,] #' smokingStatus = pData(lungTrim)$SmokingStatus #' mod = model.matrix(~smokingStatus) #' fit = fitLogNormal(obj = lungTrim,mod=mod,B=1) #' fitLogNormal <- function(obj,mod,useCSSoffset=TRUE,B=1000,coef=2,sl=1000){ if(class(obj)=="MRexperiment"){ mat = MRcounts(obj,norm=FALSE,log=FALSE) mat = log2(mat + 1) } else if(class(obj) == "matrix") { mat = obj } else { stop("Object needs to be either a MRexperiment object or matrix") } if(useCSSoffset==TRUE){ if(any(is.na(normFactors(obj)))){ stop("Calculate the normalization factors first!") } mmCount=cbind(mod,log2(normFactors(obj)/sl +1))} else{ mmCount=mod } # fit of the data fitRes = limma::lmFit(mat,mmCount) # The t-statistic tt <- fitRes$coef[,coef] / fitRes$stdev.unscaled[,coef] / fitRes$sigma perms = replicate(B,sample(mmCount[,coef])) mmCount1=mmCount[,-coef] nc = ncol(mmCount) tobs<- sapply(1:B,function(i){ # This code forces the covariate of interest to be a factor (might not apply) mmCountPerm = cbind(mmCount1,factor(perms[,i])) fit = limma::lmFit(mat,mmCountPerm) ttObs <- fit$coef[,nc] / fit$stdev.unscaled[,nc] / fit$sigma ttObs }) p = rowMeans(abs(tobs)>=abs(tt)) dat = list(call=match.call(),fit=fitRes,t = tt,p = p,type="perm") return(dat) } metagenomeSeq/R/fitPA.R0000644000175200017520000000445714710220170015721 0ustar00biocbuildbiocbuild#' Wrapper to run fisher's test on presence/absence of a feature. #' #' This function returns a data frame of p-values, odds ratios, lower and upper #' confidence limits for every row of a matrix. #' #' #' @param obj A MRexperiment object with a count matrix, or a simple count #' matrix. #' @param cl Group comparison #' @param thres Threshold for defining presence/absence. #' @param adjust.method Method to adjust p-values by. Default is "FDR". Options #' include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", #' "none". See \code{\link{p.adjust}} for more details. #' @param cores Number of cores to use. #' @param ... Extra parameters for makeCluster #' @return Matrix of odds ratios, p-values, lower and upper confidence intervals #' @seealso \code{\link{cumNorm}} \code{\link{fitZig}} \code{\link{fitDO}} \code{\link{fitMeta}} #' @examples #' #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' lungTrim = lungTrim[-which(rowSums(MRcounts(lungTrim)>0)<20),] #' res = fitPA(lungTrim,pData(lungTrim)$SmokingStatus); #' head(res) #' fitPA<-function(obj,cl,thres=0,adjust.method='fdr',cores=1,...){ x = returnAppropriateObj(obj,norm=FALSE,log=FALSE)>thres nrows= nrow(x); if(is.null(rownames(x))){rownames(x)=1:nrows} nClass1 = sum(cl==levels(cl)[1]) nClass2 = sum(cl==levels(cl)[2]) cores <- makeCluster(getOption("cl.cores", cores),...) res = parRapply(cl=cores,x,function(i){ tbl = table(1-i,cl) if(sum(dim(tbl))!=4){ tbl = array(0,dim=c(2,2)); tbl[1,1] = sum(i[cl==levels(cl)[1]]) tbl[1,2] = sum(i[cl==levels(cl)[2]]) tbl[2,1] = nClass1-tbl[1,1] tbl[2,2] = nClass2-tbl[1,2] } ft <- fisher.test(tbl,workspace=8e6,alternative="two.sided",conf.int=TRUE) cbind(o=ft$estimate,cl=ft$conf.int[1],cu=ft$conf.int[2],p=ft$p.value) }) stopCluster(cores) nres = nrows*4 seqs = seq(1,nres,by=4) p = res[seqs+3] adjp = p.adjust(p,method=adjust.method) o = res[seqs] cl = res[seqs+1] cu = res[seqs+2] res = data.frame(cbind(o,cl,cu,p,adjp)) colnames(res) = c("oddsRatio","lower","upper","pvalues","adjPvalues") rownames(res) = rownames(x) return(res) } metagenomeSeq/R/fitTimeSeries.R0000644000175200017520000006367214710220170017476 0ustar00biocbuildbiocbuild#' Trapezoidal Integration #' #' Compute the area of a function with values 'y' at the points 'x'. #' Function comes from the pracma package. #' #' @param x x-coordinates of points on the x-axis #' @param y y-coordinates of function values #' @return Approximated integral of the function from 'min(x)' to 'max(x)'. #' Or a matrix of the same size as 'y'. #' @rdname trapz #' @export #' @examples #' #' # Calculate the area under the sine curve from 0 to pi: #' n <- 101 #' x <- seq(0, pi, len = n) #' y <- sin(x) #' trapz(x, y) #=> 1.999835504 #' #' # Use a correction term at the boundary: -h^2/12*(f'(b)-f'(a)) #' h <- x[2] - x[1] #' ca <- (y[2]-y[1]) / h #' cb <- (y[n]-y[n-1]) / h #' trapz(x, y) - h^2/12 * (cb - ca) #=> 1.999999969 #' trapz <- function(x,y){ if (missing(y)) { if (length(x) == 0) return(0) y <- x x <- 1:length(x) } if (length(x) == 0) return(0) if (!(is.numeric(x) || is.complex(x)) || !(is.numeric(y) || is.complex(y))) stop("Arguments 'x' and 'y' must be real or complex.") m <- length(x) xp <- c(x, x[m:1]) yp <- c(numeric(m), y[m:1]) n <- 2 * m p1 <- sum(xp[1:(n - 1)] * yp[2:n]) + xp[n] * yp[1] p2 <- sum(xp[2:n] * yp[1:(n - 1)]) + xp[1] * yp[n] return(0.5 * (p1 - p2)) } #' smoothing-splines anova fit #' #' Sets up a data-frame with the feature abundance, #' class information, time points, sample ids and returns #' the fitted values for the fitted model. #' #' @param formula Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value. #' @param abundance Numeric vector of abundances. #' @param class Class membership (factor of group membership). #' @param time Time point vector of relative times (same length as abundance). #' @param id Sample / patient id. #' @param include Parameters to include in prediction. #' @param pd Extra variable. #' @param ... Extra parameters for ssanova function (see ?ssanova). #' @return \itemize{A list containing: #' \item data : Inputed data #' \item fit : The interpolated / fitted values for timePoints #' \item se : The standard error for CI intervals #' \item timePoints : The time points interpolated over #' } #' @seealso \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssPermAnalysis}} \code{\link{ssPerm}} \code{\link{ssIntervalCandidate}} #' @rdname ssFit #' @export #' @examples #' #' # Not run #' ssFit <- function(formula,abundance,class,time,id,include=c("class", "time:class"),pd,...) { df = data.frame(abundance = abundance, class = factor(class), time=time,id = factor(id),pd) # The smoothing splines anova model if(missing(formula)){ mod = gss::ssanova(abundance ~ time * class, data=df,...) } else{ mod = gss::ssanova(formula,data=df,...) } fullTime = seq(min(df$time), max(df$time), by=1) values = data.frame(time=fullTime, class=factor(levels(df[,"class"]))[2]) fit = predict(mod, values, include=include, se=TRUE) res = list(data=df, fit=fit$fit, se=fit$se, timePoints=fullTime) return(res) } #' class permutations for smoothing-spline time series analysis #' #' Creates a list of permuted class memberships for the time series permuation tests. #' #' @param df Data frame containing class membership and sample/patient id label. #' @param B Number of permutations. #' @return A list of permutted class memberships #' @seealso \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssFit}} \code{\link{ssPermAnalysis}} \code{\link{ssIntervalCandidate}} #' @rdname ssPerm #' @examples #' #' # Not run #' ssPerm <- function(df,B) { dat = data.frame(class=df$class, id=df$id) # id = table(dat$id) id = table(interaction(dat$class,dat$id)) id = id[id>0] classes = unique(dat)[,"class"] permList = lapply(1:B,function(i){ rep(sample(classes, replace=FALSE),id) }) return(permList) } #' smoothing-splines anova fits for each permutation #' #' Calculates the fit for each permutation and estimates #' the area under the null (permutted) model for interesting time #' intervals of differential abundance. #' #' @param data Data used in estimation. #' @param formula Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value. #' @param permList A list of permutted class memberships #' @param intTimes Interesting time intervals. #' @param timePoints Time points to interpolate over. #' @param include Parameters to include in prediction. #' @param ... Options for ssanova #' @return A matrix of permutted area estimates for time intervals of interest. #' @seealso \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssFit}} \code{\link{ssPerm}} \code{\link{ssIntervalCandidate}} #' @rdname ssPermAnalysis #' @export #' @examples #' #' # Not run #' ssPermAnalysis <- function(data,formula,permList,intTimes,timePoints,include=c("class", "time:class"),...){ resPerm=matrix(NA, length(permList), nrow(intTimes)) permData=data case = data.frame(time=timePoints, class=factor(levels(data$class)[2])) for (j in 1:length(permList)){ permData$class = permList[[j]] # The smoothing splines anova model if(!missing(formula)){ permModel = gss::ssanova(formula, data=permData,...) } else{ permModel = gss::ssanova(abundance ~ time * class,data=permData,...) } permFit = cbind(timePoints, (2*predict(permModel,case,include=include, se=TRUE)$fit)) for (i in 1:nrow(intTimes)){ permArea=permFit[which(permFit[,1]==intTimes[i,1]) : which(permFit[,1]==intTimes[i, 2]), ] resPerm[j, i]=metagenomeSeq::trapz(x=permArea[,1], y=permArea[,2]) } if(j%%100==0) show(j) } return(resPerm) } #' calculate interesting time intervals #' #' Calculates time intervals of interest using SS-Anova fitted confidence intervals. #' #' @param fit SS-Anova fits. #' @param standardError SS-Anova se estimates. #' @param timePoints Time points interpolated over. #' @param positive Positive region or negative region (difference in abundance is positive/negative). #' @param C Value for which difference function has to be larger or smaller than (default 0). #' @return Matrix of time point intervals of interest #' @seealso \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssFit}} \code{\link{ssPerm}} \code{\link{ssPermAnalysis}} #' @rdname ssIntervalCandidate #' @export #' @examples #' #' # Not run #' ssIntervalCandidate <- function(fit, standardError, timePoints, positive=TRUE,C=0){ lowerCI = (2*fit - (1.96*2*standardError)) upperCI = (2*fit + (1.96*2*standardError)) if (positive){ abundanceDifference = which( lowerCI>=0 & abs(lowerCI)>=C ) }else{ abundanceDifference = which( upperCI<=0 & abs(upperCI)>=C ) } if (length(abundanceDifference)>0){ intIndex=which(diff(abundanceDifference)!=1) intTime=matrix(NA, (length(intIndex)+1), 4) if (length(intIndex)==0){ intTime[1,1]=timePoints[abundanceDifference[1]] intTime[1,2]=timePoints[tail(abundanceDifference, n=1)] }else{ i=1 while(length(intTime)!=0 & length(intIndex)!=0){ intTime[i,1]=timePoints[abundanceDifference[1]] intTime[i,2]=timePoints[abundanceDifference[intIndex[1]]] abundanceDifference=abundanceDifference[-c(1:intIndex[1])] intIndex=intIndex[-1] i=i+1 } intTime[i,1] = timePoints[abundanceDifference[1]] intTime[i,2] = timePoints[tail(abundanceDifference, n=1)] } }else{ intTime=NULL } return(intTime) } #' Discover differentially abundant time intervals using SS-Anova #' #' Calculate time intervals of interest using SS-Anova fitted models. #' Fitting is performed uses Smoothing Spline ANOVA (SS-Anova) to find interesting intervals of time. #' Given observations at different time points for two groups, fitSSTimeSeries #' calculates a function that models the difference in abundance between two #' groups across all time. Using permutations we estimate a null distribution #' of areas for the time intervals of interest and report significant intervals of time. #' Use of the function for analyses should cite: #' "Finding regions of interest in high throughput genomics data using smoothing splines" #' Talukder H, Paulson JN, Bravo HC. (In preparation) #' #' @param obj metagenomeSeq MRexperiment-class object. #' @param formula Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value. #' @param feature Name or row of feature of interest. #' @param class Name of column in phenoData of MRexperiment-class object for class memberhip. #' @param time Name of column in phenoData of MRexperiment-class object for relative time. #' @param id Name of column in phenoData of MRexperiment-class object for sample id. #' @param lvl Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level). #' @param include Parameters to include in prediction. #' @param C Value for which difference function has to be larger or smaller than (default 0). #' @param B Number of permutations to perform #' @param norm When aggregating counts to normalize or not. #' @param log Log2 transform. #' @param sl Scaling value. #' @param featureOrder Hierarchy of levels in taxonomy as fData colnames #' @param ... Options for ssanova #' @return List of matrix of time point intervals of interest, Difference in abundance area and p-value, fit, area permutations, and call. #' @return A list of objects including: #' \itemize{ #' \item{timeIntervals - Matrix of time point intervals of interest, area of differential abundance, and pvalue.} #' \item{data - Data frame of abundance, class indicator, time, and id input.} #' \item{fit - Data frame of fitted values of the difference in abundance, standard error estimates and timepoints interpolated over.} #' \item{perm - Differential abundance area estimates for each permutation.} #' \item{call - Function call.} #' } #' @rdname fitSSTimeSeries #' @seealso \code{\link{cumNorm}} \code{\link{ssFit}} \code{\link{ssIntervalCandidate}} \code{\link{ssPerm}} \code{\link{ssPermAnalysis}} \code{\link{plotTimeSeries}} #' @export #' @examples #' #' data(mouseData) #' res = fitSSTimeSeries(obj=mouseData,feature="Actinobacteria", #' class="status",id="mouseID",time="relativeTime",lvl='class',B=2) #' fitSSTimeSeries <- function(obj,formula,feature,class,time,id,lvl=NULL,include=c("class", "time:class"),C=0,B=1000,norm=TRUE,log=TRUE,sl=1000,featureOrder=NULL,...) { if(!is.null(lvl)){ aggData = aggregateByTaxonomy(obj,lvl,norm=norm,sl=sl, featureOrder=featureOrder) abundance = MRcounts(aggData,norm=FALSE,log=log,sl=1)[feature,] } else { abundance = MRcounts(obj,norm=norm,log=log,sl=sl)[feature,] } class = pData(obj)[,class] time = pData(obj)[,time] id = pData(obj)[,id] if(any(sapply(list(id,time,class),length)==0)){ stop("provide class, time, and id names") } if(!missing(formula)){ prep=ssFit(formula=formula,abundance=abundance,class=class, time=time,id=id,include=include,pd=pData(obj),...) } else { prep=ssFit(abundance=abundance,class=class,time=time,id=id, include=include,pd=pData(obj),...) } indexPos = ssIntervalCandidate(fit=prep$fit, standardError=prep$se, timePoints=prep$timePoints, positive=TRUE,C=C) indexNeg = ssIntervalCandidate(fit=prep$fit, standardError=prep$se, timePoints=prep$timePoints, positive=FALSE,C=C) indexAll = rbind(indexPos, indexNeg) if(sum(indexAll[,1]==indexAll[,2])>0){ indexAll=indexAll[-which(indexAll[,1]==indexAll[,2]),] } fit = 2*prep$fit se = 2*prep$se timePoints = prep$timePoints fits = data.frame(fit = fit, se = se, timePoints = timePoints) if(!is.null(indexAll)){ if(length(indexAll)>0){ indexAll=matrix(indexAll,ncol=4) colnames(indexAll)=c("Interval start", "Interval end", "Area", "p.value") predArea = cbind(prep$timePoints, (2*prep$fit)) permList = ssPerm(prep$data,B=B) if(!missing(formula)){ permResult = ssPermAnalysis(data=prep$data,formula=formula,permList=permList, intTimes=indexAll,timePoints=prep$timePoints,include=include,...) } else { permResult = ssPermAnalysis(data=prep$data,permList=permList, intTimes=indexAll,timePoints=prep$timePoints,include=include,...) } for (i in 1:nrow(indexAll)){ origArea=predArea[which(predArea[,1]==indexAll[i,1]):which(predArea[,1]==indexAll[i, 2]), ] actArea=trapz(x=origArea[,1], y=origArea[,2]) indexAll[i,3] = actArea if(actArea>0){ indexAll[i,4] = 1 - (length(which(actArea>permResult[,i]))+1)/(B+1) }else{ indexAll[i,4] = (length(which(actArea>permResult[,i]))+1)/(B+1) } if(indexAll[i,4]==0){ indexAll[i,4] = 1/(B+1) } } res = list(timeIntervals=indexAll,data=prep$data,fit=fits,perm=permResult) return(res) } }else{ indexAll = "No statistically significant time intervals detected" res = list(timeIntervals=indexAll,data=prep$data,fit=fits,perm=NULL) return(res) } } #' Discover differentially abundant time intervals #' #' Calculate time intervals of significant differential abundance. #' Currently only one method is implemented (ssanova). fitSSTimeSeries is called with method="ssanova". #' #' @param obj metagenomeSeq MRexperiment-class object. #' @param formula Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value. #' @param feature Name or row of feature of interest. #' @param class Name of column in phenoData of MRexperiment-class object for class memberhip. #' @param time Name of column in phenoData of MRexperiment-class object for relative time. #' @param id Name of column in phenoData of MRexperiment-class object for sample id. #' @param method Method to estimate time intervals of differentially abundant bacteria (only ssanova method implemented currently). #' @param lvl Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level). #' @param include Parameters to include in prediction. #' @param C Value for which difference function has to be larger or smaller than (default 0). #' @param B Number of permutations to perform. #' @param norm When aggregating counts to normalize or not. #' @param log Log2 transform. #' @param sl Scaling value. #' @param featureOrder Hierarchy of levels in taxonomy as fData colnames #' @param ... Options for ssanova #' @return List of matrix of time point intervals of interest, Difference in abundance area and p-value, fit, area permutations, and call. #' @return A list of objects including: #' \itemize{ #' \item{timeIntervals - Matrix of time point intervals of interest, area of differential abundance, and pvalue.} #' \item{data - Data frame of abundance, class indicator, time, and id input.} #' \item{fit - Data frame of fitted values of the difference in abundance, standard error estimates and timepoints interpolated over.} #' \item{perm - Differential abundance area estimates for each permutation.} #' \item{call - Function call.} #' } #' @rdname fitTimeSeries #' @seealso \code{\link{cumNorm}} \code{\link{fitSSTimeSeries}} \code{\link{plotTimeSeries}} #' @export #' @examples #' #' data(mouseData) #' res = fitTimeSeries(obj=mouseData,feature="Actinobacteria", #' class="status",id="mouseID",time="relativeTime",lvl='class',B=2) #' fitTimeSeries <- function(obj,formula,feature,class,time,id,method=c("ssanova"), lvl=NULL,include=c("class", "time:class"),C=0,B=1000, norm=TRUE,log=TRUE,sl=1000,featureOrder=NULL,...) { if(method=="ssanova"){ if(requireNamespace("gss")){ if(missing(formula)){ res = fitSSTimeSeries(obj=obj,feature=feature,class=class,time=time,id=id, lvl=lvl,C=C,B=B,norm=norm,log=log,sl=sl,include=include,featureOrder=featureOrder,...) } else { res = fitSSTimeSeries(obj=obj,formula=formula,feature=feature,class=class, time=time,id=id,lvl=lvl,C=C,B=B,norm=norm,log=log,sl=sl, include=include,featureOrder=featureOrder,...) } } } res = c(res,call=match.call()) return(res) } #' Plot difference function for particular bacteria #' #' Plot the difference in abundance for significant features. #' #' @param res Output of fitTimeSeries function #' @param C Value for which difference function has to be larger or smaller than (default 0). #' @param xlab X-label. #' @param ylab Y-label. #' @param main Main label. #' @param ... Extra plotting arguments. #' @return Plot of difference in abundance for significant features. #' @rdname plotTimeSeries #' @seealso \code{\link{fitTimeSeries}} #' @export #' @examples #' #' data(mouseData) #' res = fitTimeSeries(obj=mouseData,feature="Actinobacteria", #' class="status",id="mouseID",time="relativeTime",lvl='class',B=10) #' plotTimeSeries(res) #' plotTimeSeries<-function(res,C=0,xlab="Time",ylab="Difference in abundance",main="SS difference function prediction",...){ fit = res$fit$fit se = res$fit$se timePoints = res$fit$timePoints confInt95 = 1.96 sigDiff = res$timeIntervals minValue=min(fit-(confInt95*se))-.5 maxValue=max(fit+(confInt95*se))+.5 plot(x=timePoints, y=fit, ylim=c(minValue, maxValue), xlab=xlab, ylab=ylab, main=main, ...) for (i in 1:nrow(sigDiff)){ begin=sigDiff[i,1] end=sigDiff[i,2] indBegin=which(timePoints==begin) indEnd=which(timePoints==end) x=timePoints[indBegin:indEnd] y=fit[indBegin:indEnd] xx=c(x, rev(x)) yy=c(y, rep(0, length(y))) polygon(x=xx, yy, col="grey") } lines(x=timePoints, y=fit, pch="") lines(x=timePoints, y=fit+(confInt95*se), pch="", lty=2) lines(x=timePoints, y=fit-(confInt95*se), pch="", lty=2) abline(h=C) } #' Plot abundances by class #' #' Plot the abundance of values for each class using #' a spline approach on the estimated full model. #' #' @param res Output of fitTimeSeries function #' @param formula Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value. #' @param xlab X-label. #' @param ylab Y-label. #' @param color0 Color of samples from first group. #' @param color1 Color of samples from second group. #' @param include Parameters to include in prediction. #' @param ... Extra plotting arguments. #' @return Plot for abundances of each class using a spline approach on estimated null model. #' @rdname plotClassTimeSeries #' @seealso \code{\link{fitTimeSeries}} #' @export #' @examples #' #' data(mouseData) #' res = fitTimeSeries(obj=mouseData,feature="Actinobacteria", #' class="status",id="mouseID",time="relativeTime",lvl='class',B=10) #' plotClassTimeSeries(res,pch=21,bg=res$data$class,ylim=c(0,8)) #' plotClassTimeSeries<-function(res,formula,xlab="Time",ylab="Abundance",color0="black", color1="red",include=c("1","class", "time:class"),...){ dat = res$data if(missing(formula)){ mod = gss::ssanova(abundance ~ time * class, data=dat) } else{ mod = gss::ssanova(formula,data=dat) } timePoints = seq(min(dat$time),max(dat$time),by=1) group0 = data.frame(time=timePoints,class=factor(levels(dat$class)[1])) group1 = data.frame(time=timePoints,class=factor(levels(dat$class)[2])) pred0 = predict(mod, newdata=group0,include=include, se=TRUE) pred1 = predict(mod, newdata=group1,include=include, se=TRUE) plot(x=dat$time,y=dat$abundance,xlab=xlab,ylab=ylab,...) lines(x=group0$time,y=pred0$fit,col=color0) lines(x=group0$time,y=pred0$fit+(1.96*pred0$se),lty=2,col=color0) lines(x=group0$time,y=pred0$fit-(1.96*pred0$se),lty=2,col=color0) lines(x=group1$time,y=pred1$fit,col=color1) lines(x=group1$time,y=pred1$fit+(1.96*pred1$se),lty=2,col=color1) lines(x=group1$time,y=pred1$fit-(1.96*pred1$se),lty=2,col=color1) } #' Discover differentially abundant time intervals for all bacteria #' #' Calculate time intervals of significant differential abundance over all #' bacteria of a particularly specified level (lvl). If not lvl is specified, #' all OTUs are analyzed. Warning, function can take a while #' #' @param obj metagenomeSeq MRexperiment-class object. #' @param lvl Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level). #' @param B Number of permutations to perform. #' @param featureOrder Hierarchy of levels in taxonomy as fData colnames #' @param ... Options for \code{\link{fitTimeSeries}}, except feature. #' @return List of lists of matrices of time point intervals of interest, Difference in abundance area and p-value, fit, area permutations. #' @return A list of lists for which each includes: #' \itemize{ #' \item{timeIntervals - Matrix of time point intervals of interest, area of differential abundance, and pvalue.} #' \item{data - Data frame of abundance, class indicator, time, and id input.} #' \item{fit - Data frame of fitted values of the difference in abundance, standard error estimates and timepoints interpolated over.} #' \item{perm - Differential abundance area estimates for each permutation.} #' \item{call - Function call.} #' } #' @rdname fitMultipleTimeSeries #' @seealso \code{\link{cumNorm}} \code{\link{fitSSTimeSeries}} \code{\link{fitTimeSeries}} #' @export #' @examples #' #' data(mouseData) #' res = fitMultipleTimeSeries(obj=mouseData,lvl='phylum',class="status", #' id="mouseID",time="relativeTime",B=1) #' fitMultipleTimeSeries <- function(obj,lvl=NULL,B=1,featureOrder=NULL,...) { if(is.null(lvl)){ bacteria = seq(nrow(obj)) } else { if(is.factor(fData(obj)[,lvl])){ fData(obj)[,lvl] = as.character(fData(obj)[,lvl]) } bacteria = unique(fData(obj)[,lvl]) } fits = lapply(bacteria,function(bact){ try(fitTimeSeries(obj,lvl=lvl,feature=bact,B=B,featureOrder=featureOrder,...)) }) names(fits) = bacteria fits = c(fits,call=match.call()) return(fits) } #' With a list of fitTimeSeries results, generate #' an MRexperiment that can be plotted with metavizr #' #' @param obj Output of fitMultipleTimeSeries #' @param sampleNames Sample names for plot #' @param sampleDescription Description of samples for plot axis label #' @param taxonomyLevels Feature names for plot #' @param taxonomyHierarchyRoot Root of feature hierarchy for MRexperiment #' @param taxonomyDescription Description of features for plot axis label #' @param featuresOfInterest The features to select from the fitMultipleTimeSeries output #' @param featureDataOfInterest featureData for the resulting MRexperiment #' @return MRexperiment that contains fitTimeSeries data, featureData, and phenoData #' @rdname ts2MRexperiment #' @seealso \code{\link{fitTimeSeries}} \code{\link{fitMultipleTimeSeries}} #' @export #' @examples #' #' data(mouseData) #' res = fitMultipleTimeSeries(obj=mouseData,lvl='phylum',class="status", #' id="mouseID",time="relativeTime",B=1) #' obj = ts2MRexperiment(res) #' obj #' ts2MRexperiment<-function(obj,sampleNames=NULL, sampleDescription="timepoints", taxonomyLevels=NULL, taxonomyHierarchyRoot="bacteria", taxonomyDescription="taxonomy", featuresOfInterest = NULL, featureDataOfInterest=NULL){ if(is.null(obj)){ stop("Matrix cannot be null") } if(is.null(sampleNames)){ numSamples <- dim(obj[[1]]$fit)[1] sampleNames <- paste("Timepoint", 1:numSamples, sep="_") } if(is.null(featuresOfInterest)){ hasFit <- lapply(1:(length(obj)-1), function(i) which(!is.null(obj[[i]]$fit))) featuresOfInterest <- which(hasFit == 1) hasFit <- (hasFit == 1) hasFit <- !is.na(hasFit) temp <- 1:length(hasFit) temp[!hasFit] <- 0 hasFit <- temp } if(is.null(taxonomyLevels)){ numLevels <- 1:length(hasFit) taxonomyLevels <- names(obj)[1:length(hasFit)] } numSamples <- length(sampleNames) numLevels <- length(taxonomyLevels) numFeaturesOfInterest <- length(featuresOfInterest) rangeSamples <- 1:numSamples rangeFeaturesOfInterest <- 1:numFeaturesOfInterest # print(hasFit) results <- do.call(rbind, lapply(hasFit,function(i){ if (i != 0) t(obj[[i]]$fit)[1,] else rep(NA, numSamples) })) dfSamples <- data.frame(x=rangeSamples,row.names=sampleNames) metaDataSamples <-data.frame(labelDescription=sampleDescription) annotatedDFSamples <- AnnotatedDataFrame() pData(annotatedDFSamples) <- dfSamples varMetadata(annotatedDFSamples) <- metaDataSamples validObject(annotatedDFSamples) if(is.null(featureDataOfInterest)){ dfFeatures <- data.frame(taxonomy1=rep(taxonomyHierarchyRoot, numLevels),taxonomy2=taxonomyLevels) metaDataFeatures <-data.frame(labelDescription=paste(taxonomyDescription, 1:2, sep="")) annotatedDFFeatures <- AnnotatedDataFrame() pData(annotatedDFFeatures) <- dfFeatures varMetadata(annotatedDFFeatures) <- metaDataFeatures validObject(annotatedDFFeatures) } else{ annotatedDFFeatures <- featureDataOfInterest } fitTimeSeriesMRexp <- newMRexperiment(counts=results, phenoData=annotatedDFSamples, featureData=annotatedDFFeatures) return(fitTimeSeriesMRexp) } # load("~/Dropbox/Projects/metastats/package/git/metagenomeSeq/data/mouseData.rda") # classMatrix = aggregateByTaxonomy(mouseData,lvl='class',norm=TRUE,out='MRexperiment') # data(mouseData) # fitTimeSeries(obj=mouseData,feature="Actinobacteria",class="status",id="mouseID",time="relativeTime",lvl='class',B=10) metagenomeSeq/R/fitZeroLogNormal.R0000644000175200017520000002523414710220170020147 0ustar00biocbuildbiocbuild#' Compute the log fold-change estimates for the zero-inflated log-normal model #' #' Run the zero-inflated log-normal model given a MRexperiment object #' and model matrix. Not for the average user, assumes structure of the model matrix. #' #' @param obj A MRexperiment object with count data. #' @param mod The model for the count distribution. #' @param coef Coefficient of interest to grab log fold-changes. #' @param szero TRUE/FALSE, shrink zero component parameters. #' @param spos TRUE/FALSE, shrink positive component parameters. #' @return A list of objects including: #' \itemize{ #' \item{logFC - the log fold-change estimates} #' \item{adjFactor - the adjustment factor based on the zero component} #' \item{se - standard error estimates} #' \item{fitln - parameters from the log-normal fit} #' \item{fitzero - parameters from the logistic fit} #' \item{zeroRidge - output from the ridge regression} #' \item{posRidge - output from the ridge regression} #' \item{tauPos - estimated tau^2 for positive component} #' \item{tauZero - estimated tau^2 for zero component} #' \item{exclude - features to exclude for various reasons, e.g. all zeros} #' \item{zeroExclude - features to exclude for various reasons, e.g. all zeros} #' } #' @seealso \code{\link{cumNorm}} \code{\link{fitFeatureModel}} fitZeroLogNormal<-function(obj,mod,coef=2,szero=TRUE,spos=TRUE){ positiveMod = mod[,-ncol(mod)] zeroMod = mod nf <- normFactors(obj) mat <- MRcounts(obj, norm=TRUE, log=FALSE,sl=median(nf)) posIndices = mat>0 nr = nrow(mat) nc = ncol(mat) exclude = zeroExclude = tauZero = tauPos = posRidge = zeroRidge = NULL results = array(NA,dim=c(nr,3)) rownames(results) = rownames(mat) colnames(results) = c("logFC","adjFactor","se") # calc log-normal component fitln = calcPosComponent(mat,positiveMod,posIndices) # Don't calculate shrinkage with special cases zeros2 = which(fitln[,"s2"]==0) rs = rowsum(t(1-(1-posIndices)),positiveMod[,coef]) exclude = union(which(rs[1,]<=1),which(rs[2,]<=1)) zeroExclude = which(colSums(rs)>=(nc-3)) exclude = union(zeros2,exclude); if(length(exclude)==0) exclude=NULL if(length(zeroExclude)==0) zeroExclude=NULL sdensity = density(fitln[,"s2"],na.rm=TRUE) smode = sdensity$x[which.max(sdensity$y)] if(length(zeros2)>0) fitln[zeros2,"s2"] = smode # shrink positive if(spos==TRUE){ shrinkPos<-calcShrinkParameters(fitln,coef,smode,exclude) tauPos = shrinkPos$tau vpost = shrinkPos$v.post fitln[,"s2"] = vpost posRidge = sapply(seq(nr),function(i){ k = which(posIndices[i,]) y = log(mat[i,k]) x = positiveMod[k,] l = vpost[i]/(nrow(x)*tauPos) if(i %in% exclude) return(matrix(rep(NA,ncol(positiveMod)))) ridge = glmnet(y=y,x=x,lambda=l,alpha=0) as.matrix(coefficients(ridge)[colnames(positiveMod),]) }) posFittedCoefficients = t(posRidge) rownames(posFittedCoefficients) = rownames(mat) fitln[rownames(posFittedCoefficients),1:ncol(positiveMod)] = posFittedCoefficients } # calc zero component fitzero=calcZeroComponent(mat,zeroMod,posIndices) sdensity = density(fitzero[,"s2"],na.rm=TRUE) smode = sdensity$x[which.max(sdensity$y)] if(length(exclude)>0) fitzero[exclude,"s2"] = smode # shrink zero if(szero==TRUE){ shrinkZero<-calcShrinkParameters(fitzero,coef,smode,exclude) tauZero = shrinkZero$tau vpostZero = shrinkZero$v.post fitzero[,"s2"] = vpostZero zeroRidge = sapply(1:nr,function(i){ y = posIndices[i,] l = 1/(nc*tauZero) if(i %in% c(zeroExclude,exclude)) return(matrix(rep(NA,ncol(zeroMod)))) ridge = glmnet(y=y,x=zeroMod,lambda=l,family="binomial",alpha=0, penalty.factor = c(rep(1,(ncol(zeroMod)-1)),0)) as.matrix(coefficients(ridge))[colnames(zeroMod),] }) zeroFittedCoefficients = t(zeroRidge) rownames(zeroFittedCoefficients) = rownames(mat) fitzero[rownames(zeroFittedCoefficients),1:ncol(zeroMod)] = zeroFittedCoefficients } # calc se se = calcStandardError(zeroMod,fitln,fitzero,coef=coef,exclude=union(exclude,zeroExclude)) se[zeroExclude] = sqrt(fitln[zeroExclude,"s2"]) # calc adjFactor adjFactor = calcZeroAdjustment(fitln,fitzero,zeroMod,coef,exclude=exclude) adjFactor[zeroExclude] = 0 # calc logFC logFC <- fitln[,coef] + adjFactor list(logFC=logFC,adjFactor=adjFactor,se=se, fitln=fitln,fitzero=fitzero,zeroRidge=zeroRidge,posRidge=posRidge, tauPos=tauPos,tauZero=tauZero,exclude=exclude,zeroExclude=zeroExclude) } #' Positive component #' #' Fit the positive (log-normal) component #' #' @param mat A matrix of normalized counts #' @param mod A model matrix #' @param weights Weight matrix for samples and counts #' @seealso \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} calcPosComponent<-function(mat,mod,weights){ fitln <- lmFit(log(mat),mod,weights=weights) b = coefficients(fitln) df = fitln$df res = residuals(fitln,log(mat)) s2 = sapply(seq(nrow(res)),function(i){ sum(res[i,which(weights[i,])]^2,na.rm=TRUE)/df[i] }) fitln<-data.frame(b=b,s2=s2,df=df) rownames(fitln) = rownames(mat) fitln } #' Zero component #' #' Fit the zero (logisitic) component #' #' @param mat A matrix of normalized counts #' @param mod A model matrix #' @param weights Weight matrix for samples and counts #' @seealso \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} calcZeroComponent<-function(mat,mod,weights){ fitzero <- sapply(seq(nrow(mat)), function(i) { fit <- glm.fit(mod, weights[i,], family=binomial()) cf = coefficients(fit) df = fit$df.residual mc = exp(mod %*% cf) s2 = sum((weights[i, ] - t(mc/(1 + mc)))^2)/df # s2 = sum(residuals(fit)^2)/df c(beta= cf, s2 = s2, df = df) }) fitzero <- data.frame(t(fitzero)) rownames(fitzero) = rownames(mat) fitzero } #' Calculate shrinkage parameters #' #' Calculate the shrunken variances and variance of parameters of interest across features. #' #' @param fit A matrix of fits as outputted by calcZeroComponent or calcPosComponent #' @param coef Coefficient of interest #' @param mins2 minimum variance estimate #' @param exclude Vector of features to exclude when shrinking #' @seealso \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} calcShrinkParameters<-function(fit,coef,mins2,exclude=NULL){ if(is.null(exclude)){ shrunkVar <- limma::squeezeVar(fit[,"s2"], fit[,"df"]) v.post = shrunkVar$var.post tau <-var(fit[,coef],na.rm=TRUE) } else { v.post = rep(mins2,nrow(fit)) shrunkVar <- limma::squeezeVar(fit[-exclude,"s2"], fit[-exclude,"df"]) v.post[-exclude] <- shrunkVar$var.post tau <- var(fit[-exclude,coef],na.rm=TRUE) } list(tau=tau,v.post=v.post) } #' Calculate the zero-inflated component's adjustment factor #' #' Calculate the log ratio of average marginal probabilities for each sample #' having a positive count. This becomes the adjustment factor for the log #' fold change. #' #' @param fitln A matrix with parameters from the log-normal fit #' @param fitzero A matrix with parameters from the logistic fit #' @param mod The zero component model matrix #' @param coef Coefficient of interest #' @param exclude List of features to exclude #' @seealso \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} calcZeroAdjustment<-function(fitln,fitzero,mod,coef,exclude=NULL){ b = fitln[,1:(ncol(mod)-1)] beta = fitzero[,1:ncol(mod)] # calculate for zero adjust factor mod1 <- mod mod1[,coef] <- 1 theta1 <- mod1 %*% t(beta) p1 <- exp(theta1) / (1+exp(theta1)) p1 <- t(p1) if(ncol(b)>2) p1 = p1*exp(t(mod[,3:(ncol(mod)-1)]%*%t(b[,3:ncol(b)]))) mean_p1 <- rowMeans(p1) mod0 <- mod mod0[,coef] <- 0 theta0 <- mod0 %*% t(beta) p0 <- exp(theta0) / (1+exp(theta0)) p0 <- t(p0) if(ncol(b)>2) p0 = p0*exp(t(mod[,3:(ncol(mod)-1)]%*%t(b[,3:ncol(b)]))) mean_p0 <- rowMeans(p0) adjFactor <- log(mean_p1/mean_p0) if(!is.null(exclude)) adjFactor[exclude] = NA adjFactor } #' Calculate the zero-inflated log-normal statistic's standard error #' #' Calculat the se for the model. Code modified from #' "Adjusting for covariates in zero-inflated gamma and #' zero-inflated log-normal models for semicontinuous data", ED Mills #' #' @param mod The zero component model matrix #' @param fitln A matrix with parameters from the log-normal fit #' @param fitzero A matrix with parameters from the logistic fit #' @param coef Coefficient of interest #' @param exclude List of features to exclude #' @seealso \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} calcStandardError<-function(mod,fitln,fitzero,coef=2,exclude=NULL){ mod0 = mod1 = mod mod1[,coef] <- 1 mod0[,coef] <- 0 ve = rep(NA,nrow(fitln)) features = seq(nrow(fitln)) if(length(exclude)>0) features = features[-exclude] # a) need to speed up # b) need to include more covariates fullvar = sapply(features,function(i){ beta = fitzero[i,1:ncol(mod)] b = fitln[i,1:(ncol(mod)-1)] s = as.numeric(fitln[i,"s2"]) mu0 = as.vector(exp(mod0[,-ncol(mod)]%*%t(b) + .5*s)) mu1 = as.vector(exp(mod1[,-ncol(mod)]%*%t(b) + .5*s)) # calculate for zero adjust factor theta <- mod %*% t(beta) theta1 <- mod1 %*% t(beta) theta0 <- mod0 %*% t(beta) p <- t(exp(theta) / (1+exp(theta))) p1 <- t(exp(theta1) / (1+exp(theta1))) p0 <- t(exp(theta0) / (1+exp(theta0))) checkInverse <- function(m){ inherits(try(qr.solve(m),silent=T), "matrix") } Dp2 <- diag(length(p))*as.vector(p*(1-p)) infz = t(mod)%*%Dp2%*%mod Dp <- diag(length(p))*as.vector(p) infln = t(mod[,-ncol(mod)])%*%Dp%*%mod[,-ncol(mod)] if(checkInverse(infz)) { invinf_z <-qr.solve(infz) } else { return(NA) } if(checkInverse(infln)) { invinf_ln<-as.numeric(s)*qr.solve(infln) } else { return(NA) } invInfFull = as.matrix( bdiag(invinf_z,invinf_ln, (2*s^2/sum(p))) ) logRatioBeta0<- (mean(p1*(1-p1)*mu0)/mean(p1*mu0)) - (mean(p0*(1-p0)*mu0)/mean(p0*mu0)) logRatioBeta1<-mean(p1*(1-p1)*mu0)/mean(p1*mu0) logRatioBeta2<- (mean(mod[,3]*p1*(1-p1)*mu0)/mean(p1*mu0)) - (mean(mod[,3]*p0*(1-p0)*mu0)/mean(p0*mu0)) # logRatioB2<- (mean(mod[,3]*t(p1)*exp(mod0%*%t(b)))/mean(t(p1)*exp(mod0%*%t(b))))- # (mean(mod[,3]*t(p0)*exp(mod0%*%t(b)))/mean(t(p0)*exp(mod0%*%t(b)))) # logRatioFull = t(c(logRatioBeta0,logRatioBeta1,logRatioBeta2,0,1,logRatioB2,0)) logRatioFull = t(c(logRatioBeta0,logRatioBeta1,logRatioBeta2,0,1,0)) logRatioVar = logRatioFull%*%invInfFull%*%t(logRatioFull) logRatioVar }) if(!is.null(exclude)){ if(length(features)>0){ ve[features] = fullvar } } else { ve = fullvar } sqrt(ve) } metagenomeSeq/R/fitZig.R0000644000175200017520000002263414735552263016171 0ustar00biocbuildbiocbuild#' Computes the weighted fold-change estimates and t-statistics. #' #' Wrapper to actually run the Expectation-maximization algorithm and estimate #' $f_count$ fits. Maximum-likelihood estimates are approximated using the EM #' algorithm where we treat mixture membership $delta_ij = 1$ if $y_ij$ is #' generated from the zero point mass as latent indicator variables. The #' density is defined as $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * #' f_count(y_ij; mu_i, sigma_i^2)$. The log-likelihood in this extended model #' is: $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log #' pi_j(s_j)+(1-delta_ij) log (1-pi_j (s_j))$. The responsibilities are defined #' as $z_ij = pr(delta_ij=1 | data)$. #' #' #' @param obj A MRexperiment object with count data. #' @param mod The model for the count distribution. #' @param zeroMod The zero model, the model to account for the change in the #' number of OTUs observed as a linear effect of the depth of coverage. #' @param useCSSoffset Boolean, whether to include the default scaling #' parameters in the model or not. #' @param control The settings for fitZig. #' @param useMixedModel Estimate the correlation between duplicate #' features or replicates using duplicateCorrelation. #' @param ... Additional parameters for duplicateCorrelation. #' @return A list of objects including: #' \itemize{ #' \item{call - the call made to fitZig} #' \item{fit - 'MLArrayLM' Limma object of the weighted fit} #' \item{countResiduals - standardized residuals of the fit} #' \item{z - matrix of the posterior probabilities} #' \item{eb - output of eBayes, moderated t-statistics, moderated F-statistics, etc} #' \item{taxa - vector of the taxa names} #' \item{counts - the original count matrix input} #' \item{zeroMod - the zero model matrix} #' \item{zeroCoef - the zero model fitted results} #' \item{stillActive - convergence} #' \item{stillActiveNLL - nll at convergence} #' \item{dupcor - correlation of duplicates} #' } #' @export #' @seealso \code{\link{cumNorm}} \code{\link{zigControl}} #' @examples #' #' # This is a simple demonstration #' data(lungData) #' k = grep("Extraction.Control",pData(lungData)$SampleType) #' lungTrim = lungData[,-k] #' k = which(rowSums(MRcounts(lungTrim)>0)<30) #' lungTrim = cumNorm(lungTrim) #' lungTrim = lungTrim[-k,] #' smokingStatus = pData(lungTrim)$SmokingStatus #' mod = model.matrix(~smokingStatus) #' # The maxit is not meant to be 1 - this is for demonstration/speed #' settings = zigControl(maxit=1,verbose=FALSE) #' fit = fitZig(obj = lungTrim,mod=mod,control=settings) #' fitZig <- function(obj, mod, zeroMod=NULL, useCSSoffset=TRUE, control=zigControl(), useMixedModel=FALSE, ...) { stopifnot( is( obj, "MRexperiment" ) ) if(any(is.na(normFactors(obj)))) stop("At least one NA normalization factors") if(any(is.na(libSize(obj)))) stop("Calculate the library size first!") y <- MRcounts(obj, norm=FALSE, log=FALSE) nc <- ncol(y) #nsamples nr <- nrow(y) #nfeatures # Normalization step Nmatrix <- log2(y + 1) # Initializing the model matrix if (useCSSoffset == TRUE){ if (any(is.na(normFactors(obj)))) { stop("Calculate the normalization factors first!") } mmCount <- cbind(mod, log2(normFactors(obj)/1000 + 1)) colnames(mmCount)[ncol(mmCount)] <- "scalingFactor" } else { mmCount <- mod } if (is.null(zeroMod)) { if (any(is.na(libSize(obj)))) { stop("Calculate the library size first!") } mmZero <- model.matrix(~1+log(libSize(obj))) } else { mmZero <- zeroMod } dat <- .do_fitZig(Nmatrix, mmCount, mmZero, control=control, useMixedModel=useMixedModel, ...) assayData(obj)[["z"]] <- dat$z assayData(obj)[["zUsed"]] <- dat$zUsed dat$zUsed <- NULL dat <- c(dat, list(call=match.call(),taxa=rownames(obj),counts=y)) # old way of outputting results with list # dat <- c(dat, list(call=match.call(),taxa=rownames(obj),counts=y)) # new output with defined results class dat <- new("fitZigResults", fit=dat$fit, countResiduals=dat$countResiduals, z=dat$z, zUsed=dat$zUsed, eb=dat$eb, zeroMod=dat$zeroMod, stillActive=dat$stillActive, stillActiveNLL=dat$stillActiveNLL, zeroCoef=dat$zeroCoef, dupcor=dat$dupcor, call = dat$call, taxa = rownames(obj), counts = dat$counts) dat } .do_fitZig <- function(y, count_model_matrix, zero_model_matrix, control=zigControl(), useMixedModel=FALSE, ...) { # Initialization tol <- control$tol maxit <- control$maxit verbose <- control$verbose dfMethod <- control$dfMethod pvalMethod <- control$pvalMethod nr <- nrow(y) nc <- ncol(y) zeroIndices <- (y == 0) z <- matrix(0, nrow=nr, ncol=nc) z[zeroIndices] <- 0.5 zUsed <- z curIt <- 0 nllOld <- rep(Inf, nr) nll <- rep(Inf, nr) nllUSED <- nll stillActive <- rep(TRUE, nr) stillActiveNLL <- rep(1, nr) dupcor <- NULL modRank <- ncol(count_model_matrix) # E-M Algorithm while (any(stillActive) && (curIt < maxit)) { # M-step for count density (each feature independently) if(curIt == 0){ fit <- doCountMStep(z, y, count_model_matrix, stillActive, dfMethod=dfMethod) } else { fit <- doCountMStep(z, y, count_model_matrix, stillActive, fit2=fit, dfMethod=dfMethod) } # M-step for zero density (all features together) zeroCoef <- doZeroMStep(z, zeroIndices, zero_model_matrix) # E-step z <- doEStep(fit$residuals, zeroCoef$residuals, zeroIndices) zzdata <- getZ(z, zUsed, stillActive, nll, nllUSED); zUsed <- zzdata$zUsed; # NLL nll <- getNegativeLogLikelihoods(z, fit$residuals, zeroCoef$residuals) eps <- getEpsilon(nll, nllOld) active <- isItStillActive(eps, tol,stillActive,stillActiveNLL,nll) stillActive <- active$stillActive; stillActiveNLL <- active$stillActiveNLL; if (verbose == TRUE){ cat(sprintf("it=%2d, nll=%0.2f, log10(eps+1)=%0.2f, stillActive=%d\n", curIt, mean(nll,na.rm=TRUE), log10(max(eps,na.rm=TRUE)+1), sum(stillActive))) } nllOld <- nll curIt <- curIt + 1 if (sum(rowSums((1-z) > 0) <= modRank, na.rm=TRUE) > 0) { k <- which(rowSums((1-z) > 0) <= modRank) stillActive[k] <- FALSE; stillActiveNLL[k] <- nll[k] } } if (useMixedModel == TRUE) { dupcor <- duplicateCorrelation(y, count_model_matrix, weights=(1-z), ...) fit$fit <- limma::lmFit(y, count_model_matrix, weights=(1-z), correlation=dupcor$consensus, ...) countCoef <- fit$fit$coefficients countMu <- tcrossprod(countCoef, count_model_matrix) fit$residuals <- sweep((y-countMu), 1, fit$fit$sigma, "/") } eb <- limma::eBayes(fit$fit,legacy=TRUE) dat <- list(fit=fit$fit, countResiduals=fit$residuals, z=z, zUsed=zUsed, eb=eb, zeroMod=zero_model_matrix, stillActive=stillActive, stillActiveNLL=stillActiveNLL, zeroCoef=zeroCoef, dupcor=dupcor) dat } # #' Function to perform fitZig bootstrap # #' # #' Calculates bootstrap stats # #' # #' @param y Log-transformed matrix # #' @param y string for the y-axis # #' @param norm is the data normalized? # #' @param log is the data logged? # #' @return vector of x,y labels # #' # performBoostrap<-function(fit){ # zeroIndices=(y==0) # z=matrix(0,nrow=nr, ncol=nc) # z[zeroIndices]=0.5 # zUsed = z # curIt=0 # nllOld=rep(Inf, nr) # nll=rep(Inf, nr) # nllUSED=nll # stillActive=rep(TRUE, nr) # stillActiveNLL=rep(1, nr) # tt <- fit$fit$coef[,coef] / fit$fit$stdev.unscaled[,coef] / fit$fit$sigma # perms = replicate(B,sample(mmCount[,coef])) # mmCount1=mmCount[,-coef] # # Normalization step # Nmatrix = log2(y+1) # # Initializing the model matrix # if(useCSSoffset==TRUE){ # if(any(is.na(normFactors(obj)))){stop("Calculate the normalization factors first!")} # mmCount=cbind(mod,log2(normFactors(obj)/1000 +1)) # colnames(mmCount)[ncol(mmCount)] = "scalingFactor" # } # else{ # mmCount=mod # } # if(is.null(zeroMod)){ # if(any(is.na(libSize(obj)))){ stop("Calculate the library size first!") } # mmZero=model.matrix(~1+log(libSize(obj))) # } else{ # mmZero=zeroMod # } # modRank=ncol(mmCount) # # E-M Algorithm # while(any(stillActive) && curIt0)<=modRank,na.rm=TRUE)>0){ # k = which(rowSums((1-z)>0)<=modRank) # stillActive[k] = FALSE; # stillActiveNLL[k] = nll[k] # } # } # } metagenomeSeq/R/getCountDensity.R0000644000175200017520000000204014710220170020030 0ustar00biocbuildbiocbuild#' Compute the value of the count density function from the count model #' residuals. #' #' Calculate density values from a normal: $f(x) = 1/(sqrt (2 pi ) sigma ) #' e^-((x - mu )^2/(2 sigma^2))$. Maximum-likelihood estimates are #' approximated using the EM algorithm where we treat mixture membership #' $deta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent #' indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j) cdot #' f_0(y_ij) +(1-pi_j (S_j))cdot f_count(y_ij;mu_i,sigma_i^2)$. The #' log-likelihood in this extended model is $(1-delta_ij) log #' f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j #' (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. #' #' #' @param residuals Residuals from the count model. #' @param log Whether or not we are calculating from a log-normal distribution. #' @return Density values from the count model residuals. #' @seealso \code{\link{fitZig}} getCountDensity <- function(residuals, log=FALSE){ dnorm(residuals,log=log) } metagenomeSeq/R/getEpsilon.R0000644000175200017520000000165414710220170017023 0ustar00biocbuildbiocbuild#' Calculate the relative difference between iterations of the negative #' log-likelihoods. #' #' Maximum-likelihood estimates are approximated using the EM algorithm where #' we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the #' zero point mass as latent indicator variables. The log-likelihood in this #' extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log #' pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined #' as $z_ij = pr(delta_ij=1 | data)$. #' #' #' @param nll Vector of size M with the current negative log-likelihoods. #' @param nllOld Vector of size M with the previous iterations negative #' log-likelihoods. #' @return Vector of size M of the relative differences between the previous #' and current iteration nll. #' @seealso \code{\link{fitZig}} getEpsilon <- function(nll, nllOld){ eps=(nllOld-nll)/nllOld ifelse(!is.finite(nllOld), Inf, eps) } metagenomeSeq/R/getNegativeLogLikelihoods.R0000644000175200017520000000224314710220170022000 0ustar00biocbuildbiocbuild#' Calculate the negative log-likelihoods for the various features given the #' residuals. #' #' Maximum-likelihood estimates are approximated using the EM algorithm where #' we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the #' zero point mass as latent indicator variables. The log-likelihood in this #' extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log #' pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined #' as $z_ij = pr(delta_ij=1 | data and current values)$. #' #' #' @param z Matrix (m x n) of estimate responsibilities (probabilities that a #' count comes from a spike distribution at 0). #' @param countResiduals Residuals from the count model. #' @param zeroResiduals Residuals from the zero model. #' @return Vector of size M of the negative log-likelihoods for the various #' features. #' @seealso \code{\link{fitZig}} getNegativeLogLikelihoods <- function(z, countResiduals, zeroResiduals){ pi=getPi(zeroResiduals) countDensity=getCountDensity(countResiduals, log=TRUE) res=(1-z) * countDensity res=res+sweep(z, 2, log(pi), FUN="*") res=res+sweep(1-z,2,log(1-pi), FUN="*") -rowSums(res) } metagenomeSeq/R/getPi.R0000644000175200017520000000115114710220170015752 0ustar00biocbuildbiocbuild#' Calculate the mixture proportions from the zero model / spike mass model #' residuals. #' #' F(x) = 1 / (1 + exp(-(x-m)/s)) (the CDF of the logistic distribution). #' Provides the probability that a real-valued random variable X with a given #' probability distribution will be found at a value less than or equal to x. #' The output are the mixture proportions for the samples given the residuals #' from the zero model. #' #' #' @param residuals Residuals from the zero model. #' @return Mixture proportions for each sample. #' @seealso \code{\link{fitZig}} getPi <- function(residuals){ plogis(residuals) } metagenomeSeq/R/getZ.R0000644000175200017520000000210014710220170015606 0ustar00biocbuildbiocbuild#' Calculate the current Z estimate responsibilities (posterior probabilities) #' #' Calculate the current Z estimate responsibilities (posterior probabilities) #' #' #' @param z Matrix (m x n) of estimate responsibilities (probabilities that a #' count comes from a spike distribution at 0). #' @param zUsed Matrix (m x n) of estimate responsibilities (probabilities that #' a count comes from a spike distribution at 0) that are actually used #' (following convergence). #' @param stillActive A vector of size M booleans saying if a feature is still #' active or not. #' @param nll Vector of size M with the current negative log-likelihoods. #' @param nllUSED Vector of size M with the converged negative log-likelihoods. #' @return A list of updated zUsed and nllUSED. #' @seealso \code{\link{fitZig}} getZ <- function(z,zUsed,stillActive,nll,nllUSED){ nllUSED[stillActive] = nll[stillActive] k =which(nll< (nllUSED)) if(length(k)>0){ zUsed[k,]=z[k,] nllUSED[k] = nll[k] } zUsed[stillActive,] = z[stillActive,] dat = list(zUsed = zUsed,nllUSED = nllUSED) return(dat); } metagenomeSeq/R/isItStillActive.R0000644000175200017520000000223514710220170017762 0ustar00biocbuildbiocbuild#' Function to determine if a feature is still active. #' #' In the Expectation Maximization routine features posterior probabilities routinely converge based on a tolerance threshold. This function checks #' whether or not the feature's negative log-likelihood (measure of the fit) has changed or not. #' #' @param eps Vector of size M (features) representing the relative difference between the new nll and old nll. #' @param tol The threshold tolerance for the difference #' @param stillActive A vector of size M booleans saying if a feature is still active or not. #' @param stillActiveNLL A vector of size M recording the negative log-likelihoods of the various features, updated for those still active. #' @param nll Vector of size M with the current negative log-likelihoods. #' @return None. #' #' @name isItStillActive #' @seealso \code{\link{fitZig}} #' isItStillActive <- function(eps, tol,stillActive,stillActiveNLL,nll){ stillActive[stillActive]=!is.finite(eps[stillActive]) | eps[stillActive]>tol stillActive[which(is.na(eps))]=FALSE stillActiveNLL[stillActive]=nll[stillActive] dat = list(stillActive=stillActive,stillActiveNLL = stillActiveNLL) return(dat) } metagenomeSeq/R/loadBiom.R0000644000175200017520000000115014710220170016427 0ustar00biocbuildbiocbuild#' Load objects organized in the Biom format. #' #' Wrapper to load Biom formatted object. #' #' @param file The biom object filepath. #' @return A MRexperiment object. #' @seealso \code{\link{loadMeta}} \code{\link{loadPhenoData}} \code{\link{newMRexperiment}} \code{\link{biom2MRexperiment}} #' @examples #' #' #library(biomformat) #' rich_dense_file = system.file("extdata", "rich_dense_otu_table.biom", package = "biomformat") #' x = loadBiom(rich_dense_file) #' x loadBiom <- function(file){ requireNamespace("biomformat") x = biomformat::read_biom(file); mrobj = biom2MRexperiment(x); return(mrobj); } metagenomeSeq/R/loadMeta.R0000644000175200017520000000163614710220170016440 0ustar00biocbuildbiocbuild#' Load a count dataset associated with a study. #' #' Load a matrix of OTUs in a tab delimited format #' #' #' @aliases loadMeta metagenomicLoader #' @param file Path and filename of the actual data file. #' @param sep File delimiter. #' @return A list with objects 'counts' and 'taxa'. #' @seealso \code{\link{loadPhenoData}} #' @examples #' #' dataDirectory <- system.file("extdata", package="metagenomeSeq") #' lung = loadMeta(file.path(dataDirectory,"CHK_NAME.otus.count.csv")) #' loadMeta <- function(file,sep="\t") { dat2 <- read.table(file,header=FALSE,sep=sep,nrows=1,stringsAsFactors=FALSE); subjects <- as.character(dat2[1,-1]); classes <-c("character",rep("numeric",length(subjects))); dat3 <- read.table(file,header=FALSE,skip=1,sep=sep,colClasses=classes,row.names=1); colnames(dat3) = subjects taxa<- rownames(dat3); obj <- list(counts=as.data.frame(dat3), taxa=as.data.frame(taxa)) return(obj); } metagenomeSeq/R/loadMetaQ.R0000644000175200017520000000173614710220170016562 0ustar00biocbuildbiocbuild#' Load a count dataset associated with a study set up in a Qiime format. #' #' Load a matrix of OTUs in Qiime's format #' #' #' @aliases loadMetaQ qiimeLoader #' @param file Path and filename of the actual data file. #' @return An list with 'counts' containing the count data, 'taxa' containing the otu annotation, and 'otus'. #' @seealso \code{\link{loadMeta}} \code{\link{loadPhenoData}} #' @examples #' #' # see vignette #' loadMetaQ <- function(file) { dat2 <- read.delim(file,header=FALSE,stringsAsFactors=FALSE,nrows=1,skip=1); len = ncol(dat2) subjects = as.character(dat2[1,-c(1,len)]); classes <-c("character",rep("numeric",(len-2)),"character"); dat3 <- read.delim(file,header=TRUE,colClasses=classes,skip=1); taxa<- dat3[,len]; taxa<-as.matrix(taxa); matrix <- dat3[,-c(1,len)] colnames(matrix) = subjects; otus = dat3[,1]; rownames(matrix) = otus; obj <- list(counts=as.data.frame(matrix), taxa=as.data.frame(taxa),otus = as.data.frame(otus)) return(obj); } metagenomeSeq/R/loadPhenoData.R0000644000175200017520000000263714710220170017417 0ustar00biocbuildbiocbuild#' Load a clinical/phenotypic dataset associated with a study. #' #' Load a matrix of metadata associated with a study. #' #' #' @aliases loadPhenoData phenoData #' @param file Path and filename of the actual clinical file. #' @param tran Boolean. If the covariates are along the columns and samples #' along the rows, then tran should equal TRUE. #' @param sep The separator for the file. #' @return The metadata as a dataframe. #' @seealso \code{\link{loadMeta}} #' @examples #' #' dataDirectory <- system.file("extdata", package="metagenomeSeq") #' clin = loadPhenoData(file.path(dataDirectory,"CHK_clinical.csv"),tran=TRUE) #' loadPhenoData <-function(file,tran=TRUE,sep="\t") { dat2 <- read.table(file,header=FALSE,sep=sep); # no. of subjects subjects <- array(0,dim=c(ncol(dat2)-1)); for(i in 1:length(subjects)) { subjects[i] <- as.character(dat2[1,i+1]); } # no. of rows rows <- nrow(dat2); # load remaining counts matrix <- array(NA, dim=c(length(subjects),rows-1)); covar = array(NA,dim=c(rows-1,1)); for(i in 1:(rows)-1){ for(j in 1:(length(subjects))){ matrix[j,i] <- as.character(dat2[i+1,j+1]); } covar[i] = as.character(dat2[i+1,1]); } phenoData<-as.data.frame(matrix); colnames(phenoData) = covar; if(length(unique(subjects))==(length(subjects))){ rownames(phenoData) = subjects; } if(tran==TRUE){ phenoData = as.data.frame(t(phenoData)) } return(phenoData); } metagenomeSeq/R/mergeMRexperiments.R0000644000175200017520000000610314710220170020526 0ustar00biocbuildbiocbuild#' Extract the essentials of an MRexperiment. #' #' @param obj MRexperiment-class object. #' #' @return \itemize{A list containing: #' \item counts : Count data #' \item librarySize : The column sums / library size / sequencing depth #' \item normFactors : The normalization scaling factors #' \item pheno : phenotype table #' \item feat : feature table #' } #' #' @examples #' #' data(mouseData) #' head(metagenomeSeq:::extractMR(mouseData)) #' extractMR<-function(obj){ mat = MRcounts(obj) ls = as.vector(libSize(obj)) norm= as.vector(normFactors(obj)) pd = pData(obj) fd = fData(obj) dat = list(counts=mat,librarySize=ls,normFactors=norm,pheno=pd,feat=fd) return(dat) } #' Merge two tables #' #' @param x Table 1. #' @param y Table 2. #' #' @return Merged table #' mergeTable<-function(x,y){ rows = union(rownames(x),rownames(y)) cols = union(colnames(x),colnames(y)) fullmat = array(NA,dim=c(length(rows),length(cols))) rownames(fullmat) = rows colnames(fullmat) = cols fullmat[rownames(x),colnames(x)] = as.matrix(x) fullmat[rownames(y),colnames(y)] = as.matrix(y) fullmat } #' Merge two MRexperiment objects together #' #' This function will take two MRexperiment objects and merge them together finding common #' OTUs. If there are OTUs not found in one of the two MRexperiments then a message will #' announce this and values will be coerced to zero for the second table. #' #' @param x MRexperiment-class object 1. #' @param y MRexperiment-class object 2. #' #' @return Merged MRexperiment-class object. #' @export #' #' @examples #' data(mouseData) #' newobj = mergeMRexperiments(mouseData,mouseData) #' newobj #' #' # let me know if people are interested in an option to merge by keys instead of row names. #' data(lungData) #' newobj = mergeMRexperiments(mouseData,lungData) #' newobj mergeMRexperiments<-function(x,y){ xdat = extractMR(x) ydat = extractMR(y) xmat = xdat$counts; ymat = ydat$counts cnames = union(colnames(xmat),colnames(ymat)) if(length(cnames)!=(ncol(x)+ncol(y))){ message("MRexperiment 1 and 2 share sample ids; adding labels to sample ids.") newXnames = paste(colnames(xmat),"x",sep=".") newYnames = paste(colnames(ymat),"y",sep=".") cnames = union(newXnames,newYnames) colnames(xdat$counts) = rownames(xdat$pheno) = names(xdat$normFactors) = names(xdat$librarySize) = newXnames colnames(ydat$counts) = rownames(ydat$pheno) = names(ydat$normFactors) = names(ydat$librarySize) = newYnames } counts = mergeTable(xdat$counts,ydat$counts) pheno = as.data.frame(mergeTable(xdat$pheno,ydat$pheno)) feat = as.data.frame(mergeTable(xdat$feat,ydat$feat)) librarySize = c(xdat$librarySize,ydat$librarySize) normFactors = c(xdat$normFactors,ydat$normFactors) if(any(is.na(counts))){ message("There were OTUs not shared between objects. Coercing values to 0.") counts[is.na(counts)] = 0 } obj = newMRexperiment(counts=counts, normFactors=normFactors, libSize=librarySize, phenoData = AnnotatedDataFrame(pheno), featureData=AnnotatedDataFrame(feat)) return(obj) } metagenomeSeq/R/misc.R0000644000175200017520000000421514710220170015641 0ustar00biocbuildbiocbuild#' Table of features unique to a group #' #' Creates a table of features, their index, number of positive samples in a group, #' and the number of reads in a group. Can threshold features by a minimum no. of reads #' or no. of samples. #' #' @param obj Either a MRexperiment object or matrix. #' @param cl A vector representing assigning samples to a group. #' @param nsamples The minimum number of positive samples. #' @param nreads The minimum number of raw reads. #' @return Table of features unique to a group #' @examples #' data(mouseData) #' head(uniqueFeatures(mouseData[1:100,],cl=pData(mouseData)[,3])) #' uniqueFeatures<-function(obj,cl,nsamples=0,nreads=0){ if (class(obj) == "MRexperiment") { mat = MRcounts(obj, norm = FALSE, log = FALSE) } else if (class(obj) == "matrix") { mat = obj } else { stop("Object needs to be either a MRexperiment object or matrix") } res = by(t(mat),cl,colSums) res = do.call("rbind",res) kreads = (colSums(res==0)>0) mat = mat>0 resPos = by(t(mat),cl,colSums) resPos = do.call("rbind",resPos) ksamples = (colSums(resPos==0)>0) featureIndices = intersect(which(ksamples),which(kreads)) numberReads = t(res[,featureIndices]) colnames(numberReads) = paste("Reads in",colnames(numberReads)) numberPosSamples = t(resPos[,featureIndices]) colnames(numberPosSamples) = paste("Samp. in",colnames(numberPosSamples)) featureIndices = featureIndices featureNames = rownames(mat[featureIndices,]) df = cbind(featureIndices,numberPosSamples,numberReads) interesting = which(rowSums(numberReads)>=nreads & rowSums(numberPosSamples)>=nsamples) df[interesting,] } #' Function to make labels simpler #' #' Beginning to transition to better axes for plots #' #' @param x string for the x-axis #' @param y string for the y-axis #' @param norm is the data normalized? #' @param log is the data logged? #' @return vector of x,y labels #' @examples #' metagenomeSeq::makeLabels(norm=TRUE,log=TRUE) makeLabels<-function(x="samples",y="abundance",norm,log){ yl = xl = "" if(log == TRUE){ yl = paste(yl,"Log2") } if(norm == TRUE){ yl = paste(yl,"normalized") } yl = paste(yl,y) xl = paste(xl,x) return(c(xl,yl)) } metagenomeSeq/R/plotBubble.R0000644000175200017520000000771014710220170017003 0ustar00biocbuildbiocbuild#' Basic plot of binned vectors. #' #' This function plots takes two vectors, calculates the contingency table and #' plots circles sized by the contingency table value. Optional significance vectors #' of the values significant will shade the circles by proportion of significance. #' #' #' @param yvector A vector of values represented along y-axis. #' @param xvector A vector of values represented along x-axis. #' @param sigvector A vector of the names of significant features (names should match x/yvector). #' @param nbreaks Number of bins to break yvector and xvector into. #' @param ybreak The values to break the yvector at. #' @param xbreak The values to break the xvector at. #' @param scale Scaling of circle bin sizes. #' @param local Boolean to shade by signficant bin numbers (TRUE) or overall proportion (FALSE). #' @param ... Additional plot arguments. #' @return A matrix of features along rows, and the group membership along columns. #' @seealso \code{\link{plotMRheatmap}} #' @examples #' #' data(mouseData) #' mouseData = mouseData[which(rowSums(mouseData)>139),] #' sparsity = rowMeans(MRcounts(mouseData)==0) #' lor = log(fitPA(mouseData,cl=pData(mouseData)[,3])$oddsRatio) #' plotBubble(lor,sparsity,main="lor ~ sparsity") #' # Example 2 #' x = runif(100000) #' y = runif(100000) #' plotBubble(y,x) #' plotBubble<-function(yvector,xvector,sigvector=NULL,nbreaks=10, ybreak=quantile(yvector,p=seq(0,1,length.out=nbreaks)), xbreak=quantile(xvector,p=seq(0,1,length.out=nbreaks)),scale=1,local=FALSE,...){ ybreaks = cut(yvector,breaks=ybreak,include.lowest=TRUE) xbreaks = cut(xvector,breaks=xbreak,include.lowest=TRUE) contTable = lapply(levels(xbreaks),function(i){ k = which(xbreaks==i) sapply(levels(ybreaks),function(j){ length(which(ybreaks[k]==j)) }) }) names(contTable) = levels(xbreaks) yvec = 1:length(levels(ybreaks)) nc = length(yvec) if(!is.null(sigvector)){ # I am calculating contTable twice if sigvector==TRUE # This can be changed to if else statement to return two rows contSig = lapply(levels(xbreaks),function(i){ k = which(xbreaks==i) sapply(levels(ybreaks),function(j){ x = sum(names(yvector[k])[which(ybreaks[k]==j)]%in%sigvector)/length(which(ybreaks[k]==j)) if(is.na(x)) x = 0 x }) }) if(local==TRUE){ contSigTable = sapply(contSig,function(i){i}) linMap <- function(x, a, b) approxfun(range(x), c(a, b))(x) if(length(levels(ybreak))!=length(levels(xbreak))) { warning("Not square matrix - this is not implemented currently") } contSigTable = matrix(linMap(contSigTable,a=0,b=1),nrow=length(levels(ybreaks))) for(i in 1:length(levels(ybreaks))){ contSig[[i]] = contSigTable[,i] } } } else { contSig = lapply(levels(xbreaks),function(i){ k = which(xbreaks==i) sapply(levels(ybreaks),function(j){ 1 }) }) } medianSizes = median(unlist(contTable)) plot(y=yvec,x=rep(1,nc),cex=scale*contTable[[1]]/medianSizes, xlim=c(-0.25,nc+.25),ylim=c(-0.25,nc+.25),bty="n",xaxt="n",yaxt="n", xlab="",ylab="",pch=21,...,bg=rgb(blue=1,red=0,green=0,alpha=contSig[[1]])) for(i in 2:length(contTable)){ points(y=yvec,x=rep(i,nc),cex =scale*contTable[[i]]/medianSizes,pch=21,bg=rgb(blue=1,red=0,green=0,alpha=contSig[[i]])) } axis(1,at = 1:nc,labels=levels(xbreaks),las=2,cex.axis=.5) axis(2,at = 1:nc,labels=levels(ybreaks),las=2,cex.axis=.5) res = cbind(as.character(ybreaks),as.character(xbreaks)) colnames(res) = c("yvector","xvector") rownames(res) = names(yvector) if(is.null(sigvector)){ sig = rep(0,nrow(res)) sig[which(rownames(res)%in%sigvector)] = 1 res = cbind(res,sig) } invisible(res) } metagenomeSeq/R/plotCorr.R0000644000175200017520000000250414710220170016511 0ustar00biocbuildbiocbuild#' Basic correlation plot function for normalized or unnormalized counts. #' #' This function plots a heatmap of the "n" features with greatest variance #' across rows. #' #' #' @param obj A MRexperiment object with count data. #' @param n The number of features to plot. This chooses the "n" features with greatest variance. #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param fun Function to calculate pair-wise relationships. Default is pearson #' correlation #' @param ... Additional plot arguments. #' @return plotted correlation matrix #' @seealso \code{\link{cumNormMat}} #' @examples #' #' data(mouseData) #' plotCorr(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none",dendrogram="none", #' col = colorRampPalette(brewer.pal(9, "RdBu"))(50)) #' plotCorr <- function(obj,n,norm=TRUE,log=TRUE,fun=cor,...) { mat = returnAppropriateObj(obj,norm,log) otusToKeep <- which(rowSums(mat) > 0) otuVars = rowSds(mat[otusToKeep, ]) otuIndices = otusToKeep[order(otuVars, decreasing = TRUE)[1:n]] mat2 = mat[otuIndices, ] cc = as.matrix(fun(t(mat2))) hc = hclust(dist(mat2)) otuOrder = hc$order cc = cc[otuOrder, otuOrder] heatmap.2(t(cc),...) invisible(t(cc)) } metagenomeSeq/R/plotFeature.R0000644000175200017520000000463714710220170017210 0ustar00biocbuildbiocbuild#' Basic plot function of the raw or normalized data. #' #' This function plots the abundance of a particular OTU by class. The function #' is the typical manhattan plot of the abundances. #' #' #' @param obj A MRexperiment object with count data. #' @param otuIndex The row to plot #' @param classIndex A list of the samples in their respective groups. #' @param col A vector to color samples by. #' @param sort Boolean, sort or not. #' @param sortby Default is sort by library size, alternative vector for sorting #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param sl Scaling factor - if MRexperiment and norm=TRUE. #' @param ... Additional plot arguments. #' @return counts and classindex #' @seealso \code{\link{cumNorm}} #' @examples #' #' data(mouseData) #' classIndex=list(Western=which(pData(mouseData)$diet=="Western")) #' classIndex$BK=which(pData(mouseData)$diet=="BK") #' otuIndex = 8770 #' #' par(mfrow=c(2,1)) #' dates = pData(mouseData)$date #' plotFeature(mouseData,norm=FALSE,log=FALSE,otuIndex,classIndex, #' col=dates,sortby=dates,ylab="Raw reads") #' plotFeature<-function(obj,otuIndex,classIndex,col="black",sort=TRUE,sortby=NULL,norm=TRUE,log=TRUE,sl=1000,...){ mat = returnAppropriateObj(obj,norm,log,sl) fmat = mat[otuIndex,] ylmin = min(fmat) ylmax = max(fmat) nplots = length(classIndex) nms = names(classIndex) counts = lapply(classIndex,function(i){ fmat[i] }) if(sort==TRUE){ if(is.null(sortby)){ ord = lapply(classIndex,function(i){ order(colSums(mat[,i])) }) } else{ ord = lapply(classIndex,function(i){ order(sortby[i]) }) } } else { ord = lapply(classIndex,function(i){ 1:length(i) }) } if(length(col)>1){ col = as.integer(factor(col)) col4groups = lapply(1:length(classIndex),function(i){ cindex = classIndex[[i]] oindex = ord[[i]] col[cindex[oindex]] }) } for(i in 1:nplots){ vals = counts[[i]][ord[[i]]] if(exists("col4groups")) colors = col4groups[[i]] else colors = col plot(vals,xlab=nms[i],type="h",col=colors,ylim=c(ylmin,ylmax),...) } invisible(cbind(counts,ord)) } metagenomeSeq/R/plotGenus.R0000644000175200017520000000477514710220170016701 0ustar00biocbuildbiocbuild#' Basic plot function of the raw or normalized data. #' #' This function plots the abundance of a particular OTU by class. The function #' uses the estimated posterior probabilities to make technical zeros #' transparent. #' #' #' @aliases genusPlot plotGenus #' @param obj An MRexperiment object with count data. #' @param otuIndex A list of the otus with the same annotation. #' @param classIndex A list of the samples in their respective groups. #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param no Which of the otuIndex to plot. #' @param jitter.factor Factor value for jitter #' @param pch Standard pch value for the plot command. #' @param labs Whether to include group labels or not. (TRUE/FALSE) #' @param xlab xlabel for the plot. #' @param ylab ylabel for the plot. #' @param jitter Boolean to jitter the count data or not. #' @param ... Additional plot arguments. #' @return plotted data #' @seealso \code{\link{cumNorm}} #' @examples #' #' data(mouseData) #' classIndex=list(controls=which(pData(mouseData)$diet=="BK")) #' classIndex$cases=which(pData(mouseData)$diet=="Western") #' otuIndex = grep("Strep",fData(mouseData)$family) #' otuIndex=otuIndex[order(rowSums(MRcounts(mouseData)[otuIndex,]),decreasing=TRUE)] #' plotGenus(mouseData,otuIndex,classIndex,no=1:2,xaxt="n",norm=FALSE,ylab="Strep normalized log(cpt)") #' plotGenus <- function(obj,otuIndex,classIndex,norm=TRUE,log=TRUE,no=1:length(otuIndex),labs=TRUE,xlab=NULL,ylab=NULL,jitter=TRUE,jitter.factor=1,pch=21,...){ mat = returnAppropriateObj(obj,norm,log) l=lapply(otuIndex[no], function(i) lapply(classIndex, function(j) { mat[i,j] })) l=unlist(l,recursive=FALSE) if(!is.list(l)) stop("l must be a list\n") y=unlist(l) x=rep(seq(along=l),sapply(l,length)) z = posteriorProbs(obj) #if(!is.null(z)){ # z = 1-z; # lz=lapply(classIndex,function(j){(z[otuIndex[no],j])}) # z = unlist(lz) # blackCol=t(col2rgb("black")) # col=rgb(blackCol,alpha=z) #} else { blackCol=t(col2rgb("black")) col=rgb(blackCol) #} if(jitter) x=jitter(x,jitter.factor) if(is.null(ylab)){ylab="Normalized log(cpt)"} if(is.null(xlab)){xlab="Groups of comparison"} plot(x,y,col=col,pch=pch,xlab=xlab,ylab=ylab,xaxt="n",...) if(labs==TRUE){ gp = rep(names(classIndex),length(no)) axis(1,at=seq(1:length(gp)),gp) } invisible(list(x=x,y=y)) } metagenomeSeq/R/plotMRheatmap.R0000644000175200017520000000300514710220170017457 0ustar00biocbuildbiocbuild#' Basic heatmap plot function for normalized counts. #' #' This function plots a heatmap of the 'n' features with greatest variance #' across rows (or other statistic). #' #' #' @param obj A MRexperiment object with count data. #' @param n The number of features to plot. This chooses the 'n' features of greatest positive statistic. #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param fun Function to select top 'n' features. #' @param ... Additional plot arguments. #' @return plotted matrix #' @seealso \code{\link{cumNormMat}} #' @examples #' #' data(mouseData) #' trials = pData(mouseData)$diet #' heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))]; #' heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50) #' #### version using sd #' plotMRheatmap(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none", #' col = heatmapCols,ColSideColors = heatmapColColors) #' #### version using MAD #' plotMRheatmap(obj=mouseData,n=50,fun=mad,cexRow = 0.4,cexCol = 0.4,trace="none", #' col = heatmapCols,ColSideColors = heatmapColColors) #' plotMRheatmap <- function(obj,n,norm=TRUE,log=TRUE,fun=sd,...) { mat = returnAppropriateObj(obj,norm,log) otusToKeep = which(rowSums(mat)>0); otuStats = apply(mat[otusToKeep,],1,fun); otuIndices = otusToKeep[order(otuStats,decreasing=TRUE)[1:n]]; mat2=mat[otuIndices,]; heatmap.2(mat2,...); invisible(mat2) } metagenomeSeq/R/plotOTU.R0000644000175200017520000000425614710220170016261 0ustar00biocbuildbiocbuild#' Basic plot function of the raw or normalized data. #' #' This function plots the abundance of a particular OTU by class. The function #' uses the estimated posterior probabilities to make technical zeros #' transparent. #' #' #' @param obj A MRexperiment object with count data. #' @param otu The row number/OTU to plot. #' @param classIndex A list of the samples in their respective groups. #' @param log Whether or not to log2 transform the counts - if MRexperiment object. #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param jitter.factor Factor value for jitter. #' @param pch Standard pch value for the plot command. #' @param labs Whether to include group labels or not. (TRUE/FALSE) #' @param xlab xlabel for the plot. #' @param ylab ylabel for the plot. #' @param jitter Boolean to jitter the count data or not. #' @param ... Additional plot arguments. #' @return Plotted values #' @seealso \code{\link{cumNorm}} #' @examples #' #' data(mouseData) #' classIndex=list(controls=which(pData(mouseData)$diet=="BK")) #' classIndex$cases=which(pData(mouseData)$diet=="Western") #' # you can specify whether or not to normalize, and to what level #' plotOTU(mouseData,otu=9083,classIndex,norm=FALSE,main="9083 feature abundances") #' plotOTU <- function(obj,otu,classIndex,log=TRUE,norm=TRUE,jitter.factor=1,pch=21,labs=TRUE,xlab=NULL,ylab=NULL,jitter=TRUE,...){ mat = returnAppropriateObj(obj,norm,log) l=lapply(classIndex, function(j){ mat[otu,j] }) z = posteriorProbs(obj) y=unlist(l) x=rep(seq(along=l),sapply(l,length)) if(!is.null(z)){ z = 1-z; lz=lapply(classIndex,function(j){(z[otu,j])}) z = unlist(lz) blackCol=t(col2rgb("black")) col=rgb(blackCol,alpha=z) } else { blackCol=t(col2rgb("black")) col=rgb(blackCol) } if(jitter) x=jitter(x,jitter.factor) if(is.null(ylab)){ylab="Normalized log(cpt)"} if(is.null(xlab)){xlab="Groups of comparison"} plot(x,y,col=col,pch=pch,bg=col,xlab=xlab,ylab=ylab,xaxt="n",...) if(labs==TRUE){ gp = names(classIndex) axis(1,at=seq(1:length(gp)),gp) } invisible(list(x=x,y=y)) } metagenomeSeq/R/plotOrd.R0000644000175200017520000000452614710220170016336 0ustar00biocbuildbiocbuild#' Plot of either PCA or MDS coordinates for the distances of normalized or unnormalized counts. #' #' This function plots the PCA / MDS coordinates for the "n" features of interest. Potentially uncovering batch #' effects or feature relationships. #' #' #' @param obj A MRexperiment object or count matrix. #' @param tran Transpose the matrix. #' @param comp Which components to display #' @param usePCA TRUE/FALSE whether to use PCA or MDS coordinates (TRUE is PCA). #' @param useDist TRUE/FALSE whether to calculate distances. #' @param distfun Distance function, default is stats::dist #' @param dist.method If useDist==TRUE, what method to calculate distances. #' @param norm Whether or not to normalize the counts - if MRexperiment object. #' @param log Whether or not to log2 the counts - if MRexperiment object. #' @param n Number of features to make use of in calculating your distances. #' @param ... Additional plot arguments. #' @return coordinates #' @seealso \code{\link{cumNormMat}} #' @examples #' #' data(mouseData) #' cl = pData(mouseData)[,3] #' plotOrd(mouseData,tran=TRUE,useDist=TRUE,pch=21,bg=factor(cl),usePCA=FALSE) #' plotOrd<-function(obj,tran=TRUE,comp=1:2,norm=TRUE,log=TRUE,usePCA=TRUE,useDist=FALSE,distfun=stats::dist,dist.method="euclidian",n=NULL,...){ mat = returnAppropriateObj(obj,norm,log) if(useDist==FALSE & usePCA==FALSE) stop("Classical MDS requires distances") if(is.null(n)) n = min(nrow(mat),1000) if(length(comp)>2) stop("Can't display more than two components") otusToKeep <- which(rowSums(mat)>0) otuVars<-rowSds(mat[otusToKeep,]) otuIndices<-otusToKeep[order(otuVars,decreasing=TRUE)[seq_len(n)]] mat <- mat[otuIndices,] if(tran==TRUE){ mat = t(mat) } if(useDist==TRUE){ d <- distfun(mat,method=dist.method) } else{ d = mat } if(usePCA==FALSE){ ord = cmdscale(d,k = max(comp)) xl = paste("MDS component:",comp[1]) yl = paste("MDS component:",comp[2]) } else{ pcaRes <- prcomp(d) ord <- pcaRes$x vars <- pcaRes$sdev^2 vars <- round(vars/sum(vars),5)*100 xl <- sprintf("PCA %s: %.2f%% variance",colnames(ord)[comp[1]], vars[comp[1]]) yl <- sprintf("PCA %s: %.2f%% variance",colnames(ord)[comp[2]], vars[comp[2]]) } plot(ord[,comp],ylab=yl,xlab=xl,...) invisible(ord[,comp]) } metagenomeSeq/R/plotRare.R0000644000175200017520000000305614710220170016500 0ustar00biocbuildbiocbuild#' Plot of rarefaction effect #' #' This function plots the number of observed features vs. the depth of coverage. #' #' @param obj A MRexperiment object with count data or matrix. #' @param cl Vector of classes for various samples. #' @param ... Additional plot arguments. #' @return Library size and number of detected features #' @seealso \code{\link{plotOrd}}, \code{\link{plotMRheatmap}}, \code{\link{plotCorr}}, \code{\link{plotOTU}}, \code{\link{plotGenus}} #' @examples #' #' data(mouseData) #' cl = factor(pData(mouseData)[,3]) #' res = plotRare(mouseData,cl=cl,pch=21,bg=cl) #' tmp=lapply(levels(cl), function(lv) lm(res[,"ident"]~res[,"libSize"]-1, subset=cl==lv)) #' for(i in 1:length(levels(cl))){ #' abline(tmp[[i]], col=i) #' } #' legend("topleft", c("Diet 1","Diet 2"), text.col=c(1,2),box.col=NA) #' plotRare<-function(obj,cl=NULL,...){ if(class(obj)=="MRexperiment"){ mat = MRcounts(obj,norm=FALSE,log=FALSE) totalCounts = libSize(obj) } else if(class(obj) == "matrix") { mat = obj totalCounts=colSums(mat) } else { stop("Object needs to be either a MRexperiment object or matrix") } numFeatures=colSums(mat!=0) if(is.null(cl)){ plot(totalCounts, numFeatures, xlab = "Depth of coverage", ylab = "Number of detected features",...) } else{ plot(totalCounts, numFeatures, xlab = "Depth of coverage", ylab = "Number of detected features",col=factor(cl),...) } dat = cbind(totalCounts,numFeatures); colnames(dat) = c("libSize","ident") invisible(dat) } metagenomeSeq/R/wrenchNorm.R0000644000175200017520000000156514710220170017035 0ustar00biocbuildbiocbuild#' Computes normalization factors using wrench instead of cumNorm #' #' Calculates normalization factors using method published by #' M. Sentil Kumar et al. (2018) to compute normalization factors which #' considers compositional bias introduced by sequencers. #' #' @param obj an MRexperiment object #' @param condition case control label that wrench uses to calculate #' normalization factors #' @return an MRexperiment object with updated normalization factors. #' Accessible by \code{\link{normFactors}}. #' @seealso \code{\link{cumNorm}} \code{\link{fitZig}} #' #' @examples #' #' data(mouseData) #' mouseData <- wrenchNorm(mouseData, condition = mouseData$diet) #' head(normFactors(mouseData)) #' wrenchNorm <- function(obj, condition) { count_data <- MRcounts(obj, norm = FALSE) W <- wrench(count_data, condition = condition) normFactors(obj) <- W$nf return(obj) }metagenomeSeq/R/zigControl.R0000644000175200017520000000240114710220170017033 0ustar00biocbuildbiocbuild#' Settings for the fitZig function #' #' @param tol The tolerance for the difference in negative log likelihood estimates for a feature to remain active. #' @param maxit The maximum number of iterations for the expectation-maximization algorithm. #' @param verbose Whether to display iterative step summary statistics or not. #' @param dfMethod Either 'default' or 'modified' (by responsibilities). #' @param pvalMethod Either 'default' or 'bootstrap'. #' @return The value for the tolerance, maximum no. of iterations, and the verbose warning. #' @note \code{\link{fitZig}} makes use of zigControl. #' #' @name zigControl #' @aliases settings2 #' @seealso \code{\link{fitZig}} \code{\link{cumNorm}} \code{\link{plotOTU}} #' @examples #' control = zigControl(tol=1e-10,maxit=10,verbose=FALSE) #' zigControl <-function(tol=1e-4,maxit=10,verbose=TRUE,dfMethod="modified",pvalMethod="default"){ # to do: add stop if not DFMETHODS <- c("default", "modified") PMETHODS <- c("default", "bootstrap") dfMethod <- DFMETHODS[pmatch(dfMethod, DFMETHODS)] pvalMethod<- PMETHODS[pmatch(pvalMethod,PMETHODS)] stopifnot(dfMethod%in%DFMETHODS) stopifnot(pvalMethod%in%PMETHODS) set <-list(tol=tol,maxit=maxit,verbose=verbose,dfMethod=dfMethod,pvalMethod=pvalMethod); return(set) } metagenomeSeq/README.md0000644000175200017520000000466714710220170015654 0ustar00biocbuildbiocbuildmetagenomeSeq ============= Statistical analysis for sparse high-throughput sequencing [![Travis-CI Build Status](https://travis-ci.org/HCBravoLab/metagenomeSeq.svg?branch=master)](https://travis-ci.org/HCBravoLab/metagenomeSeq) metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and undersampling of microbial communities on disease association detection and the testing of feature correlations. To install the latest release version of metagenomeSeq: ```S if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("metagenomeSeq") ``` To install the latest development version of metagenomeSeq: ```S install.packages("devtools") library("devtools") install_github("Bioconductor-mirror/metagenomeSeq") ``` Author: [Joseph Nathaniel Paulson](http://bcb.dfci.harvard.edu/~jpaulson), Hisham Talukder, [Mihai Pop](http://www.cbcb.umd.edu/~mpop), [Hector Corrada Bravo](http://www.cbcb.umd.edu/~hcorrada) Maintainer: Joseph N. 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Paulson"), as.person("Oscar Colin Stine"), as.person("Hector Corrada Bravo"), as.person("Mihai Pop")), year = 2013, journal = "Nat Meth", volume = "advance online publication", pages = "", doi = "10.1038/nmeth.2658", url = "http://www.nature.com/nmeth/journal/vaop/ncurrent/abs/nmeth.2658.html", header = "To cite the original statistical method and normalization method implemented in metagenomeSeq use") bibentry(bibtype="Manual", title = "metagenomeSeq: Statistical analysis for sparse high-throughput sequncing.", author = personList( as.person("Joseph N. Paulson"), as.person("Nathan D. Olson"), as.person("Domenick J. Braccia"), as.person("Justin Wagner"), as.person("Hisham Talukder"), as.person("Mihai Pop"), as.person("Hector Corrada Bravo")), year = 2013, note = "Bioconductor package", url = "http://www.cbcb.umd.edu/software/metagenomeSeq", header = "To cite the metagenomeSeq software/vignette guide use" ) bibentry(bibtype="Article", title = "Longitudinal differential abundance analysis of marker-gene surveys using smoothing splines", author = personList( as.person("Joseph N. Paulson*"), as.person("Hisham Talukder*"), as.person("Hector Corrada Bravo")), year = "2017", journal = "biorxiv", doi = "10.1101/099457", url = "https://www.biorxiv.org/content/10.1101/099457v1", header = "To cite time series analysis/function fitTimeSeries use") metagenomeSeq/inst/Dockerfile0000644000175200017520000000161714710220170017334 0ustar00biocbuildbiocbuild# Docker image to check metagenomeSeq with Bioc devel FROM bioconductor/bioconductor_docker:devel # Install all the latex stuff to build vignettes RUN apt-get update \ && apt-get install -y --no-install-recommends apt-utils \ && apt-get install -y --no-install-recommends \ texlive \ texlive-latex-extra \ texlive-fonts-extra \ texlive-bibtex-extra \ texlive-science \ texi2html \ texinfo \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* ## Install BiocStyle RUN R -e 'BiocManager::install("BiocStyle")' # Install metagenomeSeq RUN R -e 'BiocManager::install("metagenomeSeq")' # Now dependencies to build vignettes RUN R -e 'BiocManager::install(c("biomformat", "gss"))' # build with command: docker build -t biocondcutor_docker_metagenomeseq:devel inst # run rstudio with command: docker run -e PASSWORD= -p 8787:8787 -v $PWD:/metagenomeSeq bioconductor_docker_metagenomeseq:devel metagenomeSeq/inst/doc/0000755000175200017520000000000014735615226016123 5ustar00biocbuildbiocbuildmetagenomeSeq/inst/doc/fitTimeSeries.R0000644000175200017520000000737514735615163021036 0ustar00biocbuildbiocbuild## ----include=FALSE------------------------------------------------------------ require(knitr) opts_chunk$set(concordance=TRUE,tidy=TRUE) ## ----config,echo=FALSE----------------------------------------- options(width = 65) options(continue=" ") options(warn=-1) set.seed(42) ## ----requireMetagenomeSeq,warning=FALSE,message=FALSE---------- library(metagenomeSeq) library(gss) ## ----dataset2,tidy=FALSE--------------------------------------- data(mouseData) mouseData ## ----createMRexperiment1--------------------------------------- # Creating mock sample replicates sampleID = rep(paste("sample",1:10,sep=":"),times=20) # Creating mock class membership class = rep(c(rep(0,5),rep(1,5)),times=20) # Creating mock time time = rep(1:20,each=10) phenotypeData = AnnotatedDataFrame(data.frame(sampleID,class,time)) # Creating mock abundances set.seed(1) # No difference measurement1 = rnorm(200,mean=100,sd=1) # Some difference measurement2 = rnorm(200,mean=100,sd=1) measurement2[1:5]=measurement2[1:5] + 100 measurement2[11:15]=measurement2[11:15] + 100 measurement2[21:25]=measurement2[21:25] + 50 mat = rbind(measurement1,measurement2) colnames(mat) = 1:200 mat[1:2,1:10] ## ----createMRexperiment2--------------------------------------- # This is an example of potential lvl's to aggregate by. data(mouseData) colnames(fData(mouseData)) ## ----createMRexperiment3,tidy=FALSE---------------------------- obj = newMRexperiment(counts=mat,phenoData=phenotypeData) obj res1 = fitTimeSeries(obj,feature=1, class='class',time='time',id='sampleID', B=10,norm=FALSE,log=FALSE) res2 = fitTimeSeries(obj,feature=2, class='class',time='time',id='sampleID', B=10,norm=FALSE,log=FALSE) classInfo = factor(res1$data$class) ## ----plotMRexperiment3,tidy=FALSE------------------------------ par(mfrow=c(3,1)) plotClassTimeSeries(res1,pch=21,bg=classInfo) plotTimeSeries(res2) plotClassTimeSeries(res2,pch=21,bg=classInfo) ## ----timeSeries------------------------------------------------ res = fitTimeSeries(obj=mouseData,lvl="class",feature="Actinobacteria",class="status",id="mouseID",time="relativeTime",B=10) # We observe a time period of differential abundance for "Actinobacteria" res$timeIntervals str(res) ## ----timeSeriesAllClasses, tidy=FALSE-------------------------- set.seed(123) classes = unique(fData(mouseData)[,"class"]) timeSeriesFits = lapply(classes,function(i){ fitTimeSeries(obj=mouseData, feature=i, class="status", id="mouseID", time="relativeTime", lvl='class', C=.3,# a cutoff for 'interesting' B=1) # B is the number of permutations and should clearly not be 1 }) names(timeSeriesFits) = classes # Removing classes of bacteria without a potentially # interesting time interval difference. timeSeriesFits = lapply(timeSeriesFits,function(i){i[[1]]})[-grep("No",timeSeriesFits)] # Naming the various interesting time intervals. for(i in 1:length(timeSeriesFits)){ rownames(timeSeriesFits[[i]]) = paste( paste(names(timeSeriesFits)[i]," interval",sep=""), 1:nrow(timeSeriesFits[[i]]),sep=":" ) } # Merging into a table. timeSeriesFits = do.call(rbind,timeSeriesFits) # Correcting for multiple testing. pvalues = timeSeriesFits[,"p.value"] adjPvalues = p.adjust(pvalues,"bonferroni") timeSeriesFits = cbind(timeSeriesFits,adjPvalues) head(timeSeriesFits) ## ----timeSeriesPlotting---------------------------------------- par(mfrow=c(2,1)) plotClassTimeSeries(res,pch=21, bg=res$data$class,ylim=c(0,8)) plotTimeSeries(res) ## ----cite------------------------------------------------------ citation("metagenomeSeq") ## ----sessionInfo----------------------------------------------- sessionInfo() metagenomeSeq/inst/doc/fitTimeSeries.Rnw0000644000175200017520000003412414710220170021352 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{fitTimeSeries: differential abundance analysis through time or location} %\VignetteEngine{knitr::knitr} \documentclass[a4paper,11pt]{article} \usepackage{url} \usepackage{afterpage} \usepackage{hyperref} \usepackage{geometry} \usepackage{cite} \geometry{hmargin=2.5cm, vmargin=2.5cm} \usepackage{graphicx} \usepackage{courier} \bibliographystyle{unsrt} \begin{document} <>= require(knitr) opts_chunk$set(concordance=TRUE,tidy=TRUE) @ \title{{\textbf{\texttt{fitTimeSeries}: Longitudinal differential abundance analysis for marker-gene surveys}}} \author{Hisham Talukder, Joseph N. Paulson, Hector Corrada Bravo\\[1em]\\ Applied Mathematics $\&$ Statistics, and Scientific Computation\\ Center for Bioinformatics and Computational Biology\\ University of Maryland, College Park\\[1em]\\ \texttt{jpaulson@umiacs.umd.edu}} \date{Modified: February 18, 2015. Compiled: \today} \maketitle \tableofcontents \newpage <>= options(width = 65) options(continue=" ") options(warn=-1) set.seed(42) @ \section{Introduction} \textbf{This is a vignette specifically for the fitTimeSeries function. For a full list of functions available in the package: help(package=metagenomeSeq). For more information about a particular function call: ?function.} Smoothing spline regression models~\cite{Wahba:1990} are commonly used to model longitudinal data and form the basis for methods used in a large number of applications ~\cite{networkped1,LongCrisp}. Specifically, an extension of the methodology called Smoothing-Spline ANOVA~\cite{Gu} is capable of directly estimating a smooth function of interest while incorporating other covariates in the model. A common approach to detect regions/times of interest in a genome or for differential abundance is to model differences between two groups with respect to the quantitative measurements as smooth functions and perform statistical inference on these models. In particular, widely used methods for region finding using DNA methylation data use local regression methods to estimate these smooth functions. An important aspect of these tools is their ability to incorporate sample characteristics as covariates in these models, e.g., sex and age in population studies, or technical factors like processing batches. Incorporating these sources of variability, both biological and technical is essential in high-throughput studies. Therefore, these methods require that the models used are capable of estimating both smooth functions and sample-specfic characteristics. We present fitTimeSeries - a method for estimating and detecting regions/times of interest due to differential abundance of a quantitative measurement (for example, normalized abundance). \subsection{Problem Formulation} We model data in the following form: $$ Y_{itk}= f_i(t,x_{k})+e_{tk} $$ where i represents group factor (diet, health status, etc.), $t$ represents series factor (for example, time or location), $k$ represents replicate observations, $x_{k}$ are covariates for sample $k$ (including an indicator for group membership $I\{k \in i\}$) and $e_{tk}$ are independent $N(0,\sigma^2)$ errors. We assume $f_i$ to be a smooth function, defined in an interval $[a,b]$, that can be parametric, non-parametric or a mixture of both. Our goal is to identify intervals where the absolute difference between two groups $\eta_d(t)=f_1(t, \cdot)-f_2(t, \cdot)$ is large, that is, regions, $R_{t_1,t_2}$, where: $R_{t_1,t_2}= \{t_1,t_2 \in x \textit{ such that } | \eta_{d}(x) | \ge C \}$ and $C$ is a predefined constant threshold. To identify these areas we use hypothesis testing using the area $A_{t_1,t_2}=\int_{R_{t_1,t_2}}\eta_d(t) dt$ under the estimated function of $\eta_d(t)$ as a statistic with null and alternative hypotheses $$ H_0: A_{t_1,t_2} \le K $$ $$ H_1: A_{t_1,t_2} > K $$ with $K$ some fixed threshold. We employ a permutation-based method to calculate a null distribution of the area statistics $A_(t1,t2)$'s. To do this, the group-membership indicator variables (0-1 binary variable) are randomly permuted $B$ times, e.g., $B=1000$ and the method above is used to estimate the difference function $\eta_d^b$ (in this case simulating the null hypothesis) and an area statistics $A_(t1,t2)^b$ for each random permutation. Estimates $A_(t1,t2)^b$ are then used to construct an empirical estimate of $A_(t1,t2)$ under the null hypothesis. The observed area, $A_(t1,t2)^*$, is compared to the empirical null distribution to calculate a p-value. Figure 1 illustrates the relationship between $R_(t1,t2)$ and $A_(t1,t2)$. The key is to estimate regions $R_(t1,t2)$ where point-wise confidence intervals would be appropriate. \section{Data preparation} Data should be preprocessed and prepared in tab-delimited files. Measurements are stored in a matrix with samples along the columns and features along the rows. For example, given $m$ features and $n$ samples, the entries in a marker-gene or metagenomic count matrix \textbf{C} ($m, n$), $c_{ij}$, are the number of reads annotated for a particular feature $i$ (whether it be OTU, species, genus, etc.) in sample $j$. Alternatively, the measurements could be some quantitative measurement such as methylation percentages or CD4 levels.\\ \begin{center} $\bordermatrix{ &sample_1&sample_2&\ldots &sample_n\cr feature_1&c_{11} & c_{12} & \ldots & c_{1n}\cr feature_2& c_{21} & c_{22} & \ldots & c_{2n}\cr \vdots & \vdots & \vdots & \ddots & \vdots\cr feature_m & c_{m1} & c_{m2} &\ldots & c_{mn}}$ \end{center} Data should be stored in a file (tab-delimited by default) with sample names along the first row, feature names in the first column and should be loaded into R and formatted into a MRexperiment object. To prepare the data please read the section on data preparation in the full metagenomeSeq vignette - \texttt{vignette("metagenomeSeq")}. \subsection{Example datasets} There is a time-series dataset included as an examples in the \texttt{metagenomeSeq} package. Data needs to be in a \texttt{MRexperiment} object format to normalize, run the statistical tests, and visualize. As an example, throughout the vignette we'll use the following datasets. To understand a \texttt{fitTimeSeries}'s usage or included data simply enter ?\texttt{fitTimeSeries}. <>= library(metagenomeSeq) library(gss) @ \begin{enumerate} \setcounter{enumi}{1} \item Humanized gnotobiotic mouse gut \cite{ts_mouse}: Twelve germ-free adult male C57BL/6J mice were fed a low-fat, plant polysaccharide-rich diet. Each mouse was gavaged with healthy adult human fecal material. Following the fecal transplant, mice remained on the low-fat, plant polysacchaaride-rich diet for four weeks, following which a subset of 6 were switched to a high-fat and high-sugar diet for eight weeks. Fecal samples for each mouse went through PCR amplification of the bacterial 16S rRNA gene V2 region weekly. Details of experimental protocols and further details of the data can be found in Turnbaugh et. al. Sequences and further information can be found at: \url{http://gordonlab.wustl.edu/TurnbaughSE_10_09/STM_2009.html} \end{enumerate} <>= data(mouseData) mouseData @ \subsection{Creating a \texttt{MRexperiment} object with other measurements} For a fitTimeSeries analysis a minimal MRexperiment-object is required and can be created using the function \texttt{newMRexperiment} which takes a count matrix described above and phenoData (annotated data frame). \texttt{Biobase} provides functions to create annotated data frames. <>= # Creating mock sample replicates sampleID = rep(paste("sample",1:10,sep=":"),times=20) # Creating mock class membership class = rep(c(rep(0,5),rep(1,5)),times=20) # Creating mock time time = rep(1:20,each=10) phenotypeData = AnnotatedDataFrame(data.frame(sampleID,class,time)) # Creating mock abundances set.seed(1) # No difference measurement1 = rnorm(200,mean=100,sd=1) # Some difference measurement2 = rnorm(200,mean=100,sd=1) measurement2[1:5]=measurement2[1:5] + 100 measurement2[11:15]=measurement2[11:15] + 100 measurement2[21:25]=measurement2[21:25] + 50 mat = rbind(measurement1,measurement2) colnames(mat) = 1:200 mat[1:2,1:10] @ If phylogenetic information exists for the features and there is a desire to aggregate measurements based on similar annotations choosing the featureData column name in lvl will aggregate measurements using the default parameters in the \texttt{aggregateByTaxonomy} function. <>= # This is an example of potential lvl's to aggregate by. data(mouseData) colnames(fData(mouseData)) @ Here we create the actual MRexperiment to run through fitTimeSeries. <>= obj = newMRexperiment(counts=mat,phenoData=phenotypeData) obj res1 = fitTimeSeries(obj,feature=1, class='class',time='time',id='sampleID', B=10,norm=FALSE,log=FALSE) res2 = fitTimeSeries(obj,feature=2, class='class',time='time',id='sampleID', B=10,norm=FALSE,log=FALSE) classInfo = factor(res1$data$class) @ <>= par(mfrow=c(3,1)) plotClassTimeSeries(res1,pch=21,bg=classInfo) plotTimeSeries(res2) plotClassTimeSeries(res2,pch=21,bg=classInfo) @ \section{Time series analysis} Implemented in the \texttt{fitTimeSeries} function is a method for calculating time intervals for which bacteria are differentially abundant. Fitting is performed using Smoothing Splines ANOVA (SS-ANOVA), as implemented in the \texttt{gss} package. Given observations at multiple time points for two groups the method calculates a function modeling the difference in abundance across all time. Using group membership permutations we estimate a null distribution of areas under the difference curve for the time intervals of interest and report significant intervals of time. Here we provide a real example from the microbiome of two groups of mice on different diets. The gnotobiotic mice come from a longitudinal study ideal for this type of analysis. We choose to perform our analysis at the class level and look for differentially abundant time intervals for "Actinobacteria". For demonstrations sake we perform only 10 permutations. If you find the method useful, please cite: "Longitudinal differential abundance analysis for marker-gene surveys" Talukder H*, Paulson JN*, Bravo HC. (Submitted) <>= res = fitTimeSeries(obj=mouseData,lvl="class",feature="Actinobacteria",class="status",id="mouseID",time="relativeTime",B=10) # We observe a time period of differential abundance for "Actinobacteria" res$timeIntervals str(res) @ For example, to test every class in the mouse dataset: <>= set.seed(123) classes = unique(fData(mouseData)[,"class"]) timeSeriesFits = lapply(classes,function(i){ fitTimeSeries(obj=mouseData, feature=i, class="status", id="mouseID", time="relativeTime", lvl='class', C=.3,# a cutoff for 'interesting' B=1) # B is the number of permutations and should clearly not be 1 }) names(timeSeriesFits) = classes # Removing classes of bacteria without a potentially # interesting time interval difference. timeSeriesFits = lapply(timeSeriesFits,function(i){i[[1]]})[-grep("No",timeSeriesFits)] # Naming the various interesting time intervals. for(i in 1:length(timeSeriesFits)){ rownames(timeSeriesFits[[i]]) = paste( paste(names(timeSeriesFits)[i]," interval",sep=""), 1:nrow(timeSeriesFits[[i]]),sep=":" ) } # Merging into a table. timeSeriesFits = do.call(rbind,timeSeriesFits) # Correcting for multiple testing. pvalues = timeSeriesFits[,"p.value"] adjPvalues = p.adjust(pvalues,"bonferroni") timeSeriesFits = cbind(timeSeriesFits,adjPvalues) head(timeSeriesFits) @ Please see the help page for \texttt{fitTimeSeries} for parameters. Note, only two groups can be compared to each other and the time parameter must be an actual value (currently no support for posix, etc.). \subsection{Paramaters} There are a number of parameters for the \texttt{fitTimeSeries} function. We list and provide a brief discussion below. For parameters influencing \texttt{ssanova}, \texttt{aggregateByTaxonomy}, \texttt{MRcounts} type ?function for more details. \begin{itemize} \item obj - the metagenomeSeq MRexperiment-class object. \item feature - Name or row of feature of interest. \item class - Name of column in phenoData of MRexperiment-class object for class memberhip. \item time - Name of column in phenoData of MRexperiment-class object for relative time. \item id - Name of column in phenoData of MRexperiment-class object for sample id. \item method - Method to estimate time intervals of differentially abundant bacteria (only ssanova method implemented currently). \item lvl - Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level). \item C - Value for which difference function has to be larger or smaller than (default 0). \item B - Number of permutations to perform (default 1000) \item norm - When aggregating counts to normalize or not. (see MRcounts) \item log - Log2 transform. (see MRcounts) \item sl - Scaling value. (see MRcounts) \item ... - Options for ssanova \end{itemize} \section{Visualization of features} To help with visualization and analysis of datasets \texttt{metagenomeSeq} has several plotting functions to gain insight of the model fits and the differentially abundant time intervals using \texttt{plotClassTimeSeries} and \texttt{plotTimeSeries} on the result. More plots will be updated. <>= par(mfrow=c(2,1)) plotClassTimeSeries(res,pch=21, bg=res$data$class,ylim=c(0,8)) plotTimeSeries(res) @ \section{Summary} \texttt{metagenomeSeq}'s \texttt{fitTimeSeries} is a novel methodology for differential abundance testing of longitudinal data. If you make use of the statistical method please cite our paper. 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----include=FALSE--------------------------------------------- require(knitr) opts_chunk$set(concordance=TRUE,tidy=TRUE) ## ----config,echo=FALSE------------------------------------ options(width = 60) options(continue=" ") options(warn=-1) set.seed(42) ## ----requireMetagenomeSeq,warning=FALSE,message=FALSE----- library(metagenomeSeq) ## ----loadBiom--------------------------------------------- # reading in a biom file library(biomformat) biom_file <- system.file("extdata", "min_sparse_otu_table.biom", package = "biomformat") b <- read_biom(biom_file) biom2MRexperiment(b) ## ----writeBiom,eval=FALSE--------------------------------- # data(mouseData) # # options include to normalize or not # b <- MRexperiment2biom(mouseData) # write_biom(b,biom_file="~/Desktop/otu_table.biom") ## ----loadData--------------------------------------------- dataDirectory <- system.file("extdata", package="metagenomeSeq") lung = loadMeta(file.path(dataDirectory,"CHK_NAME.otus.count.csv")) dim(lung$counts) ## ----loadTaxa--------------------------------------------- taxa = read.delim(file.path(dataDirectory,"CHK_otus.taxonomy.csv"),stringsAsFactors=FALSE) ## ----loadClin--------------------------------------------- clin = loadPhenoData(file.path(dataDirectory,"CHK_clinical.csv"),tran=TRUE) ord = match(colnames(lung$counts),rownames(clin)) clin = clin[ord,] head(clin[1:2,]) ## ----createMRexperiment1---------------------------------- phenotypeData = AnnotatedDataFrame(clin) phenotypeData ## ----createMRexperiment2---------------------------------- OTUdata = AnnotatedDataFrame(taxa) OTUdata ## ----createMRexperiment3,tidy=FALSE----------------------- obj = newMRexperiment(lung$counts,phenoData=phenotypeData,featureData=OTUdata) # Links to a paper providing further details can be included optionally. # experimentData(obj) = annotate::pmid2MIAME("21680950") obj ## ----dataset1,tidy=FALSE---------------------------------- data(lungData) lungData ## ----dataset2,tidy=FALSE---------------------------------- data(mouseData) mouseData ## ----pdata------------------------------------------------ phenoData(obj) head(pData(obj),3) ## ----fdata------------------------------------------------ featureData(obj) head(fData(obj)[,-c(2,10)],3) ## ----MRcounts--------------------------------------------- head(MRcounts(obj[,1:2])) ## --------------------------------------------------------- featuresToKeep = which(rowSums(obj)>=100) samplesToKeep = which(pData(obj)$SmokingStatus=="Smoker") obj_smokers = obj[featuresToKeep,samplesToKeep] obj_smokers head(pData(obj_smokers),3) ## ----normFactors------------------------------------------ head(normFactors(obj)) normFactors(obj) <- rnorm(ncol(obj)) head(normFactors(obj)) ## ----libSize---------------------------------------------- head(libSize(obj)) libSize(obj) <- rnorm(ncol(obj)) head(libSize(obj)) ## ----filterData------------------------------------------- data(mouseData) filterData(mouseData,present=10,depth=1000) ## ----mergeMRexperiment------------------------------------ data(mouseData) newobj = mergeMRexperiments(mouseData,mouseData) newobj ## ----calculateNormFactors--------------------------------- data(lungData) p=cumNormStatFast(lungData) ## ----normalizeData---------------------------------------- lungData = cumNorm(lungData,p=p) ## ----wrenchNorm------------------------------------------- condition = mouseData$diet mouseData = wrenchNorm(mouseData,condition=condition) ## ----saveData--------------------------------------------- mat = MRcounts(lungData,norm=TRUE,log=TRUE)[1:5,1:5] exportMat(mat,file=file.path(dataDirectory,"tmp.tsv")) ## ----exportStats------------------------------------------ exportStats(lungData[,1:5],file=file.path(dataDirectory,"tmp.tsv")) head(read.csv(file=file.path(dataDirectory,"tmp.tsv"),sep="\t")) ## ----removeData, echo=FALSE------------------------------- system(paste("rm",file.path(dataDirectory,"tmp.tsv"))) ## ----fitFeatureModel-------------------------------------- data(lungData) lungData = lungData[,-which(is.na(pData(lungData)$SmokingStatus))] lungData=filterData(lungData,present=30,depth=1) lungData <- cumNorm(lungData, p=.5) pd <- pData(lungData) mod <- model.matrix(~1+SmokingStatus, data=pd) lungres1 = fitFeatureModel(lungData,mod) head(MRcoefs(lungres1)) ## ----preprocess,dev='pdf',out.width='.55\\linewidth',out.height='.55\\linewidth',fig.cap='Relative difference for the median difference in counts from the reference.',fig.align='center',warning=FALSE---- data(lungData) controls = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-controls] rareFeatures = which(rowSums(MRcounts(lungTrim)>0)<10) lungTrim = lungTrim[-rareFeatures,] lungp = cumNormStat(lungTrim,pFlag=TRUE,main="Trimmed lung data") lungTrim = cumNorm(lungTrim,p=lungp) ## ----zigTesting------------------------------------------- smokingStatus = pData(lungTrim)$SmokingStatus bodySite = pData(lungTrim)$SampleType normFactor = normFactors(lungTrim) normFactor = log2(normFactor/median(normFactor) + 1) mod = model.matrix(~smokingStatus+bodySite + normFactor) settings = zigControl(maxit=10,verbose=TRUE) fit = fitZig(obj = lungTrim,mod=mod,useCSSoffset = FALSE, control=settings) # The default, useCSSoffset = TRUE, automatically includes the CSS scaling normalization factor. ## ----contrasts-------------------------------------------- # maxit=1 is for demonstration purposes settings = zigControl(maxit=1,verbose=FALSE) mod = model.matrix(~bodySite) colnames(mod) = levels(bodySite) # fitting the ZIG model res = fitZig(obj = lungTrim,mod=mod,control=settings) # The output of fitZig contains a list of various useful items. hint: names(res). # # Probably the most useful is the limma 'MLArrayLM' object called fit. zigFit = slot(res,"fit") finalMod = slot(res,"fit")$design contrast.matrix = makeContrasts(BAL.A-BAL.B,OW-PSB,levels=finalMod) fit2 = contrasts.fit(zigFit, contrast.matrix) fit2 = eBayes(fit2) topTable(fit2) # See help pages on decideTests, topTable, topTableF, vennDiagram, etc. ## ----fittedResult,tidy=TRUE------------------------------- taxa = sapply(strsplit(as.character(fData(lungTrim)$taxa),split=";"), function(i){i[length(i)]}) head(MRcoefs(fit,taxa=taxa,coef=2)) ## ----timeSeries------------------------------------------- # vignette("fitTimeSeries") ## ----perm------------------------------------------------- coeffOfInterest = 2 res = fitLogNormal(obj = lungTrim, mod = mod, useCSSoffset = FALSE, B = 10, coef = coeffOfInterest) # extract p.values and adjust for multiple testing # res$p are the p-values calculated through permutation adjustedPvalues = p.adjust(res$p,method="fdr") # extract the absolute fold-change estimates foldChange = abs(res$fit$coef[,coeffOfInterest]) # determine features still significant and order by the sigList = which(adjustedPvalues <= .05) sigList = sigList[order(foldChange[sigList])] # view the top taxa associated with the coefficient of interest. head(taxa[sigList]) ## ----presenceAbsence-------------------------------------- classes = pData(mouseData)$diet res = fitPA(mouseData[1:5,],cl=classes) # Warning - the p-value is calculating 1 despite a high odd's ratio. head(res) ## ----discOdds--------------------------------------------- classes = pData(mouseData)$diet res = fitDO(mouseData[1:100,],cl=classes,norm=FALSE,log=FALSE) head(res) ## ----corTest---------------------------------------------- cors = correlationTest(mouseData[55:60,],norm=FALSE,log=FALSE) head(cors) ## ----uniqueFeatures--------------------------------------- cl = pData(mouseData)[["diet"]] uniqueFeatures(mouseData,cl,nsamples = 10,nreads = 100) ## ----aggTax----------------------------------------------- obj = aggTax(mouseData,lvl='phylum',out='matrix') head(obj[1:5,1:5]) ## ----aggSamp---------------------------------------------- obj = aggSamp(mouseData,fct='mouseID',out='matrix') head(obj[1:5,1:5]) ## ----interactiveDisplay----------------------------------- # Calling display on the MRexperiment object will start a browser session with interactive plots. # require(interactiveDisplay) # display(mouseData) ## ----heatmapData,fig.cap='Left) Abundance heatmap (plotMRheatmap). Right) Correlation heatmap (plotCorr).',dev='pdf',fig.show='hold',out.width='.5\\linewidth', out.height='.5\\linewidth'---- trials = pData(mouseData)$diet heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))]; heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50) # plotMRheatmap plotMRheatmap(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none", col = heatmapCols,ColSideColors = heatmapColColors) # plotCorr plotCorr(obj=mouseData,n=200,cexRow = 0.25,cexCol = 0.25, trace="none",dendrogram="none",col=heatmapCols) ## ----MDSandRareplots,fig.cap='Left) CMDS of features (plotOrd). Right) Rarefaction effect (plotRare).',dev='pdf',fig.show='hold',out.width='.5\\linewidth', out.height='.5\\linewidth'---- cl = factor(pData(mouseData)$diet) # plotOrd - can load vegan and set distfun = vegdist and use dist.method="bray" plotOrd(mouseData,tran=TRUE,usePCA=FALSE,useDist=TRUE,bg=cl,pch=21) # plotRare res = plotRare(mouseData,cl=cl,pch=21,bg=cl) # Linear fits for plotRare / legend tmp=lapply(levels(cl), function(lv) lm(res[,"ident"]~res[,"libSize"]-1, subset=cl==lv)) for(i in 1:length(levels(cl))){ abline(tmp[[i]], col=i) } legend("topleft", c("Diet 1","Diet 2"), text.col=c(1,2),box.col=NA) ## ----plotOTUData,fig.cap='Left) Abundance plot (plotOTU). Right) Multiple OTU abundances (plotGenus).',dev='pdf',fig.show='hold',out.width='.5\\linewidth', out.height='.5\\linewidth',tidy=TRUE---- head(MRtable(fit,coef=2,taxa=1:length(fData(lungTrim)$taxa))) patients=sapply(strsplit(rownames(pData(lungTrim)),split="_"), function(i){ i[3] }) pData(lungTrim)$patients=patients classIndex=list(smoker=which(pData(lungTrim)$SmokingStatus=="Smoker")) classIndex$nonsmoker=which(pData(lungTrim)$SmokingStatus=="NonSmoker") otu = 779 # plotOTU plotOTU(lungTrim,otu=otu,classIndex,main="Neisseria meningitidis") # Now multiple OTUs annotated similarly x = fData(lungTrim)$taxa[otu] otulist = grep(x,fData(lungTrim)$taxa) # plotGenus plotGenus(lungTrim,otulist,classIndex,labs=FALSE, main="Neisseria meningitidis") lablist<- c("S","NS") axis(1, at=seq(1,6,by=1), labels = rep(lablist,times=3)) ## ----plotFeatureData,fig.cap='Plot of raw abundances',dev='pdf',fig.show='hold',out.width='.5\\linewidth', out.height='.5\\linewidth',tidy=TRUE---- classIndex=list(Western=which(pData(mouseData)$diet=="Western")) classIndex$BK=which(pData(mouseData)$diet=="BK") otuIndex = 8770 # par(mfrow=c(1,2)) dates = pData(mouseData)$date plotFeature(mouseData,norm=FALSE,log=FALSE,otuIndex,classIndex, col=dates,sortby=dates,ylab="Raw reads") ## ----cite------------------------------------------------- citation("metagenomeSeq") ## ----sessionInfo------------------------------------------ sessionInfo() metagenomeSeq/inst/doc/metagenomeSeq.Rnw0000644000175200017520000012230214710220170021364 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{metagenomeSeq: statistical analysis for sparse high-throughput sequencing} %\VignetteEngine{knitr::knitr} \documentclass[a4paper,11pt]{article} \usepackage{url} \usepackage{afterpage} \usepackage{hyperref} \usepackage{geometry} \usepackage{cite} \geometry{hmargin=2.5cm, vmargin=2.5cm} \usepackage{graphicx} \usepackage{courier} \bibliographystyle{unsrt} \begin{document} <>= require(knitr) opts_chunk$set(concordance=TRUE,tidy=TRUE) @ \title{{\textbf{\texttt{metagenomeSeq}: Statistical analysis for sparse high-throughput sequencing}}} \author{Joseph Nathaniel Paulson\\[1em]\\ Applied Mathematics $\&$ Statistics, and Scientific Computation\\ Center for Bioinformatics and Computational Biology\\ University of Maryland, College Park\\[1em]\\ \texttt{jpaulson@umiacs.umd.edu}} \date{Modified: October 4, 2016. Compiled: \today} \maketitle \tableofcontents \newpage <>= options(width = 60) options(continue=" ") options(warn=-1) set.seed(42) @ \section{Introduction} \textbf{This is a vignette for pieces of an association study pipeline. For a full list of functions available in the package: help(package=metagenomeSeq). For more information about a particular function call: ?function.} See \textit{fitFeatureModel} for our latest development. To load the metagenomeSeq library: <>= library(metagenomeSeq) @ Metagenomics is the study of genetic material targeted directly from an environmental community. Originally focused on exploratory and validation projects, these studies now focus on understanding the differences in microbial communities caused by phenotypic differences. Analyzing high-throughput sequencing data has been a challenge to researchers due to the unique biological and technological biases that are present in marker-gene survey data. We present a R package, \texttt{metagenomeSeq}, that implements methods developed to account for previously unaddressed biases specific to high-throughput sequencing microbial marker-gene survey data. Our method implements a novel normalization technique and method to account for sparsity due to undersampling. Other methods include White \textit{et al.}'s Metastats and Segata \textit{et al.}'s LEfSe. The first is a non-parametric permutation test on $t$-statistics and the second is a non-parametric Kruskal-Wallis test followed by subsequent wilcox rank-sum tests on subgroups to guard against positive discoveries of differential abundance driven by potential confounders - neither address normalization nor sparsity. This vignette describes the basic protocol when using \texttt{metagenomeSeq}. A normalization method able to control for biases in measurements across taxonomic features and a mixture model that implements a zero-inflated Gaussian distribution to account for varying depths of coverage are implemented. Using a linear model methodology, it is easy to include confounding sources of variability and interpret results. Additionally, visualization functions are provided to examine discoveries. The software was designed to determine features (be it Operational Taxonomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. The software was also designed to address the effects of both normalization and undersampling of microbial communities on disease association detection and testing of feature correlations. \begin{figure} \centerline{\includegraphics[width=.55\textwidth]{overview.pdf}} \caption{General overview. metagenomeSeq requires the user to convert their data into MRexperiment objects. Using those MRexperiment objects, one can normalize their data, run statistical tests (abundance or presence-absence), and visualize or save results.} \end{figure} \newpage \section{Data preparation} Microbial marker-gene sequence data is preprocessed and counts are algorithmically defined from project-specific sequence data by clustering reads according to read similarity. Given $m$ features and $n$ samples, the elements in a count matrix \textbf{C} ($m, n$), $c_{ij}$, are the number of reads annotated for a particular feature $i$ (whether it be OTU, species, genus, etc.) in sample $j$. \\ \begin{center} $\bordermatrix{ &sample_1&sample_2&\ldots &sample_n\cr feature_1&c_{11} & c_{12} & \ldots & c_{1n}\cr feature_2& c_{21} & c_{22} & \ldots & c_{2n}\cr \vdots & \vdots & \vdots & \ddots & \vdots\cr feature_m & c_{m1} & c_{m2} &\ldots & c_{mn}}$ \end{center} Count data should be stored in a delimited (tab by default) file with sample names along the first row and feature names along the first column. Data is prepared and formatted as a \texttt{MRexperiment} object. For an overview of the internal structure please see Appendix A. \subsection{Biom-Format} You can load in BIOM file format data, the output of many commonly used, using the \texttt{loadBiom} function. The \texttt{biom2MRexperiment} and \texttt{MRexperiment2biom} functions serve as a gateway between the \texttt{biom-class} object defined in the \textbf{biom} package and a \texttt{MRexperiment-class} object. BIOM format files IO is available thanks to the \texttt{biomformat} package. As an example, we show how one can read in a BIOM file and convert it to a \texttt{MRexperiment} object. <>= # reading in a biom file library(biomformat) biom_file <- system.file("extdata", "min_sparse_otu_table.biom", package = "biomformat") b <- read_biom(biom_file) biom2MRexperiment(b) @ As an example, we show how one can write a \texttt{MRexperiment} object out as a BIOM file. Here is an example writing out the mouseData \texttt{MRexperiment} object to a BIOM file. <>= data(mouseData) # options include to normalize or not b <- MRexperiment2biom(mouseData) write_biom(b,biom_file="~/Desktop/otu_table.biom") @ \subsection{Loading count data} Following preprocessing and annotation of sequencing data \texttt{metagenomeSeq} requires a count matrix with features along rows and samples along the columns. \texttt{metagenomeSeq} includes functions for loading delimited files of counts \texttt{loadMeta} and phenodata \texttt{loadPhenoData}. As an example, a portion of the lung microbiome \cite{charlson} OTU matrix is provided in \texttt{metagenomeSeq}'s library "extdata" folder. The OTU matrix is stored as a tab delimited file. \texttt{loadMeta} loads the taxa and counts into a list. <>= dataDirectory <- system.file("extdata", package="metagenomeSeq") lung = loadMeta(file.path(dataDirectory,"CHK_NAME.otus.count.csv")) dim(lung$counts) @ \subsection{Loading taxonomy} Next we want to load the annotated taxonomy. Check to make sure that your taxa annotations and OTUs are in the same order as your matrix rows. <>= taxa = read.delim(file.path(dataDirectory,"CHK_otus.taxonomy.csv"),stringsAsFactors=FALSE) @ As our OTUs appear to be in order with the count matrix we loaded earlier, the next step is to load phenodata. \textbf{Warning}: features need to have the same names as the rows of the count matrix when we create the MRexperiment object for provenance purposes. \subsection{Loading metadata} Phenotype data can be optionally loaded into \texttt{R} with \texttt{loadPhenoData}. This function loads the data as a list. <>= clin = loadPhenoData(file.path(dataDirectory,"CHK_clinical.csv"),tran=TRUE) ord = match(colnames(lung$counts),rownames(clin)) clin = clin[ord,] head(clin[1:2,]) @ \textbf{Warning}: phenotypes must have the same names as the columns on the count matrix when we create the MRexperiment object for provenance purposes. \subsection{Creating a \texttt{MRexperiment} object} Function \texttt{newMRexperiment} takes a count matrix, phenoData (annotated data frame), and featureData (annotated data frame) as input. \texttt{Biobase} provides functions to create annotated data frames. Library sizes (depths of coverage) and normalization factors are also optional inputs. <>= phenotypeData = AnnotatedDataFrame(clin) phenotypeData @ A feature annotated data frame. In this example it is simply the OTU numbers, but it can as easily be the annotated taxonomy at multiple levels. <>= OTUdata = AnnotatedDataFrame(taxa) OTUdata @ <>= obj = newMRexperiment(lung$counts,phenoData=phenotypeData,featureData=OTUdata) # Links to a paper providing further details can be included optionally. # experimentData(obj) = annotate::pmid2MIAME("21680950") obj @ \subsection{Example datasets} There are two datasets included as examples in the \texttt{metagenomeSeq} package. Data needs to be in a \texttt{MRexperiment} object format to normalize, run statistical tests, and visualize. As an example, throughout the vignette we'll use the following datasets. To understand a function's usage or included data simply enter ?functionName. \begin{enumerate} \item Human lung microbiome \cite{charlson}: The lung microbiome consists of respiratory flora sampled from six healthy individuals. Three healthy nonsmokers and three healthy smokers. The upper lung tracts were sampled by oral wash and oro-/nasopharyngeal swabs. Samples were taken using two bronchoscopes, serial bronchoalveolar lavage and lower airway protected brushes. \end{enumerate} <>= data(lungData) lungData @ \begin{enumerate} \setcounter{enumi}{1} \item Humanized gnotobiotic mouse gut \cite{ts_mouse}: Twelve germ-free adult male C57BL/6J mice were fed a low-fat, plant polysaccharide-rich diet. Each mouse was gavaged with healthy adult human fecal material. Following the fecal transplant, mice remained on the low-fat, plant polysacchaaride-rich diet for four weeks, following which a subset of 6 were switched to a high-fat and high-sugar diet for eight weeks. Fecal samples for each mouse went through PCR amplification of the bacterial 16S rRNA gene V2 region weekly. Details of experimental protocols and further details of the data can be found in Turnbaugh et. al. Sequences and further information can be found at: \url{http://gordonlab.wustl.edu/TurnbaughSE_10_09/STM_2009.html} \end{enumerate} <>= data(mouseData) mouseData @ \newpage \subsection{Useful commands} Phenotype information can be accessed with the \verb+phenoData+ and \verb+pData+ methods: <>= phenoData(obj) head(pData(obj),3) @ Feature information can be accessed with the \verb+featureData+ and \verb+fData+ methods: <>= featureData(obj) head(fData(obj)[,-c(2,10)],3) @ \newpage The raw or normalized counts matrix can be accessed with the \verb+MRcounts+ function: <>= head(MRcounts(obj[,1:2])) @ A \texttt{MRexperiment-class} object can be easily subsetted, for example: <<>>= featuresToKeep = which(rowSums(obj)>=100) samplesToKeep = which(pData(obj)$SmokingStatus=="Smoker") obj_smokers = obj[featuresToKeep,samplesToKeep] obj_smokers head(pData(obj_smokers),3) @ Alternative normalization scaling factors can be accessed or replaced with the \verb+normFactors+ method: <>= head(normFactors(obj)) normFactors(obj) <- rnorm(ncol(obj)) head(normFactors(obj)) @ Library sizes (sequencing depths) can be accessed or replaced with the \verb+libSize+ method: <>= head(libSize(obj)) libSize(obj) <- rnorm(ncol(obj)) head(libSize(obj)) @ \newpage Additionally, data can be filtered to maintain a threshold of minimum depth or OTU presence: <>= data(mouseData) filterData(mouseData,present=10,depth=1000) @ Two \texttt{MRexperiment-class} objects can be merged with the \texttt{mergeMRexperiments} function, e.g.: <>= data(mouseData) newobj = mergeMRexperiments(mouseData,mouseData) newobj @ \newpage \section{Normalization} Normalization is required due to varying depths of coverage across samples. \texttt{cumNorm} is a normalization method that calculates scaling factors equal to the sum of counts up to a particular quantile. Denote the $l$th quantile of sample $j$ as $q_j^l$, that is, in sample $j$ there are $l$ taxonomic features with counts smaller than $q_j^l$. For $l= \lfloor .95m \rfloor$ then $q_j^l$ corresponds to the 95th percentile of the count distribution for sample $j$. Denote $s_j^l= \sum_{(i|c_{ij}\leq q_j^l)}c_{ij}$ as the sum of counts for sample $j$ up to the $l$th quantile. Our normalization chooses a value $\hat{l}\leq m$ to define a normalization scaling factor for each sample to produce normalized counts $\tilde{c_{ij}}$ = $\frac{c_{ij}}{s_j^{\hat{l}}}N$ where $N$ is an appropriately chosen normalization constant. See Appendix C for more information on how our method calculates the proper percentile. These normalization factors are stored in the experiment summary slot. Functions to determine the proper percentile \texttt{cumNormStat}, save normalized counts \texttt{exportMat}, or save various sample statistics \texttt{exportStats} are also provided. Normalized counts can be called easily by \texttt{cumNormMat(MRexperimentObject)} or \texttt{MRcounts(MRexperimentObject,norm=TRUE,log=FALSE)}. \subsection{Calculating normalization factors} After defining a \texttt{MRexperiment} object, the first step is to calculate the proper percentile by which to normalize counts. There are several options in calculating and visualizing the relative differences in the reference. Figure 3 is an example from the lung dataset. <>= data(lungData) p=cumNormStatFast(lungData) @ \noindent To calculate the scaling factors we simply run \texttt{cumNorm} <>= lungData = cumNorm(lungData,p=p) @ The user can alternatively choose different percentiles for the normalization scheme by specifying $p$. There are other functions, including \texttt{normFactors}, \texttt{cumNormMat}, that return the normalization factors or a normalized matrix for a specified percentile. To see a full list of functions please refer to the manual and help pages. \subsubsection{Calculating normalization factors using Wrench} An alternative to normalizing counts using \texttt{cumNorm} is to use \texttt{wrenchNorm}. It behaves similarly to \texttt{cumNorm}, however, it takes the argument \texttt{condition} instead of \texttt{p}. \texttt{condition} is a factor with values that separate samples into phenotypic groups of interest. When appropriate, wrench normalization is preferrable over cumulative normalization (see https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-5160-5 for details). In the example below, \texttt{mouseData} samples are compared based on diet. <>= condition = mouseData$diet mouseData = wrenchNorm(mouseData,condition=condition) @ \subsection{Exporting data} To export normalized count matrices: <>= mat = MRcounts(lungData,norm=TRUE,log=TRUE)[1:5,1:5] exportMat(mat,file=file.path(dataDirectory,"tmp.tsv")) @ \noindent To save sample statistics (sample scaling factor, quantile value, number of identified features and library size): <>= exportStats(lungData[,1:5],file=file.path(dataDirectory,"tmp.tsv")) head(read.csv(file=file.path(dataDirectory,"tmp.tsv"),sep="\t")) @ <>= system(paste("rm",file.path(dataDirectory,"tmp.tsv"))) @ \newpage \section{Statistical testing} Now that we have taken care of normalization we can address the effects of under sampling on detecting differentially abundant features (OTUs, genes, etc). This is our latest development and we recommend \textit{fitFeatureModel} over \textit{fitZig}. \textit{MRcoefs}, \textit{MRtable} and \textit{MRfulltable} are useful summary tables of the model outputs. \subsection{Zero-inflated Log-Normal mixture model for each feature} By reparametrizing our zero-inflation model, we're able to fit a zero-inflated model for each specific OTU separately. We currently recommend using the zero-inflated log-normal model as implemented in \textit{fitFeatureModel}. \subsubsection{Example using fitFeatureModel for differential abundance testing} Here is an example comparing smoker's and non-smokers lung microbiome. <>= data(lungData) lungData = lungData[,-which(is.na(pData(lungData)$SmokingStatus))] lungData=filterData(lungData,present=30,depth=1) lungData <- cumNorm(lungData, p=.5) pd <- pData(lungData) mod <- model.matrix(~1+SmokingStatus, data=pd) lungres1 = fitFeatureModel(lungData,mod) head(MRcoefs(lungres1)) @ \subsection{Zero-inflated Gaussian mixture model} The depth of coverage in a sample is directly related to how many features are detected in a sample motivating our zero-inflated Gaussian (ZIG) mixture model. Figure 2 is representative of the linear relationship between depth of coverage and OTU identification ubiquitous in marker-gene survey datasets currently available. For a quick overview of the mathematical model see Appendix B. \begin{figure} \centerline{\includegraphics[width=.55\textwidth]{metagenomeSeq_figure1.png}} \caption{\footnotesize{The number of unique features is plotted against depth of coverage for samples from the Human Microbiome Project \cite{hmp}. Including the depth of coverage and the interaction of body site and sequencing site we are able to acheive an adjusted $\mathrm{R}^2$ of .94. The zero-inflated Gaussian mixture was developed to account for missing features.}}\label{fig1} \end{figure} Function \texttt{fitZig} performs a complex mathematical optimization routine to estimate probabilities that a zero for a particular feature in a sample is a technical zero or not. The function relies heavily on the \texttt{limma} package \cite{limma}. Design matrices can be created in R by using the \texttt{model.matrix} function and are inputs for \texttt{fitZig}. For large survey studies it is often pertinent to include phenotype information or confounders into a design matrix when testing the association between the abundance of taxonomic features and a phenotype of interest (disease, for instance). Our linear model methodology can easily incorporate these confounding covariates in a straightforward manner. \texttt{fitZig} output includes weighted fits for each of the $m$ features. Results can be filtered and saved using \texttt{MRcoefs} or \texttt{MRtable}. \subsubsection{Example using fitZig for differential abundance testing} \textbf{Warning}: The user should restrict significant features to those with a minimum number of positive samples. What this means is that one should not claim features are significant unless the effective number of samples is above a particular percentage. For example, fold-change estimates might be unreliable if an entire group does not have a positive count for the feature in question. We recommend the user remove features based on the number of estimated effective samples, please see \texttt{calculateEffectiveSamples}. We recommend removing features with less than the average number of effective samples in all features. In essence, setting eff = .5 when using \texttt{MRcoefs}, \texttt{MRfulltable}, or \texttt{MRtable}. To find features absent from a group the function \texttt{uniqueFeatures} provides a table of the feature ids, the number of positive features and reads for each group. In our analysis of the lung microbiome data, we can remove features that are not present in many samples, controls, and calculate the normalization factors. The user needs to decide which metadata should be included in the linear model. <>= data(lungData) controls = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-controls] rareFeatures = which(rowSums(MRcounts(lungTrim)>0)<10) lungTrim = lungTrim[-rareFeatures,] lungp = cumNormStat(lungTrim,pFlag=TRUE,main="Trimmed lung data") lungTrim = cumNorm(lungTrim,p=lungp) @ After the user defines an appropriate model matrix for hypothesis testing there are optional inputs to \texttt{fitZig}, including settings determined by \texttt{zigControl}. We ask the user to review the help files for both \texttt{fitZig} and \texttt{zigControl}. For this example we include body site as covariates and want to test for the bacteria differentially abundant between smokers and non-smokers. <>= smokingStatus = pData(lungTrim)$SmokingStatus bodySite = pData(lungTrim)$SampleType normFactor = normFactors(lungTrim) normFactor = log2(normFactor/median(normFactor) + 1) mod = model.matrix(~smokingStatus+bodySite + normFactor) settings = zigControl(maxit=10,verbose=TRUE) fit = fitZig(obj = lungTrim,mod=mod,useCSSoffset = FALSE, control=settings) # The default, useCSSoffset = TRUE, automatically includes the CSS scaling normalization factor. @ The result, \texttt{fit}, is a list providing detailed estimates of the fits including a \texttt{limma} fit in \texttt{fit\$fit} and an \texttt{ebayes} statistical fit in \texttt{fit\$eb}. This data can be analyzed like any \texttt{limma} fit and in this example, the column of the fitted coefficients represents the fold-change for our "smoker" vs. "nonsmoker" analysis. Looking at the particular analysis just performed, there appears to be OTUs representing two \textit{Prevotella}, two \textit{Neisseria}, a \textit{Porphyromonas} and a \textit{Leptotrichia} that are differentially abundant. One should check that similarly annotated OTUs are not equally differentially abundant in controls. Alternatively, the user can input a model with their own normalization factors including them directly in the model matrix and specifying the option \texttt{useCSSoffset = FALSE} in fitZig. \subsubsection{Multiple groups} Assuming there are multiple groups it is possible to make use of Limma's topTable functions for F-tests and contrast functions to compare multiple groups and covariates of interest. The output of fitZig includes a 'MLArrayLM' Limma object that can be called on by other functions. When running fitZig by default there is an additional covariate added to the design matrix. The fit and the ultimate design matrix are crucial for contrasts. <>= # maxit=1 is for demonstration purposes settings = zigControl(maxit=1,verbose=FALSE) mod = model.matrix(~bodySite) colnames(mod) = levels(bodySite) # fitting the ZIG model res = fitZig(obj = lungTrim,mod=mod,control=settings) # The output of fitZig contains a list of various useful items. hint: names(res). # # Probably the most useful is the limma 'MLArrayLM' object called fit. zigFit = slot(res,"fit") finalMod = slot(res,"fit")$design contrast.matrix = makeContrasts(BAL.A-BAL.B,OW-PSB,levels=finalMod) fit2 = contrasts.fit(zigFit, contrast.matrix) fit2 = eBayes(fit2) topTable(fit2) # See help pages on decideTests, topTable, topTableF, vennDiagram, etc. @ Further specific details can be found in section 9.3 and beyond of the Limma user guide. The take home message is that to make use of any Limma functions one needs to extract the final model matrix used: \textit{res\$fit\$design} and the MLArrayLM Limma fit object: \textit{res\$fit}. \subsubsection{Exporting fits} Currently functions are being developed to wrap and output results more neatly, but \texttt{MRcoefs}, \texttt{MRtable}, \texttt{MRfulltable} can be used to view coefficient fits and related statistics and export the data with optional output values - see help files to learn how they differ. An important note is that the \texttt{by} variable controls which coefficients are of interest whereas \texttt{coef} determines the display.\\ To only consider features that are found in a large percentage of effectively positive (positive samples + the weight of zero counts included in the Gaussian mixture) use the \textbf{eff} option in the \texttt{MRtables}. <>= taxa = sapply(strsplit(as.character(fData(lungTrim)$taxa),split=";"), function(i){i[length(i)]}) head(MRcoefs(fit,taxa=taxa,coef=2)) @ \subsection{Time series analysis} Implemented in the \texttt{fitTimeSeries} function is a method for calculating time intervals for which bacteria are differentially abundant. Fitting is performed using Smoothing Splines ANOVA (SS-ANOVA), as implemented in the \texttt{gss} package. Given observations at multiple time points for two groups the method calculates a function modeling the difference in abundance across all time. Using group membership permutations weestimate a null distribution of areas under the difference curve for the time intervals of interest and report significant intervals of time. Use of the function for analyses should cite: "Finding regions of interest in high throughput genomics data using smoothing splines" Talukder H, Paulson JN, Bravo HC. (Submitted) For a description of how to perform a time-series / genome based analysis call the \texttt{fitTimeSeries} vignette. <>= # vignette("fitTimeSeries") @ \subsection{Log Normal permutation test} Included is a standard log normal linear model with permutation based p-values permutation. We show the fit for the same model as above using 10 permutations providing p-value resolution to the tenth. The \texttt{coef} parameter refers to the coefficient of interest to test. We first generate the list of significant features. <>= coeffOfInterest = 2 res = fitLogNormal(obj = lungTrim, mod = mod, useCSSoffset = FALSE, B = 10, coef = coeffOfInterest) # extract p.values and adjust for multiple testing # res$p are the p-values calculated through permutation adjustedPvalues = p.adjust(res$p,method="fdr") # extract the absolute fold-change estimates foldChange = abs(res$fit$coef[,coeffOfInterest]) # determine features still significant and order by the sigList = which(adjustedPvalues <= .05) sigList = sigList[order(foldChange[sigList])] # view the top taxa associated with the coefficient of interest. head(taxa[sigList]) @ \subsection{Presence-absence testing} The hypothesis for the implemented presence-absence test is that the proportion/odds of a given feature present is higher/lower among one group of individuals compared to another, and we want to test whether any difference in the proportions observed is significant. We use Fisher's exact test to create a 2x2 contingency table and calculate p-values, odd's ratios, and confidence intervals. \texttt{fitPA} calculates the presence-absence for each organism and returns a table of p-values, odd's ratios, and confidence intervals. The function will accept either a \texttt{MRexperiment} object or matrix. \texttt{MRfulltable} when sent a result of fitZig will also include the results of \texttt{fitPA}. <>= classes = pData(mouseData)$diet res = fitPA(mouseData[1:5,],cl=classes) # Warning - the p-value is calculating 1 despite a high odd's ratio. head(res) @ \subsection{Discovery odds ratio testing} The hypothesis for the implemented discovery test is that the proportion of observed counts for a feature of all counts are comparable between groups. We use Fisher's exact test to create a 2x2 contingency table and calculate p-values, odd's ratios, and confidence intervals. \texttt{fitDO} calculates the proportion of counts for each organism and returns a table of p-values, odd's ratios, and confidence intervals. The function will accept either a \texttt{MRexperiment} object or matrix. <>= classes = pData(mouseData)$diet res = fitDO(mouseData[1:100,],cl=classes,norm=FALSE,log=FALSE) head(res) @ \subsection{Feature correlations} To test the correlations of abundance features, or samples, in a pairwise fashion we have implemented \texttt{correlationTest} and \texttt{correctIndices}. The \texttt{correlationTest} function will calculate basic pearson, spearman, kendall correlation statistics for the rows of the input and report the associated p-values. If a vector of length ncol(obj) it will also calculate the correlation of each row with the associated vector. <>= cors = correlationTest(mouseData[55:60,],norm=FALSE,log=FALSE) head(cors) @ \textbf{Caution:} http://www.ncbi.nlm.nih.gov/pubmed/23028285 \subsection{Unique OTUs or features} To find features absent from any number of classes the function \texttt{uniqueFeatures} provides a table of the feature ids, the number of positive features and reads for each group. Thresholding for the number of positive samples or reads required are options. <>= cl = pData(mouseData)[["diet"]] uniqueFeatures(mouseData,cl,nsamples = 10,nreads = 100) @ \newpage \section{Aggregating counts} Normalization is recommended at the OTU level. However, functions are in place to aggregate the count matrix (normalized or not), based on a particular user defined level. Using the featureData information in the MRexperiment object, calling \texttt{aggregateByTaxonomy} or \texttt{aggTax} on a MRexperiment object and declaring particular featureData column name (i.e. 'genus') will aggregate counts to the desired level with the aggfun function (default colSums). Possible aggfun alternatives include colMeans and colMedians. <>= obj = aggTax(mouseData,lvl='phylum',out='matrix') head(obj[1:5,1:5]) @ Additionally, aggregating samples can be done using the phenoData information in the MRexperiment object. Calling \texttt{aggregateBySample} or \texttt{aggsamp} on a MRexperiment object and declaring a particular phenoData column name (i.e. 'diet') will aggregate counts with the aggfun function (default rowMeans). Possible aggfun alternatives include rowSums and rowMedians. <>= obj = aggSamp(mouseData,fct='mouseID',out='matrix') head(obj[1:5,1:5]) @ The \texttt{aggregateByTaxonomy},\texttt{aggregateBySample}, \texttt{aggTax} \texttt{aggSamp} functions are flexible enough to put in either 1) a matrix with a vector of labels or 2) a MRexperiment object with a vector of labels or featureData column name. The function can also output either a matrix or MRexperiment object. \newpage \section{Visualization of features} To help with visualization and analysis of datasets \texttt{metagenomeSeq} has several plotting functions to gain insight of the dataset's overall structure and particular individual features. An initial interactive exploration of the data can be displayed with the \texttt{display} function. For an overall look at the dataset we provide a number of plots including heatmaps of feature counts: \texttt{plotMRheatmap}, basic feature correlation structures: \texttt{plotCorr}, PCA/MDS coordinates of samples or features: \texttt{plotOrd}, rarefaction effects: \texttt{plotRare} and contingency table style plots: \texttt{plotBubble}. Other plotting functions look at particular features such as the abundance for a single feature: \texttt{plotOTU} and \texttt{plotFeature}, or of multiple features at once: \texttt{plotGenus}. Plotting multiple OTUs with similar annotations allows for additional control of false discoveries. \subsection{Interactive Display} Due to recent advances in the \texttt{interactiveDisplay} package, calling the \texttt{display} function on \texttt{MRexperiment} objects will bring up a browser to explore your data through several interactive visualizations. For more detailed interactive visualizations one might be interested in the shiny-phyloseq package. <>= # Calling display on the MRexperiment object will start a browser session with interactive plots. # require(interactiveDisplay) # display(mouseData) @ \subsection{Structural overview} Many studies begin by comparing the abundance composition across sample or feature phenotypes. Often a first step of data analysis is a heatmap, correlation or co-occurence plot or some other data exploratory method. The following functions have been implemented to provide a first step overview of the data: \begin{enumerate} \item \texttt{plotMRheatmap} - heatmap of abundance estimates (Fig. 4 left) \item \texttt{plotCorr} - heatmap of pairwise correlations (Fig. 4 right) \item \texttt{plotOrd} - PCA/CMDS components (Fig. 5 left) \item \texttt{plotRare} - rarefaction effect (Fig. 5 right) \item \texttt{plotBubble} - contingency table style plot (see help) \end{enumerate} \noindent Each of the above can include phenotypic information in helping to explore the data. Below we show an example of how to create a heatmap and hierarchical clustering of $\log_2$ transformed counts for the 200 OTUs with the largest overall variance. Red values indicate counts close to zero. Row color labels indicate OTU taxonomic class; column color labels indicate diet (green = high fat, yellow = low fat). Notice the samples cluster by diet in these cases and there are obvious clusters. We then plot a correlation matrix for the same features. <>= trials = pData(mouseData)$diet heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))]; heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50) # plotMRheatmap plotMRheatmap(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none", col = heatmapCols,ColSideColors = heatmapColColors) # plotCorr plotCorr(obj=mouseData,n=200,cexRow = 0.25,cexCol = 0.25, trace="none",dendrogram="none",col=heatmapCols) @ Below is an example of plotting CMDS plots of the data and the rarefaction effect at the OTU level. None of the data is removed (we recommend removing outliers typically). <>= cl = factor(pData(mouseData)$diet) # plotOrd - can load vegan and set distfun = vegdist and use dist.method="bray" plotOrd(mouseData,tran=TRUE,usePCA=FALSE,useDist=TRUE,bg=cl,pch=21) # plotRare res = plotRare(mouseData,cl=cl,pch=21,bg=cl) # Linear fits for plotRare / legend tmp=lapply(levels(cl), function(lv) lm(res[,"ident"]~res[,"libSize"]-1, subset=cl==lv)) for(i in 1:length(levels(cl))){ abline(tmp[[i]], col=i) } legend("topleft", c("Diet 1","Diet 2"), text.col=c(1,2),box.col=NA) @ \subsection{Feature specific} Reads clustered with high similarity represent functional or taxonomic units. However, it is possible that reads from the same organism get clustered into multiple OTUs. Following differential abundance analysis. It is important to confirm differential abundance. One way to limit false positives is ensure that the feature is actually abundant (enough positive samples). Another way is to plot the abundances of features similarly annotated. \begin{enumerate} \item \texttt{plotOTU} - abundances of a particular feature by group (Fig. 6 left) \item \texttt{plotGenus} - abundances for several features similarly annotated by group (Fig. 6 right) \item \texttt{plotFeature} - abundances of a particular feature by group (similar to plotOTU, Fig. 7) \end{enumerate} Below we use \texttt{plotOTU} to plot the normalized log(cpt) of a specific OTU annotated as \textit{Neisseria meningitidis}, in particular the 779th row of lungTrim's count matrix. Using \texttt{plotGenus} we plot the normalized log(cpt) of all OTUs annotated as \textit{Neisseria meningitidis}. It would appear that \textit{Neisseria meningitidis} is differentially more abundant in nonsmokers. <>= head(MRtable(fit,coef=2,taxa=1:length(fData(lungTrim)$taxa))) patients=sapply(strsplit(rownames(pData(lungTrim)),split="_"), function(i){ i[3] }) pData(lungTrim)$patients=patients classIndex=list(smoker=which(pData(lungTrim)$SmokingStatus=="Smoker")) classIndex$nonsmoker=which(pData(lungTrim)$SmokingStatus=="NonSmoker") otu = 779 # plotOTU plotOTU(lungTrim,otu=otu,classIndex,main="Neisseria meningitidis") # Now multiple OTUs annotated similarly x = fData(lungTrim)$taxa[otu] otulist = grep(x,fData(lungTrim)$taxa) # plotGenus plotGenus(lungTrim,otulist,classIndex,labs=FALSE, main="Neisseria meningitidis") lablist<- c("S","NS") axis(1, at=seq(1,6,by=1), labels = rep(lablist,times=3)) @ <>= classIndex=list(Western=which(pData(mouseData)$diet=="Western")) classIndex$BK=which(pData(mouseData)$diet=="BK") otuIndex = 8770 # par(mfrow=c(1,2)) dates = pData(mouseData)$date plotFeature(mouseData,norm=FALSE,log=FALSE,otuIndex,classIndex, col=dates,sortby=dates,ylab="Raw reads") @ \newpage \section{Summary} \texttt{metagenomeSeq} is specifically designed for sparse high-throughput sequencing experiments that addresses the analysis of differential abundance for marker-gene survey data. The package, while designed for marker-gene survey datasets, may be appropriate for other sparse data sets for which the zero-inflated Gaussian mixture model may apply. If you make use of the statistical method please cite our paper. If you made use of the manual/software, please cite the manual/software! \subsection{Citing metagenomeSeq} <>= citation("metagenomeSeq") @ \subsection{Session Info} <>= sessionInfo() @ \newpage \section{Appendix} \subsection{Appendix A: MRexperiment internals} The S4 class system in R allows for object oriented definitions. \texttt{metagenomeSeq} makes use of the \texttt{Biobase} package in Bioconductor and their virtual-class, \texttt{eSet}. Building off of \texttt{eSet}, the main S4 class in \texttt{metagenomeSeq} is termed \texttt{MRexperiment}. \texttt{MRexperiment} is a simple extension of \texttt{eSet}, adding a single slot, \texttt{expSummary}. The experiment summary slot is a data frame that includes the depth of coverage and the normalization factors for each sample. Future datasets can be formated as MRexperiment objects and analyzed with relative ease. A \texttt{MRexperiment} object is created by calling \texttt{newMRexperiment}, passing the counts, phenotype and feature data as parameters. We do not include normalization factors or library size in the currently available slot specified for the sample specific phenotype data. All matrices are organized in the \texttt{assayData} slot. All phenotype data (disease status, age, etc.) is stored in \texttt{phenoData} and feature data (OTUs, taxonomic assignment to varying levels, etc.) in \texttt{featureData}. Additional slots are available for reproducibility and annotation. \subsection{Appendix B: Mathematical model} Defining the class comparison of interest as $k(j)=I\{j \in \mathrm{ group } A\}$. The zero-inflated model is defined for the continuity-corrected $\log_2$ of the count data $y_{ij} = \log_2(c_{ij}+1)$ as a mixture of a point mass at zero $I_{\{0\}}(y_{ij})$ and a count distribution $f_{count}(y_{ij};\mu_i, \sigma_i^2) \sim N(\mu_i, \sigma_i^2)$. Given mixture parameters $\pi_{j}$, we have that the density of the zero-inflated Gaussian distribution for feature $i$, in sample $j$ with $S_{j}$ total counts is: \begin{equation} f_{zig}(y_{ij}; \theta ) = \pi_{j}(S_{j}) \cdot I_{\{0\}}(y_{ij}) + (1-\pi_{j}(S_{j})) \cdot f_{count}(y_{ij};\theta) \end{equation} Maximum-likelihood estimates are approximated using an EM algorithm, where we treat mixture membership $\Delta_{ij}=1$ if $y_{ij}$ is generated from the zero point mass as latent indicator variables\cite{EM}. We make use of an EM algorithm to account for the linear relationship between sparsity and depth of coverage. The user can specify within the \texttt{fitZig} function a non-default zero model that accounts for more than simply the depth of coverage (e.g. country, age, any metadata associated with sparsity, etc.). See Figure 8 for the graphical model. \begin{figure} \centerline{\includegraphics[width=.7\textwidth]{metagenomeSeq_figure2.png}} \caption{\footnotesize{Graphical model. Green nodes represent observed variables: $S_j$ is the total number of reads in sample $j$; $k_j$ the case-control status of sample $j$; and $y_{ij}$ the logged normalized counts for feature $i$ in sample $j$. Yellow nodes represent counts obtained from each mixture component: counts come from either a spike-mass at zero, $y_{ij}^0$, or the ``count'' distribution, $y_{ij}^1$. Grey nodes $b_{0i}$, $b_{1i}$ and $\sigma_{i}^2$ represent the estimated overall mean, fold-change and variance of the count distribution component for feature $i$. $\pi_j$, is the mixture proportion for sample $j$ which depends on sequencing depth via a linear model defined by parameters $\beta_0$ and $\beta_1$. The expected value of latent indicator variables $\Delta_{ij}$ give the posterior probability of a count being generated from a spike-mass at zero, i.e. $y_{ij}^0$. We assume $M$ features and $N$ samples.}} \end{figure} More information will be included later. For now, please see the online methods in: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.2658.html \subsection{Appendix C: Calculating the proper percentile} To be included: an overview of the two methods implemented for the data driven percentile calculation and more description below. The choice of the appropriate quantile given is crucial for ensuring that the normalization approach does not introduce normalization-related artifacts in the data. At a high level, the count distribution of samples should all be roughly equivalent and independent of each other up to this quantile under the assumption that, at this range, counts are derived from a common distribution. More information will be included later. 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N!«Nazm,L'i1eB_f9eA<,뇏2@G^\A^g"+V]r!+*9q-խ߂쇁p2x )'<ʄWזMWbVmTsz{͟48w϶;ΞzeFMvgV=$ wFsPKU]]9ppt1Gov3/,ݜ:s֑ \tW]p&t8I3 MclLQ@ gt $dXf$8&w!!a3}<㷭A򫈵c6}Co\8d'\wQgL)^{-:y#r~ʤ (#\@J_լudeo*^qxKp͡>Z71:r/z˜#Yѥg'BreϠw7ÙV_,c$XT}pTM΂#) pXw5eGz/> WM|DH>i;qmYnHؙ͉   e~li^ CU}{/Pm=]r⇆pTńlg?JPƧaiGĦGF`OxȌ(G$*,);mJ ))LJR!"J0eBCEE:DFD .J?¹[6($**0 @Mdaڬ KX^ c7`odzFok3Q|gF9$칽GnӞ+UZ]ASPsE}?,zZ߄fG`IDw $2*$4tTȌ@ 'GG|DgW_metagenomeSeq/man/0000755000175200017520000000000014710220170015133 5ustar00biocbuildbiocbuildmetagenomeSeq/man/MRcoefs.Rd0000644000175200017520000000524714710220170016770 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MRcoefs.R \name{MRcoefs} \alias{MRcoefs} \title{Table of top-ranked features from fitZig or fitFeatureModel} \usage{ MRcoefs( obj, by = 2, coef = NULL, number = 10, taxa = obj@taxa, uniqueNames = FALSE, adjustMethod = "fdr", alpha = 0.1, group = 0, eff = 0, numberEff = FALSE, counts = 0, file = NULL ) } \arguments{ \item{obj}{Output of fitFeatureModel or fitZig.} \item{by}{Column number or column name specifying which coefficient or contrast of the linear model is of interest.} \item{coef}{Column number(s) or column name(s) specifying which coefficient or contrast of the linear model to display.} \item{number}{The number of bacterial features to pick out.} \item{taxa}{Taxa list.} \item{uniqueNames}{Number the various taxa.} \item{adjustMethod}{Method to adjust p-values by. Default is "FDR". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See \code{\link{p.adjust}} for more details. Additionally, options using independent hypothesis weighting (IHW) are available. See \code{\link{MRihw}} for more details.} \item{alpha}{Value for p-value significance threshold when running IHW. The default is set to 0.1} \item{group}{One of five choices, 0,1,2,3,4. 0: the sort is ordered by a decreasing absolute value coefficient fit. 1: the sort is ordered by the raw coefficient fit in decreasing order. 2: the sort is ordered by the raw coefficient fit in increasing order. 3: the sort is ordered by the p-value of the coefficient fit in increasing order. 4: no sorting.} \item{eff}{Filter features to have at least a "eff" quantile or number of effective samples.} \item{numberEff}{Boolean, whether eff should represent quantile (default/FALSE) or number.} \item{counts}{Filter features to have at least 'counts' counts.} \item{file}{Name of output file, including location, to save the table.} } \value{ Table of the top-ranked features determined by the linear fit's coefficient. } \description{ Extract a table of the top-ranked features from a linear model fit. This function will be updated soon to provide better flexibility similar to limma's topTable. } \examples{ data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] lungTrim=filterData(lungTrim,present=30) lungTrim=cumNorm(lungTrim,p=0.5) smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) fit = fitZig(obj = lungTrim,mod=mod) head(MRcoefs(fit)) #### fit = fitFeatureModel(obj = lungTrim,mod=mod) head(MRcoefs(fit)) } \seealso{ \code{\link{fitZig}} \code{\link{fitFeatureModel}} \code{\link{MRtable}} \code{\link{MRfulltable}} } metagenomeSeq/man/MRcounts.Rd0000644000175200017520000000155014710220170017175 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{methods} \name{MRcounts} \alias{MRcounts} \alias{MRcounts,MRexperiment-method} \title{Accessor for the counts slot of a MRexperiment object} \usage{ MRcounts(obj, norm = FALSE, log = FALSE, sl = 1000) } \arguments{ \item{obj}{a \code{MRexperiment} object.} \item{norm}{logical indicating whether or not to return normalized counts.} \item{log}{TRUE/FALSE whether or not to log2 transform scale.} \item{sl}{The value to scale by (default=1000).} } \value{ Normalized or raw counts } \description{ The counts slot holds the raw count data representing (along the rows) the number of reads annotated for a particular feature and (along the columns) the sample. } \examples{ data(lungData) head(MRcounts(lungData)) } \author{ Joseph N. Paulson, jpaulson@umiacs.umd.edu } metagenomeSeq/man/MRexperiment-class.Rd0000644000175200017520000000640614710220170021152 0ustar00biocbuildbiocbuild\name{MRexperiment} \Rdversion{1.0} \docType{class} \alias{MRexperiment-class} \alias{[,MRexperiment,ANY,ANY,ANY-method} \alias{[,MRexperiment-method} \alias{colSums,MRexperiment-method} \alias{rowSums,MRexperiment-method} \alias{colMeans,MRexperiment-method} \alias{rowMeans,MRexperiment-method} \alias{libSize,MRexperiment-method} \alias{normFactors,MRexperiment-method} \title{Class "MRexperiment" -- a modified eSet object for the data from high-throughput sequencing experiments} \description{This is the main class for metagenomeSeq.} \section{Objects from the Class}{ Objects should be created with calls to \code{\link{newMRexperiment}}. } \section{Extends}{ Class \code{eSet} (package 'Biobase'), directly. Class \code{VersionedBiobase} (package 'Biobase'), by class "eSet", distance 2. Class \code{Versioned} (package 'Biobase'), by class "eSet", distance 3. } \note{ Note: This is a summary for reference. For an explanation of the actual usage, see the vignette. MRexperiments are the main class in use by metagenomeSeq. The class extends eSet and provides additional slots which are populated during the analysis pipeline. MRexperiment dataset are created with calls to \code{\link{newMRexperiment}}. MRexperiment datasets contain raw count matrices (integers) accessible through \code{\link{MRcounts}}. Similarly, normalized count matrices can be accessed (following normalization) through \code{\link{MRcounts}} by calling norm=TRUE. Following an analysis, a matrix of posterior probabilities for counts is accessible through \code{\link{posteriorProbs}}. The normalization factors used in analysis can be recovered by \code{\link{normFactors}}, as can the library sizes of samples (depths of coverage), \code{\link{libSize}}. Similarly to other RNASeq bioconductor packages available, the rows of the matrix correspond to a feature (be it OTU, species, gene, etc.) and each column an experimental sample. Pertinent clinical information and potential confounding factors are stored in the phenoData slot (accessed via \code{pData}). To populate the various slots in an MRexperiment several functions are run. 1) \code{\link{cumNormStat}} calculates the proper percentile to calculate normalization factors. The cumNormStat slot is populated. 2) \code{\link{cumNorm}} calculates the actual normalization factors using p = cumNormStat. Other functions will place subsequent matrices (normalized counts (\code{\link{cumNormMat}}), posterior probabilities (\code{\link{posteriorProbs}})) As mentioned above, \code{MRexperiment} is derived from the virtual class,\code{eSet} and thereby has a \code{phenoData} slot which allows for sample annotation. In the phenoData data frame factors are stored. The normalization factors and library size information is stored in a slot called expSummary that is an annotated data frame and is repopulated for subsetted data. } \section{Methods}{ Class-specific methods. \describe{ \item{\code{[}}{Subset operation, taking two arguments and indexing the sample and variable. Returns an \code{MRexperiment object}, including relevant metadata. Setting \code{drop=TRUE} generates an error. Subsetting the data, the experiment summary slot is repopulated and pData is repopulated after calling factor (removing levels not present).} } } \examples{ # See vignette } metagenomeSeq/man/MRexperiment2biom.Rd0000644000175200017520000000155214710220170020775 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MRexperiment2biom.R \name{MRexperiment2biom} \alias{MRexperiment2biom} \title{MRexperiment to biom objects} \usage{ MRexperiment2biom( obj, id = NULL, norm = FALSE, log = FALSE, sl = 1000, qiimeVersion = TRUE ) } \arguments{ \item{obj}{The MRexperiment object.} \item{id}{Optional id for the biom matrix.} \item{norm}{normalize count table} \item{log}{log2 transform count table} \item{sl}{scaling factor for normalized counts.} \item{qiimeVersion}{Format fData according to QIIME specifications (assumes only taxonomy in fData).} } \value{ A biom object. } \description{ Wrapper to convert MRexperiment objects to biom objects. } \seealso{ \code{\link{loadMeta}} \code{\link{loadPhenoData}} \code{\link{newMRexperiment}} \code{\link{loadBiom}} \code{\link{biom2MRexperiment}} } metagenomeSeq/man/MRfulltable.Rd0000644000175200017520000000530614710220170017637 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MRfulltable.R \name{MRfulltable} \alias{MRfulltable} \title{Table of top microbial marker gene from linear model fit including sequence information} \usage{ MRfulltable( obj, by = 2, coef = NULL, number = 10, taxa = obj@taxa, uniqueNames = FALSE, adjustMethod = "fdr", group = 0, eff = 0, numberEff = FALSE, ncounts = 0, file = NULL ) } \arguments{ \item{obj}{Output of fitFeatureModel or fitZig.} \item{by}{Column number or column name specifying which coefficient or contrast of the linear model is of interest.} \item{coef}{Column number(s) or column name(s) specifying which coefficient or contrast of the linear model to display.} \item{number}{The number of bacterial features to pick out.} \item{taxa}{Taxa list.} \item{uniqueNames}{Number the various taxa.} \item{adjustMethod}{Method to adjust p-values by. Default is "FDR". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See \code{\link{p.adjust}} for more details.} \item{group}{One of five choices: 0,1,2,3,4. 0: the sort is ordered by a decreasing absolute value coefficient fit. 1: the sort is ordered by the raw coefficient fit in decreasing order. 2: the sort is ordered by the raw coefficient fit in increasing order. 3: the sort is ordered by the p-value of the coefficient fit in increasing order. 4: no sorting.} \item{eff}{Filter features to have at least a "eff" quantile or number of effective samples.} \item{numberEff}{Boolean, whether eff should represent quantile (default/FALSE) or number.} \item{ncounts}{Filter features to those with at least 'counts' counts.} \item{file}{Name of output file, including location, to save the table.} } \value{ Table of the top-ranked features determined by the linear fit's coefficient. } \description{ Extract a table of the top-ranked features from a linear model fit. This function will be updated soon to provide better flexibility similar to limma's topTable. This function differs from \code{link{MRcoefs}} in that it provides other information about the presence or absence of features to help ensure significant features called are moderately present. } \examples{ data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] lungTrim=filterData(lungTrim,present=30) lungTrim=cumNorm(lungTrim,p=0.5) smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) fit = fitZig(obj = lungTrim,mod=mod) head(MRfulltable(fit)) #### fit = fitFeatureModel(obj = lungTrim,mod=mod) head(MRfulltable(fit)) } \seealso{ \code{\link{fitZig}} \code{\link{fitFeatureModel}} \code{\link{MRcoefs}} \code{\link{MRtable}} \code{\link{fitPA}} } metagenomeSeq/man/MRihw-fitFeatureModelResults.Rd0000644000175200017520000000205614710220170023112 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MRihw.R \name{MRihw,fitFeatureModelResults-method} \alias{MRihw,fitFeatureModelResults-method} \title{MRihw runs IHW within a MRcoefs() call} \usage{ \S4method{MRihw}{fitFeatureModelResults}(obj, p, adjustMethod, alpha) } \arguments{ \item{obj}{Either a fitFeatureModelResults or fitZigResults object} \item{p}{a vector of pvalues extracted from obj} \item{adjustMethod}{Value specifying which adjustment method and which covariate to use for IHW pvalue adjustment. For obj of class \code{\link{fitFeatureModelResults-class}}, options are "ihw-abundance" (median feature count per row) and "ihw-ubiquity" (number of non-zero features per row). For obj of class \code{\link{fitZigResults-class}}, options are "ihw-abundance" (weighted mean per feature) and "ihw-ubiquity" (number of non-zero features per row).} \item{alpha}{pvalue significance level specified for IHW call. Default is 0.1} } \description{ Function used in MRcoefs() when "IHW" is set as the p value adjustment method } metagenomeSeq/man/MRihw-fitZigResults.Rd0000644000175200017520000000202314710220170021261 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MRihw.R \name{MRihw,fitZigResults-method} \alias{MRihw,fitZigResults-method} \title{MRihw runs IHW within a MRcoefs() call} \usage{ \S4method{MRihw}{fitZigResults}(obj, p, adjustMethod, alpha) } \arguments{ \item{obj}{Either a fitFeatureModelResults or fitZigResults object} \item{p}{a vector of pvalues extracted from obj} \item{adjustMethod}{Value specifying which adjustment method and which covariate to use for IHW pvalue adjustment. For obj of class \code{\link{fitFeatureModelResults-class}}, options are "ihw-abundance" (median feature count per row) and "ihw-ubiquity" (number of non-zero features per row). For obj of class \code{\link{fitZigResults-class}}, options are "ihw-abundance" (weighted mean per feature) and "ihw-ubiquity" (number of non-zero features per row).} \item{alpha}{pvalue significance level specified for IHW call. Default is 0.1} } \description{ Function used in MRcoefs() when "IHW" is set as the p value adjustment method } metagenomeSeq/man/MRihw.Rd0000644000175200017520000000061014710220170016445 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MRihw.R \name{MRihw} \alias{MRihw} \title{MRihw runs IHW within a MRcoefs() call} \usage{ MRihw(obj, ...) } \arguments{ \item{obj}{Either a fitFeatureModelResults or fitZigResults object} \item{...}{other parameters} } \description{ Function used in MRcoefs() when "IHW" is set as the p value adjustment method } metagenomeSeq/man/MRtable.Rd0000644000175200017520000000522414710220170016753 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MRtable.R \name{MRtable} \alias{MRtable} \title{Table of top microbial marker gene from linear model fit including sequence information} \usage{ MRtable( obj, by = 2, coef = NULL, number = 10, taxa = obj@taxa, uniqueNames = FALSE, adjustMethod = "fdr", group = 0, eff = 0, numberEff = FALSE, ncounts = 0, file = NULL ) } \arguments{ \item{obj}{Output of fitFeatureModel or fitZig.} \item{by}{Column number or column name specifying which coefficient or contrast of the linear model is of interest.} \item{coef}{Column number(s) or column name(s) specifying which coefficient or contrast of the linear model to display.} \item{number}{The number of bacterial features to pick out.} \item{taxa}{Taxa list.} \item{uniqueNames}{Number the various taxa.} \item{adjustMethod}{Method to adjust p-values by. Default is "FDR". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See \code{\link{p.adjust}} for more details.} \item{group}{One of five choices, 0,1,2,3,4. 0: the sort is ordered by a decreasing absolute value coefficient fit. 1: the sort is ordered by the raw coefficient fit in decreasing order. 2: the sort is ordered by the raw coefficient fit in increasing order. 3: the sort is ordered by the p-value of the coefficient fit in increasing order. 4: no sorting.} \item{eff}{Filter features to have at least a "eff" quantile or number of effective samples.} \item{numberEff}{Boolean, whether eff should represent quantile (default/FALSE) or number.} \item{ncounts}{Filter features to have at least 'counts' of counts.} \item{file}{Name of file, including location, to save the table.} } \value{ Table of the top-ranked features determined by the linear fit's coefficient. } \description{ Extract a table of the top-ranked features from a linear model fit. This function will be updated soon to provide better flexibility similar to limma's topTable. This function differs from \code{link{MRcoefs}} in that it provides other information about the presence or absence of features to help ensure significant features called are moderately present. } \examples{ data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] lungTrim=filterData(lungTrim,present=30) lungTrim=cumNorm(lungTrim,p=0.5) smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) fit = fitZig(obj = lungTrim,mod=mod) head(MRtable(fit)) #### fit = fitFeatureModel(obj = lungTrim,mod=mod) head(MRtable(fit)) } \seealso{ \code{\link{fitZig}} \code{\link{fitFeatureModel}} \code{\link{MRcoefs}} \code{\link{MRfulltable}} } metagenomeSeq/man/aggregateBySample.Rd0000644000175200017520000000235614710220170021013 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/aggregateBySample.R \name{aggregateBySample} \alias{aggregateBySample} \alias{aggSamp} \title{Aggregates a MRexperiment object or counts matrix to by a factor.} \usage{ aggregateBySample(obj, fct, aggfun = rowMeans, out = "MRexperiment") aggSamp(obj, fct, aggfun = rowMeans, out = "MRexperiment") } \arguments{ \item{obj}{A MRexperiment object or count matrix.} \item{fct}{phenoData column name from the MRexperiment object or if count matrix object a vector of labels.} \item{aggfun}{Aggregation function.} \item{out}{Either 'MRexperiment' or 'matrix'} } \value{ An aggregated count matrix or MRexperiment object where the new pData is a vector of `fct` levels. } \description{ Using the phenoData information in the MRexperiment, calling aggregateBySample on a MRexperiment and a particular phenoData column (i.e. 'diet') will aggregate counts using the aggfun function (default rowMeans). Possible aggfun alternatives include rowMeans and rowMedians. } \examples{ data(mouseData) aggregateBySample(mouseData[1:100,],fct="diet",aggfun=rowSums) # not run # aggregateBySample(mouseData,fct="diet",aggfun=matrixStats::rowMedians) # aggSamp(mouseData,fct='diet',aggfun=rowMaxs) } metagenomeSeq/man/aggregateByTaxonomy.Rd0000644000175200017520000000367114710220170021411 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/aggregateByTaxonomy.R \name{aggregateByTaxonomy} \alias{aggregateByTaxonomy} \alias{aggTax} \title{Aggregates a MRexperiment object or counts matrix to a particular level.} \usage{ aggregateByTaxonomy( obj, lvl, alternate = FALSE, norm = FALSE, log = FALSE, aggfun = colSums, sl = 1000, featureOrder = NULL, returnFullHierarchy = TRUE, out = "MRexperiment" ) aggTax( obj, lvl, alternate = FALSE, norm = FALSE, log = FALSE, aggfun = colSums, sl = 1000, featureOrder = NULL, returnFullHierarchy = TRUE, out = "MRexperiment" ) } \arguments{ \item{obj}{A MRexperiment object or count matrix.} \item{lvl}{featureData column name from the MRexperiment object or if count matrix object a vector of labels.} \item{alternate}{Use the rowname for undefined OTUs instead of aggregating to "no_match".} \item{norm}{Whether to aggregate normalized counts or not.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{aggfun}{Aggregation function.} \item{sl}{scaling value, default is 1000.} \item{featureOrder}{Hierarchy of levels in taxonomy as fData colnames} \item{returnFullHierarchy}{Boolean value to indicate return single column of fData or all columns of hierarchy} \item{out}{Either 'MRexperiment' or 'matrix'} } \value{ An aggregated count matrix. } \description{ Using the featureData information in the MRexperiment, calling aggregateByTaxonomy on a MRexperiment and a particular featureData column (i.e. 'genus') will aggregate counts to the desired level using the aggfun function (default colSums). Possible aggfun alternatives include colMeans and colMedians. } \examples{ data(mouseData) aggregateByTaxonomy(mouseData[1:100,],lvl="class",norm=TRUE,aggfun=colSums) # not run # aggregateByTaxonomy(mouseData,lvl="class",norm=TRUE,aggfun=colMedians) # aggTax(mouseData,lvl='phylum',norm=FALSE,aggfun=colSums) } metagenomeSeq/man/biom2MRexperiment.Rd0000644000175200017520000000125014710220170020770 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/biom2MRexperiment.R \name{biom2MRexperiment} \alias{biom2MRexperiment} \title{Biom to MRexperiment objects} \usage{ biom2MRexperiment(obj) } \arguments{ \item{obj}{The biom object file.} } \value{ A MRexperiment object. } \description{ Wrapper to convert biom files to MRexperiment objects. } \examples{ library(biomformat) rich_dense_file = system.file("extdata", "rich_dense_otu_table.biom", package = "biomformat") x = biomformat::read_biom(rich_dense_file) biom2MRexperiment(x) } \seealso{ \code{\link{loadMeta}} \code{\link{loadPhenoData}} \code{\link{newMRexperiment}} \code{\link{loadBiom}} } metagenomeSeq/man/calcNormFactors.Rd0000644000175200017520000000120114710220170020474 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cumNorm.R \name{calcNormFactors} \alias{calcNormFactors} \title{Cumulative sum scaling (css) normalization factors} \usage{ calcNormFactors(obj, p = cumNormStatFast(obj)) } \arguments{ \item{obj}{An MRexperiment object or matrix.} \item{p}{The pth quantile.} } \value{ Vector of the sum up to and including a sample's pth quantile. } \description{ Return a vector of the the sum up to and including a quantile. } \examples{ data(mouseData) head(calcNormFactors(mouseData)) } \seealso{ \code{\link{fitZig}} \code{\link{cumNormStatFast}} \code{\link{cumNorm}} } metagenomeSeq/man/calcPosComponent.Rd0000644000175200017520000000074614710220170020700 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitZeroLogNormal.R \name{calcPosComponent} \alias{calcPosComponent} \title{Positive component} \usage{ calcPosComponent(mat, mod, weights) } \arguments{ \item{mat}{A matrix of normalized counts} \item{mod}{A model matrix} \item{weights}{Weight matrix for samples and counts} } \description{ Fit the positive (log-normal) component } \seealso{ \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} } metagenomeSeq/man/calcShrinkParameters.Rd0000644000175200017520000000124214710220170021526 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitZeroLogNormal.R \name{calcShrinkParameters} \alias{calcShrinkParameters} \title{Calculate shrinkage parameters} \usage{ calcShrinkParameters(fit, coef, mins2, exclude = NULL) } \arguments{ \item{fit}{A matrix of fits as outputted by calcZeroComponent or calcPosComponent} \item{coef}{Coefficient of interest} \item{mins2}{minimum variance estimate} \item{exclude}{Vector of features to exclude when shrinking} } \description{ Calculate the shrunken variances and variance of parameters of interest across features. } \seealso{ \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} } metagenomeSeq/man/calcStandardError.Rd0000644000175200017520000000147214710220170021023 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitZeroLogNormal.R \name{calcStandardError} \alias{calcStandardError} \title{Calculate the zero-inflated log-normal statistic's standard error} \usage{ calcStandardError(mod, fitln, fitzero, coef = 2, exclude = NULL) } \arguments{ \item{mod}{The zero component model matrix} \item{fitln}{A matrix with parameters from the log-normal fit} \item{fitzero}{A matrix with parameters from the logistic fit} \item{coef}{Coefficient of interest} \item{exclude}{List of features to exclude} } \description{ Calculat the se for the model. Code modified from "Adjusting for covariates in zero-inflated gamma and zero-inflated log-normal models for semicontinuous data", ED Mills } \seealso{ \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} } metagenomeSeq/man/calcZeroAdjustment.Rd0000644000175200017520000000144614710220170021230 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitZeroLogNormal.R \name{calcZeroAdjustment} \alias{calcZeroAdjustment} \title{Calculate the zero-inflated component's adjustment factor} \usage{ calcZeroAdjustment(fitln, fitzero, mod, coef, exclude = NULL) } \arguments{ \item{fitln}{A matrix with parameters from the log-normal fit} \item{fitzero}{A matrix with parameters from the logistic fit} \item{mod}{The zero component model matrix} \item{coef}{Coefficient of interest} \item{exclude}{List of features to exclude} } \description{ Calculate the log ratio of average marginal probabilities for each sample having a positive count. This becomes the adjustment factor for the log fold change. } \seealso{ \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} } metagenomeSeq/man/calcZeroComponent.Rd0000644000175200017520000000074014710220170021050 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitZeroLogNormal.R \name{calcZeroComponent} \alias{calcZeroComponent} \title{Zero component} \usage{ calcZeroComponent(mat, mod, weights) } \arguments{ \item{mat}{A matrix of normalized counts} \item{mod}{A model matrix} \item{weights}{Weight matrix for samples and counts} } \description{ Fit the zero (logisitic) component } \seealso{ \code{\link{fitZeroLogNormal}} \code{\link{fitFeatureModel}} } metagenomeSeq/man/calculateEffectiveSamples.Rd0000644000175200017520000000142614710220170022530 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calculateEffectiveSamples.R \name{calculateEffectiveSamples} \alias{calculateEffectiveSamples} \title{Estimated effective samples per feature} \usage{ calculateEffectiveSamples(obj) } \arguments{ \item{obj}{The output of fitZig run on a MRexperiment object.} } \value{ A list of the estimated effective samples per feature. } \description{ Calculates the number of estimated effective samples per feature from the output of a fitZig run. The estimated effective samples per feature is calculated as the sum_1^n (n = number of samples) 1-z_i where z_i is the posterior probability a feature belongs to the technical distribution. } \seealso{ \code{\link{fitZig}} \code{\link{MRcoefs}} \code{\link{MRfulltable}} } metagenomeSeq/man/correctIndices.Rd0000644000175200017520000000203014710220170020355 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/correlationTest.R \name{correctIndices} \alias{correctIndices} \title{Calculate the correct indices for the output of correlationTest} \usage{ correctIndices(n) } \arguments{ \item{n}{The number of features compared by correlationTest (nrow(mat)).} } \value{ A vector of the indices for an upper triangular matrix. } \description{ Consider the upper triangular portion of a matrix of size nxn. Results from the \code{correlationTest} are output as the combination of two vectors, correlation statistic and p-values. The order of the output is 1vs2, 1vs3, 1vs4, etc. The correctIndices returns the correct indices to fill a correlation matrix or correlation-pvalue matrix. } \examples{ data(mouseData) mat = MRcounts(mouseData)[55:60,] cors = correlationTest(mat) ind = correctIndices(nrow(mat)) cormat = as.matrix(dist(mat)) cormat[cormat>0] = 0 cormat[upper.tri(cormat)][ind] = cors[,1] table(cormat[1,-1] - cors[1:5,1]) } \seealso{ \code{\link{correlationTest}} } metagenomeSeq/man/correlationTest.Rd0000644000175200017520000000373014710220170020606 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/correlationTest.R \name{correlationTest} \alias{correlationTest} \alias{corTest} \title{Correlation of each row of a matrix or MRexperiment object} \usage{ correlationTest( obj, y = NULL, method = "pearson", alternative = "two.sided", norm = TRUE, log = TRUE, cores = 1, override = FALSE, ... ) } \arguments{ \item{obj}{A MRexperiment object or count matrix.} \item{y}{Vector of length ncol(obj) to compare to.} \item{method}{One of 'pearson','spearman', or 'kendall'.} \item{alternative}{Indicates the alternative hypothesis and must be one of 'two.sided', 'greater' (positive) or 'less'(negative). You can specify just the initial letter.} \item{norm}{Whether to aggregate normalized counts or not - if MRexperiment object.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{cores}{Number of cores to use.} \item{override}{If the number of rows to test is over a thousand the test will not commence (unless override==TRUE).} \item{...}{Extra parameters for mclapply.} } \value{ A matrix of size choose(number of rows, 2) by 2. The first column corresponds to the correlation value. The second column the p-value. } \description{ Calculates the (pairwise) correlation statistics and associated p-values of a matrix or the correlation of each row with a vector. } \examples{ # Pairwise correlation of raw counts data(mouseData) cors = correlationTest(mouseData[1:10,],norm=FALSE,log=FALSE) head(cors) mat = MRcounts(mouseData)[1:10,] cormat = as.matrix(dist(mat)) # Creating a matrix cormat[cormat>0] = 0 # Creating an empty matrix ind = correctIndices(nrow(mat)) cormat[upper.tri(cormat)][ind] = cors[,1] table(cormat[1,-1] - cors[1:9,1]) # Correlation of raw counts with a vector (library size in this case) data(mouseData) cors = correlationTest(mouseData[1:10,],libSize(mouseData),norm=FALSE,log=FALSE) head(cors) } \seealso{ \code{\link{correctIndices}} } metagenomeSeq/man/cumNorm.Rd0000644000175200017520000000124314710220170017042 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cumNorm.R \name{cumNorm} \alias{cumNorm} \title{Cumulative sum scaling normalization} \usage{ cumNorm(obj, p = cumNormStatFast(obj)) } \arguments{ \item{obj}{An MRexperiment object.} \item{p}{The pth quantile.} } \value{ Object with the normalization factors stored as a vector of the sum up to and including a sample's pth quantile. } \description{ Calculates each column's quantile and calculates the sum up to and including that quantile. } \examples{ data(mouseData) mouseData <- cumNorm(mouseData) head(normFactors(mouseData)) } \seealso{ \code{\link{fitZig}} \code{\link{cumNormStat}} } metagenomeSeq/man/cumNormMat.Rd0000644000175200017520000000125514710220170017507 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cumNormMat.R \name{cumNormMat} \alias{cumNormMat} \title{Cumulative sum scaling factors.} \usage{ cumNormMat(obj, p = cumNormStatFast(obj), sl = 1000) } \arguments{ \item{obj}{A matrix or MRexperiment object.} \item{p}{The pth quantile.} \item{sl}{The value to scale by (default=1000).} } \value{ Returns a matrix normalized by scaling counts up to and including the pth quantile. } \description{ Calculates each column's quantile and calculates the sum up to and including that quantile. } \examples{ data(mouseData) head(cumNormMat(mouseData)) } \seealso{ \code{\link{fitZig}} \code{\link{cumNorm}} } metagenomeSeq/man/cumNormStat.Rd0000644000175200017520000000217114710220170017677 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cumNormStat.R \name{cumNormStat} \alias{cumNormStat} \title{Cumulative sum scaling percentile selection} \usage{ cumNormStat(obj, qFlag = TRUE, pFlag = FALSE, rel = 0.1, ...) } \arguments{ \item{obj}{A matrix or MRexperiment object.} \item{qFlag}{Flag to either calculate the proper percentile using R's step-wise quantile function or approximate function.} \item{pFlag}{Plot the relative difference of the median deviance from the reference.} \item{rel}{Cutoff for the relative difference from one median difference from the reference to the next} \item{...}{Applicable if pFlag == TRUE. Additional plotting parameters.} } \value{ Percentile for which to scale data } \description{ Calculates the percentile for which to sum counts up to and scale by. cumNormStat might be deprecated one day. Deviates from methods in Nature Methods paper by making use row means for generating reference. } \examples{ data(mouseData) p = round(cumNormStat(mouseData,pFlag=FALSE),digits=2) } \seealso{ \code{\link{fitZig}} \code{\link{cumNorm}} \code{\link{cumNormStatFast}} } metagenomeSeq/man/cumNormStatFast.Rd0000644000175200017520000000173514710220170020522 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cumNormStatFast.R \name{cumNormStatFast} \alias{cumNormStatFast} \title{Cumulative sum scaling percentile selection} \usage{ cumNormStatFast(obj, pFlag = FALSE, rel = 0.1, ...) } \arguments{ \item{obj}{A matrix or MRexperiment object.} \item{pFlag}{Plot the median difference quantiles.} \item{rel}{Cutoff for the relative difference from one median difference from the reference to the next.} \item{...}{Applicable if pFlag == TRUE. Additional plotting parameters.} } \value{ Percentile for which to scale data } \description{ Calculates the percentile for which to sum counts up to and scale by. Faster version than available in cumNormStat. Deviates from methods described in Nature Methods by making use of ro means for reference. } \examples{ data(mouseData) p = round(cumNormStatFast(mouseData,pFlag=FALSE),digits=2) } \seealso{ \code{\link{fitZig}} \code{\link{cumNorm}} \code{\link{cumNormStat}} } metagenomeSeq/man/doCountMStep.Rd0000644000175200017520000000303214710220170020004 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doCountMStep.R \name{doCountMStep} \alias{doCountMStep} \title{Compute the Maximization step calculation for features still active.} \usage{ doCountMStep(z, y, mmCount, stillActive, fit2 = NULL, dfMethod = "modified") } \arguments{ \item{z}{Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).} \item{y}{Matrix (m x n) of count observations.} \item{mmCount}{Model matrix for the count distribution.} \item{stillActive}{Boolean vector of size M, indicating whether a feature converged or not.} \item{fit2}{Previous fit of the count model.} \item{dfMethod}{Either 'default' or 'modified' (by responsibilities)} } \value{ Update matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0). } \description{ Maximization step is solved by weighted least squares. The function also computes counts residuals. } \details{ Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (s_j))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/doEStep.Rd0000644000175200017520000000233314710220170016766 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doEStep.R \name{doEStep} \alias{doEStep} \title{Compute the Expectation step.} \usage{ doEStep(countResiduals, zeroResiduals, zeroIndices) } \arguments{ \item{countResiduals}{Residuals from the count model.} \item{zeroResiduals}{Residuals from the zero model.} \item{zeroIndices}{Index (matrix m x n) of counts that are zero/non-zero.} } \value{ Updated matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0). } \description{ Estimates the responsibilities $z_ij = fracpi_j cdot I_0(y_ijpi_j cdot I_0(y_ij + (1-pi_j) cdot f_count(y_ij } \details{ Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j) cdot f_0(y_ij) +(1-pi_j (S_j))cdot f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/doZeroMStep.Rd0000644000175200017520000000272514710220170017643 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doZeroMStep.R \name{doZeroMStep} \alias{doZeroMStep} \title{Compute the zero Maximization step.} \usage{ doZeroMStep(z, zeroIndices, mmZero) } \arguments{ \item{z}{Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).} \item{zeroIndices}{Index (matrix m x n) of counts that are zero/non-zero.} \item{mmZero}{The zero model, the model matrix to account for the change in the number of OTUs observed as a linear effect of the depth of coverage.} } \value{ List of the zero fit (zero mean model) coefficients, variance - scale parameter (scalar), and normalized residuals of length sum(zeroIndices). } \description{ Performs Maximization step calculation for the mixture components. Uses least squares to fit the parameters of the mean of the logistic distribution. $$ pi_j = sum_i^M frac1Mz_ij $$ Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j) cdot f_0(y_ij) +(1-pi_j (S_j))cdot f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/expSummary.Rd0000644000175200017520000000122114710220170017570 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{methods} \name{expSummary} \alias{expSummary} \alias{expSummary,MRexperiment-method} \title{Access MRexperiment object experiment data} \usage{ expSummary(obj) } \arguments{ \item{obj}{a \code{MRexperiment} object.} } \value{ Experiment summary table } \description{ The expSummary vectors represent the column (sample specific) sums of features, i.e. the total number of reads for a sample, libSize and also the normalization factors, normFactor. } \examples{ data(mouseData) expSummary(mouseData) } \author{ Joseph N. Paulson, jpaulson@umiacs.umd.edu } metagenomeSeq/man/exportMat.Rd0000644000175200017520000000203714710220170017407 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/exportMat.R \name{exportMat} \alias{exportMat} \alias{exportMatrix} \title{Export the normalized MRexperiment dataset as a matrix.} \usage{ exportMat( obj, log = TRUE, norm = TRUE, sep = "\\t", file = "~/Desktop/matrix.tsv" ) } \arguments{ \item{obj}{A MRexperiment object or count matrix.} \item{log}{Whether or not to log transform the counts - if MRexperiment object.} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{sep}{Separator for writing out the count matrix.} \item{file}{Output file name.} } \value{ NA } \description{ This function allows the user to take a dataset of counts and output the dataset to the user's workspace as a tab-delimited file, etc. } \examples{ data(lungData) dataDirectory <- system.file("extdata", package="metagenomeSeq") exportMat(lungData[,1:5],file=file.path(dataDirectory,"tmp.tsv")) head(read.csv(file=file.path(dataDirectory,"tmp.tsv"),sep="\t")) } \seealso{ \code{\link{cumNorm}} } metagenomeSeq/man/exportStats.Rd0000644000175200017520000000167314710220170017771 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/exportStats.R \name{exportStats} \alias{exportStats} \title{Various statistics of the count data.} \usage{ exportStats(obj, p = cumNormStat(obj), file = "~/Desktop/res.stats.tsv") } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{p}{Quantile value to calculate the scaling factor and quantiles for the various samples.} \item{file}{Output file name.} } \value{ None. } \description{ A matrix of values for each sample. The matrix consists of sample ids, the sample scaling factor, quantile value, the number identified features, and library size (depth of coverage). } \examples{ data(lungData) dataDirectory <- system.file("extdata", package="metagenomeSeq") exportStats(lungData[,1:5],file=file.path(dataDirectory,"tmp.tsv")) head(read.csv(file=file.path(dataDirectory,"tmp.tsv"),sep="\t")) } \seealso{ \code{\link{cumNorm}} \code{\link{quantile}} } metagenomeSeq/man/extractMR.Rd0000644000175200017520000000126214710220170017334 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mergeMRexperiments.R \name{extractMR} \alias{extractMR} \title{Extract the essentials of an MRexperiment.} \usage{ extractMR(obj) } \arguments{ \item{obj}{MRexperiment-class object.} } \value{ \itemize{A list containing: \item counts : Count data \item librarySize : The column sums / library size / sequencing depth \item normFactors : The normalization scaling factors \item pheno : phenotype table \item feat : feature table } } \description{ Extract the essentials of an MRexperiment. } \examples{ data(mouseData) head(metagenomeSeq:::extractMR(mouseData)) } metagenomeSeq/man/filterData.Rd0000644000175200017520000000135214710220170017502 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/filterData.R \name{filterData} \alias{filterData} \title{Filter datasets according to no. features present in features with at least a certain depth.} \usage{ filterData(obj, present = 1, depth = 1000) } \arguments{ \item{obj}{A MRexperiment object or count matrix.} \item{present}{Features with at least 'present' postive samples.} \item{depth}{Sampls with at least this much depth of coverage} } \value{ A MRexperiment object. } \description{ Filter the data based on the number of present features after filtering samples by depth of coverage. There are many ways to filter the object, this is just one way. } \examples{ data(mouseData) filterData(mouseData) } metagenomeSeq/man/fitDO.Rd0000644000175200017520000000333414710220170016432 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitDO.R \name{fitDO} \alias{fitDO} \title{Wrapper to calculate Discovery Odds Ratios on feature values.} \usage{ fitDO(obj, cl, norm = TRUE, log = TRUE, adjust.method = "fdr", cores = 1, ...) } \arguments{ \item{obj}{A MRexperiment object with a count matrix, or a simple count matrix.} \item{cl}{Group comparison} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{adjust.method}{Method to adjust p-values by. Default is "FDR". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See \code{\link{p.adjust}} for more details.} \item{cores}{Number of cores to use.} \item{...}{Extra options for makeCluster} } \value{ Matrix of odds ratios, p-values, lower and upper confidence intervals } \description{ This function returns a data frame of p-values, odds ratios, lower and upper confidence limits for every row of a matrix. The discovery odds ratio is calculated as using Fisher's exact test on actual counts. The test's hypothesis is whether or not the discovery of counts for a feature (of all counts) is found in greater proportion in a particular group. } \examples{ data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] lungTrim = lungTrim[-which(rowSums(MRcounts(lungTrim)>0)<20),] res = fitDO(lungTrim,pData(lungTrim)$SmokingStatus); head(res) } \seealso{ \code{\link{cumNorm}} \code{\link{fitZig}} \code{\link{fitPA}} \code{\link{fitMeta}} } metagenomeSeq/man/fitFeatureModel.Rd0000644000175200017520000000310214710220170020475 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitFeatureModel.R \name{fitFeatureModel} \alias{fitFeatureModel} \title{Computes differential abundance analysis using a zero-inflated log-normal model} \usage{ fitFeatureModel(obj, mod, coef = 2, B = 1, szero = FALSE, spos = TRUE) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{mod}{The model for the count distribution.} \item{coef}{Coefficient of interest to grab log fold-changes.} \item{B}{Number of bootstraps to perform if >1. If >1 performs permutation test.} \item{szero}{TRUE/FALSE, shrink zero component parameters.} \item{spos}{TRUE/FALSE, shrink positive component parameters.} } \value{ A list of objects including: \itemize{ \item{call - the call made to fitFeatureModel} \item{fitZeroLogNormal - list of parameter estimates for the zero-inflated log normal model} \item{design - model matrix} \item{taxa - taxa names} \item{counts - count matrix} \item{pvalues - calculated p-values} \item{permuttedfits - permutted z-score estimates under the null} } } \description{ Wrapper to actually run zero-inflated log-normal model given a MRexperiment object and model matrix. User can decide to shrink parameter estimates. } \examples{ data(lungData) lungData = lungData[,-which(is.na(pData(lungData)$SmokingStatus))] lungData=filterData(lungData,present=30,depth=1) lungData <- cumNorm(lungData, p=.5) s <- normFactors(lungData) pd <- pData(lungData) mod <- model.matrix(~1+SmokingStatus, data=pd) lungres1 = fitFeatureModel(lungData,mod) } \seealso{ \code{\link{cumNorm}} } metagenomeSeq/man/fitFeatureModelResults-class.Rd0000644000175200017520000000154314710220170023171 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{class} \name{fitFeatureModelResults-class} \alias{fitFeatureModelResults-class} \title{Class "fitFeatureModelResults" -- a formal class for storing results from a fitFeatureModel call} \description{ This class contains all of the same information expected from a fitFeatureModel call, but it is defined in the S4 style as opposed to being stored as a list. } \section{Slots}{ \describe{ \item{\code{call}}{the call made to fitFeatureModel} \item{\code{fitZeroLogNormal}}{list of parameter estimates for the zero-inflated log normal model} \item{\code{design}}{model matrix} \item{\code{taxa}}{taxa names} \item{\code{counts}}{count matrix} \item{\code{pvalues}}{calculated p-values} \item{\code{permuttedFits}}{permutted z-score estimates under the null} }} metagenomeSeq/man/fitLogNormal.Rd0000644000175200017520000000257214710220170020025 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitLogNormal.R \name{fitLogNormal} \alias{fitLogNormal} \title{Computes a log-normal linear model and permutation based p-values.} \usage{ fitLogNormal(obj, mod, useCSSoffset = TRUE, B = 1000, coef = 2, sl = 1000) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{mod}{The model for the count distribution.} \item{useCSSoffset}{Boolean, whether to include the default scaling parameters in the model or not.} \item{B}{Number of permutations.} \item{coef}{The coefficient of interest.} \item{sl}{The value to scale by (default=1000).} } \value{ Call made, fit object from lmFit, t-statistics and p-values for each feature. } \description{ Wrapper to perform the permutation test on the t-statistic. This is the original method employed by metastats (for non-sparse large samples). We include CSS normalization though (optional) and log2 transform the data. In this method the null distribution is not assumed to be a t-dist. } \examples{ # This is a simple demonstration data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] k = which(rowSums(MRcounts(lungTrim)>0)<30) lungTrim = cumNorm(lungTrim) lungTrim = lungTrim[-k,] smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) fit = fitLogNormal(obj = lungTrim,mod=mod,B=1) } metagenomeSeq/man/fitMultipleTimeSeries.Rd0000644000175200017520000000335414710220170021717 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{fitMultipleTimeSeries} \alias{fitMultipleTimeSeries} \title{Discover differentially abundant time intervals for all bacteria} \usage{ fitMultipleTimeSeries(obj, lvl = NULL, B = 1, featureOrder = NULL, ...) } \arguments{ \item{obj}{metagenomeSeq MRexperiment-class object.} \item{lvl}{Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level).} \item{B}{Number of permutations to perform.} \item{featureOrder}{Hierarchy of levels in taxonomy as fData colnames} \item{...}{Options for \code{\link{fitTimeSeries}}, except feature.} } \value{ List of lists of matrices of time point intervals of interest, Difference in abundance area and p-value, fit, area permutations. A list of lists for which each includes: \itemize{ \item{timeIntervals - Matrix of time point intervals of interest, area of differential abundance, and pvalue.} \item{data - Data frame of abundance, class indicator, time, and id input.} \item{fit - Data frame of fitted values of the difference in abundance, standard error estimates and timepoints interpolated over.} \item{perm - Differential abundance area estimates for each permutation.} \item{call - Function call.} } } \description{ Calculate time intervals of significant differential abundance over all bacteria of a particularly specified level (lvl). If not lvl is specified, all OTUs are analyzed. Warning, function can take a while } \examples{ data(mouseData) res = fitMultipleTimeSeries(obj=mouseData,lvl='phylum',class="status", id="mouseID",time="relativeTime",B=1) } \seealso{ \code{\link{cumNorm}} \code{\link{fitSSTimeSeries}} \code{\link{fitTimeSeries}} } metagenomeSeq/man/fitPA.Rd0000644000175200017520000000240114710220170016422 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitPA.R \name{fitPA} \alias{fitPA} \title{Wrapper to run fisher's test on presence/absence of a feature.} \usage{ fitPA(obj, cl, thres = 0, adjust.method = "fdr", cores = 1, ...) } \arguments{ \item{obj}{A MRexperiment object with a count matrix, or a simple count matrix.} \item{cl}{Group comparison} \item{thres}{Threshold for defining presence/absence.} \item{adjust.method}{Method to adjust p-values by. Default is "FDR". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See \code{\link{p.adjust}} for more details.} \item{cores}{Number of cores to use.} \item{...}{Extra parameters for makeCluster} } \value{ Matrix of odds ratios, p-values, lower and upper confidence intervals } \description{ This function returns a data frame of p-values, odds ratios, lower and upper confidence limits for every row of a matrix. } \examples{ data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] lungTrim = lungTrim[-which(rowSums(MRcounts(lungTrim)>0)<20),] res = fitPA(lungTrim,pData(lungTrim)$SmokingStatus); head(res) } \seealso{ \code{\link{cumNorm}} \code{\link{fitZig}} \code{\link{fitDO}} \code{\link{fitMeta}} } metagenomeSeq/man/fitSSTimeSeries.Rd0000644000175200017520000000601714710220170020450 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{fitSSTimeSeries} \alias{fitSSTimeSeries} \title{Discover differentially abundant time intervals using SS-Anova} \usage{ fitSSTimeSeries( obj, formula, feature, class, time, id, lvl = NULL, include = c("class", "time:class"), C = 0, B = 1000, norm = TRUE, log = TRUE, sl = 1000, featureOrder = NULL, ... ) } \arguments{ \item{obj}{metagenomeSeq MRexperiment-class object.} \item{formula}{Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value.} \item{feature}{Name or row of feature of interest.} \item{class}{Name of column in phenoData of MRexperiment-class object for class memberhip.} \item{time}{Name of column in phenoData of MRexperiment-class object for relative time.} \item{id}{Name of column in phenoData of MRexperiment-class object for sample id.} \item{lvl}{Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level).} \item{include}{Parameters to include in prediction.} \item{C}{Value for which difference function has to be larger or smaller than (default 0).} \item{B}{Number of permutations to perform} \item{norm}{When aggregating counts to normalize or not.} \item{log}{Log2 transform.} \item{sl}{Scaling value.} \item{featureOrder}{Hierarchy of levels in taxonomy as fData colnames} \item{...}{Options for ssanova} } \value{ List of matrix of time point intervals of interest, Difference in abundance area and p-value, fit, area permutations, and call. A list of objects including: \itemize{ \item{timeIntervals - Matrix of time point intervals of interest, area of differential abundance, and pvalue.} \item{data - Data frame of abundance, class indicator, time, and id input.} \item{fit - Data frame of fitted values of the difference in abundance, standard error estimates and timepoints interpolated over.} \item{perm - Differential abundance area estimates for each permutation.} \item{call - Function call.} } } \description{ Calculate time intervals of interest using SS-Anova fitted models. Fitting is performed uses Smoothing Spline ANOVA (SS-Anova) to find interesting intervals of time. Given observations at different time points for two groups, fitSSTimeSeries calculates a function that models the difference in abundance between two groups across all time. Using permutations we estimate a null distribution of areas for the time intervals of interest and report significant intervals of time. Use of the function for analyses should cite: "Finding regions of interest in high throughput genomics data using smoothing splines" Talukder H, Paulson JN, Bravo HC. (In preparation) } \examples{ data(mouseData) res = fitSSTimeSeries(obj=mouseData,feature="Actinobacteria", class="status",id="mouseID",time="relativeTime",lvl='class',B=2) } \seealso{ \code{\link{cumNorm}} \code{\link{ssFit}} \code{\link{ssIntervalCandidate}} \code{\link{ssPerm}} \code{\link{ssPermAnalysis}} \code{\link{plotTimeSeries}} } metagenomeSeq/man/fitTimeSeries.Rd0000644000175200017520000000512614710220170020202 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{fitTimeSeries} \alias{fitTimeSeries} \title{Discover differentially abundant time intervals} \usage{ fitTimeSeries( obj, formula, feature, class, time, id, method = c("ssanova"), lvl = NULL, include = c("class", "time:class"), C = 0, B = 1000, norm = TRUE, log = TRUE, sl = 1000, featureOrder = NULL, ... ) } \arguments{ \item{obj}{metagenomeSeq MRexperiment-class object.} \item{formula}{Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value.} \item{feature}{Name or row of feature of interest.} \item{class}{Name of column in phenoData of MRexperiment-class object for class memberhip.} \item{time}{Name of column in phenoData of MRexperiment-class object for relative time.} \item{id}{Name of column in phenoData of MRexperiment-class object for sample id.} \item{method}{Method to estimate time intervals of differentially abundant bacteria (only ssanova method implemented currently).} \item{lvl}{Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level).} \item{include}{Parameters to include in prediction.} \item{C}{Value for which difference function has to be larger or smaller than (default 0).} \item{B}{Number of permutations to perform.} \item{norm}{When aggregating counts to normalize or not.} \item{log}{Log2 transform.} \item{sl}{Scaling value.} \item{featureOrder}{Hierarchy of levels in taxonomy as fData colnames} \item{...}{Options for ssanova} } \value{ List of matrix of time point intervals of interest, Difference in abundance area and p-value, fit, area permutations, and call. A list of objects including: \itemize{ \item{timeIntervals - Matrix of time point intervals of interest, area of differential abundance, and pvalue.} \item{data - Data frame of abundance, class indicator, time, and id input.} \item{fit - Data frame of fitted values of the difference in abundance, standard error estimates and timepoints interpolated over.} \item{perm - Differential abundance area estimates for each permutation.} \item{call - Function call.} } } \description{ Calculate time intervals of significant differential abundance. Currently only one method is implemented (ssanova). fitSSTimeSeries is called with method="ssanova". } \examples{ data(mouseData) res = fitTimeSeries(obj=mouseData,feature="Actinobacteria", class="status",id="mouseID",time="relativeTime",lvl='class',B=2) } \seealso{ \code{\link{cumNorm}} \code{\link{fitSSTimeSeries}} \code{\link{plotTimeSeries}} } metagenomeSeq/man/fitZeroLogNormal.Rd0000644000175200017520000000275214710220170020665 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitZeroLogNormal.R \name{fitZeroLogNormal} \alias{fitZeroLogNormal} \title{Compute the log fold-change estimates for the zero-inflated log-normal model} \usage{ fitZeroLogNormal(obj, mod, coef = 2, szero = TRUE, spos = TRUE) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{mod}{The model for the count distribution.} \item{coef}{Coefficient of interest to grab log fold-changes.} \item{szero}{TRUE/FALSE, shrink zero component parameters.} \item{spos}{TRUE/FALSE, shrink positive component parameters.} } \value{ A list of objects including: \itemize{ \item{logFC - the log fold-change estimates} \item{adjFactor - the adjustment factor based on the zero component} \item{se - standard error estimates} \item{fitln - parameters from the log-normal fit} \item{fitzero - parameters from the logistic fit} \item{zeroRidge - output from the ridge regression} \item{posRidge - output from the ridge regression} \item{tauPos - estimated tau^2 for positive component} \item{tauZero - estimated tau^2 for zero component} \item{exclude - features to exclude for various reasons, e.g. all zeros} \item{zeroExclude - features to exclude for various reasons, e.g. all zeros} } } \description{ Run the zero-inflated log-normal model given a MRexperiment object and model matrix. Not for the average user, assumes structure of the model matrix. } \seealso{ \code{\link{cumNorm}} \code{\link{fitFeatureModel}} } metagenomeSeq/man/fitZig.Rd0000644000175200017520000000524714710220170016666 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitZig.R \name{fitZig} \alias{fitZig} \title{Computes the weighted fold-change estimates and t-statistics.} \usage{ fitZig( obj, mod, zeroMod = NULL, useCSSoffset = TRUE, control = zigControl(), useMixedModel = FALSE, ... ) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{mod}{The model for the count distribution.} \item{zeroMod}{The zero model, the model to account for the change in the number of OTUs observed as a linear effect of the depth of coverage.} \item{useCSSoffset}{Boolean, whether to include the default scaling parameters in the model or not.} \item{control}{The settings for fitZig.} \item{useMixedModel}{Estimate the correlation between duplicate features or replicates using duplicateCorrelation.} \item{...}{Additional parameters for duplicateCorrelation.} } \value{ A list of objects including: \itemize{ \item{call - the call made to fitZig} \item{fit - 'MLArrayLM' Limma object of the weighted fit} \item{countResiduals - standardized residuals of the fit} \item{z - matrix of the posterior probabilities} \item{eb - output of eBayes, moderated t-statistics, moderated F-statistics, etc} \item{taxa - vector of the taxa names} \item{counts - the original count matrix input} \item{zeroMod - the zero model matrix} \item{zeroCoef - the zero model fitted results} \item{stillActive - convergence} \item{stillActiveNLL - nll at convergence} \item{dupcor - correlation of duplicates} } } \description{ Wrapper to actually run the Expectation-maximization algorithm and estimate $f_count$ fits. Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij = 1$ if $y_ij$ is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * f_count(y_ij; mu_i, sigma_i^2)$. The log-likelihood in this extended model is: $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij) log (1-pi_j (s_j))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. } \examples{ # This is a simple demonstration data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] k = which(rowSums(MRcounts(lungTrim)>0)<30) lungTrim = cumNorm(lungTrim) lungTrim = lungTrim[-k,] smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) # The maxit is not meant to be 1 - this is for demonstration/speed settings = zigControl(maxit=1,verbose=FALSE) fit = fitZig(obj = lungTrim,mod=mod,control=settings) } \seealso{ \code{\link{cumNorm}} \code{\link{zigControl}} } metagenomeSeq/man/fitZigResults-class.Rd0000644000175200017520000000226614710220170021351 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{class} \name{fitZigResults-class} \alias{fitZigResults-class} \title{Class "fitZigResults" -- a formal class for storing results from a fitZig call} \description{ This class contains all of the same information expected from a fitZig call, but it is defined in the S4 style as opposed to being stored as a list. } \section{Slots}{ \describe{ \item{\code{call}}{the call made to fitZig} \item{\code{fit}}{'MLArrayLM' Limma object of the weighted fit} \item{\code{countResiduals}}{standardized residuals of the fit} \item{\code{z}}{matrix of the posterior probabilities. It is defined as $z_ij = pr(delta_ij=1 | data)$} \item{\code{zUsed}}{used in \code{\link{getZ}}} \item{\code{eb}}{output of eBayes, moderated t-statistics, moderated F-statistics, etc} \item{\code{taxa}}{vector of the taxa names} \item{\code{counts}}{the original count matrix input} \item{\code{zeroMod}}{the zero model matrix} \item{\code{zeroCoef}}{the zero model fitted results} \item{\code{stillActive}}{convergence} \item{\code{stillActiveNLL}}{nll at convergence} \item{\code{dupcor}}{correlation of duplicates} }} metagenomeSeq/man/getCountDensity.Rd0000644000175200017520000000220714710220170020553 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/getCountDensity.R \name{getCountDensity} \alias{getCountDensity} \title{Compute the value of the count density function from the count model residuals.} \usage{ getCountDensity(residuals, log = FALSE) } \arguments{ \item{residuals}{Residuals from the count model.} \item{log}{Whether or not we are calculating from a log-normal distribution.} } \value{ Density values from the count model residuals. } \description{ Calculate density values from a normal: $f(x) = 1/(sqrt (2 pi ) sigma ) e^-((x - mu )^2/(2 sigma^2))$. Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $deta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j) cdot f_0(y_ij) +(1-pi_j (S_j))cdot f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/getEpsilon.Rd0000644000175200017520000000174014710220170017535 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/getEpsilon.R \name{getEpsilon} \alias{getEpsilon} \title{Calculate the relative difference between iterations of the negative log-likelihoods.} \usage{ getEpsilon(nll, nllOld) } \arguments{ \item{nll}{Vector of size M with the current negative log-likelihoods.} \item{nllOld}{Vector of size M with the previous iterations negative log-likelihoods.} } \value{ Vector of size M of the relative differences between the previous and current iteration nll. } \description{ Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables. The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/getNegativeLogLikelihoods.Rd0000644000175200017520000000217014710220170022515 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/getNegativeLogLikelihoods.R \name{getNegativeLogLikelihoods} \alias{getNegativeLogLikelihoods} \title{Calculate the negative log-likelihoods for the various features given the residuals.} \usage{ getNegativeLogLikelihoods(z, countResiduals, zeroResiduals) } \arguments{ \item{z}{Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).} \item{countResiduals}{Residuals from the count model.} \item{zeroResiduals}{Residuals from the zero model.} } \value{ Vector of size M of the negative log-likelihoods for the various features. } \description{ Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables. The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data and current values)$. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/getPi.Rd0000644000175200017520000000130414710220170016470 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/getPi.R \name{getPi} \alias{getPi} \title{Calculate the mixture proportions from the zero model / spike mass model residuals.} \usage{ getPi(residuals) } \arguments{ \item{residuals}{Residuals from the zero model.} } \value{ Mixture proportions for each sample. } \description{ F(x) = 1 / (1 + exp(-(x-m)/s)) (the CDF of the logistic distribution). Provides the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. The output are the mixture proportions for the samples given the residuals from the zero model. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/getZ.Rd0000644000175200017520000000171314710220170016335 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/getZ.R \name{getZ} \alias{getZ} \title{Calculate the current Z estimate responsibilities (posterior probabilities)} \usage{ getZ(z, zUsed, stillActive, nll, nllUSED) } \arguments{ \item{z}{Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).} \item{zUsed}{Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0) that are actually used (following convergence).} \item{stillActive}{A vector of size M booleans saying if a feature is still active or not.} \item{nll}{Vector of size M with the current negative log-likelihoods.} \item{nllUSED}{Vector of size M with the converged negative log-likelihoods.} } \value{ A list of updated zUsed and nllUSED. } \description{ Calculate the current Z estimate responsibilities (posterior probabilities) } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/isItStillActive.Rd0000644000175200017520000000204614710220170020500 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/isItStillActive.R \name{isItStillActive} \alias{isItStillActive} \title{Function to determine if a feature is still active.} \usage{ isItStillActive(eps, tol, stillActive, stillActiveNLL, nll) } \arguments{ \item{eps}{Vector of size M (features) representing the relative difference between the new nll and old nll.} \item{tol}{The threshold tolerance for the difference} \item{stillActive}{A vector of size M booleans saying if a feature is still active or not.} \item{stillActiveNLL}{A vector of size M recording the negative log-likelihoods of the various features, updated for those still active.} \item{nll}{Vector of size M with the current negative log-likelihoods.} } \value{ None. } \description{ In the Expectation Maximization routine features posterior probabilities routinely converge based on a tolerance threshold. This function checks whether or not the feature's negative log-likelihood (measure of the fit) has changed or not. } \seealso{ \code{\link{fitZig}} } metagenomeSeq/man/libSize-set.Rd0000644000175200017520000000122014710220170017607 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{methods} \name{libSize<-} \alias{libSize<-} \alias{libSize<-,MRexperiment,numeric-method} \title{Replace the library sizes in a MRexperiment object} \usage{ \S4method{libSize}{MRexperiment,numeric}(object) <- value } \arguments{ \item{object}{a \code{MRexperiment} object} \item{value}{vector of library sizes} } \value{ vector library sizes } \description{ Function to replace the scaling factors, aka the library sizes, of samples in a MRexperiment object. } \examples{ data(lungData) head(libSize(lungData)<- rnorm(1)) } \author{ Joseph N. Paulson } metagenomeSeq/man/libSize.Rd0000644000175200017520000000112614710220170017023 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{methods} \name{libSize} \alias{libSize} \title{Access sample depth of coverage from MRexperiment object} \usage{ libSize(object) } \arguments{ \item{object}{a \code{MRexperiment} object} } \value{ Library sizes } \description{ Access the libSize vector represents the column (sample specific) sums of features, i.e. the total number of reads for a sample or depth of coverage. It is used by \code{\link{fitZig}}. } \examples{ data(lungData) head(libSize(lungData)) } \author{ Joseph N. Paulson } metagenomeSeq/man/loadBiom.Rd0000644000175200017520000000116114710220170017147 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/loadBiom.R \name{loadBiom} \alias{loadBiom} \title{Load objects organized in the Biom format.} \usage{ loadBiom(file) } \arguments{ \item{file}{The biom object filepath.} } \value{ A MRexperiment object. } \description{ Wrapper to load Biom formatted object. } \examples{ #library(biomformat) rich_dense_file = system.file("extdata", "rich_dense_otu_table.biom", package = "biomformat") x = loadBiom(rich_dense_file) x } \seealso{ \code{\link{loadMeta}} \code{\link{loadPhenoData}} \code{\link{newMRexperiment}} \code{\link{biom2MRexperiment}} } metagenomeSeq/man/loadMeta.Rd0000644000175200017520000000120214710220170017143 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/loadMeta.R \name{loadMeta} \alias{loadMeta} \alias{metagenomicLoader} \title{Load a count dataset associated with a study.} \usage{ loadMeta(file, sep = "\\t") } \arguments{ \item{file}{Path and filename of the actual data file.} \item{sep}{File delimiter.} } \value{ A list with objects 'counts' and 'taxa'. } \description{ Load a matrix of OTUs in a tab delimited format } \examples{ dataDirectory <- system.file("extdata", package="metagenomeSeq") lung = loadMeta(file.path(dataDirectory,"CHK_NAME.otus.count.csv")) } \seealso{ \code{\link{loadPhenoData}} } metagenomeSeq/man/loadMetaQ.Rd0000644000175200017520000000110214710220170017263 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/loadMetaQ.R \name{loadMetaQ} \alias{loadMetaQ} \alias{qiimeLoader} \title{Load a count dataset associated with a study set up in a Qiime format.} \usage{ loadMetaQ(file) } \arguments{ \item{file}{Path and filename of the actual data file.} } \value{ An list with 'counts' containing the count data, 'taxa' containing the otu annotation, and 'otus'. } \description{ Load a matrix of OTUs in Qiime's format } \examples{ # see vignette } \seealso{ \code{\link{loadMeta}} \code{\link{loadPhenoData}} } metagenomeSeq/man/loadPhenoData.Rd0000644000175200017520000000145314710220170020130 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/loadPhenoData.R \name{loadPhenoData} \alias{loadPhenoData} \alias{phenoData} \title{Load a clinical/phenotypic dataset associated with a study.} \usage{ loadPhenoData(file, tran = TRUE, sep = "\\t") } \arguments{ \item{file}{Path and filename of the actual clinical file.} \item{tran}{Boolean. If the covariates are along the columns and samples along the rows, then tran should equal TRUE.} \item{sep}{The separator for the file.} } \value{ The metadata as a dataframe. } \description{ Load a matrix of metadata associated with a study. } \examples{ dataDirectory <- system.file("extdata", package="metagenomeSeq") clin = loadPhenoData(file.path(dataDirectory,"CHK_clinical.csv"),tran=TRUE) } \seealso{ \code{\link{loadMeta}} } metagenomeSeq/man/lungData.Rd0000644000175200017520000000074014710220170017162 0ustar00biocbuildbiocbuild\name{lungData} \docType{data} \alias{lungData} \title{OTU abundance matrix of samples from a smoker/non-smoker study} \description{This is a list with a matrix of OTU counts,otu names, taxa annotations for each OTU, and phenotypic data. Samples along the columns and OTUs along the rows.} \value{ MRexperiment-class object of 16S lung samples. } %\usage{lungData} \format{A list of OTU matrix, taxa, otus, and phenotypes} \references{http://www.ncbi.nlm.nih.gov/pubmed/21680950}metagenomeSeq/man/makeLabels.Rd0000644000175200017520000000102414710220170017457 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/misc.R \name{makeLabels} \alias{makeLabels} \title{Function to make labels simpler} \usage{ makeLabels(x = "samples", y = "abundance", norm, log) } \arguments{ \item{x}{string for the x-axis} \item{y}{string for the y-axis} \item{norm}{is the data normalized?} \item{log}{is the data logged?} } \value{ vector of x,y labels } \description{ Beginning to transition to better axes for plots } \examples{ metagenomeSeq::makeLabels(norm=TRUE,log=TRUE) } metagenomeSeq/man/mergeMRexperiments.Rd0000644000175200017520000000156014710220170021246 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mergeMRexperiments.R \name{mergeMRexperiments} \alias{mergeMRexperiments} \title{Merge two MRexperiment objects together} \usage{ mergeMRexperiments(x, y) } \arguments{ \item{x}{MRexperiment-class object 1.} \item{y}{MRexperiment-class object 2.} } \value{ Merged MRexperiment-class object. } \description{ This function will take two MRexperiment objects and merge them together finding common OTUs. If there are OTUs not found in one of the two MRexperiments then a message will announce this and values will be coerced to zero for the second table. } \examples{ data(mouseData) newobj = mergeMRexperiments(mouseData,mouseData) newobj # let me know if people are interested in an option to merge by keys instead of row names. data(lungData) newobj = mergeMRexperiments(mouseData,lungData) newobj } metagenomeSeq/man/mergeTable.Rd0000644000175200017520000000045114710220170017471 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mergeMRexperiments.R \name{mergeTable} \alias{mergeTable} \title{Merge two tables} \usage{ mergeTable(x, y) } \arguments{ \item{x}{Table 1.} \item{y}{Table 2.} } \value{ Merged table } \description{ Merge two tables } metagenomeSeq/man/metagenomeSeq-deprecated.Rd0000644000175200017520000000157514710220170022322 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated_metagenomeSeq_function.R \docType{package} \name{metagenomeSeq-deprecated} \alias{metagenomeSeq-deprecated} \alias{deprecated_metagenomeSeq_function} \alias{fitMeta} \alias{load_phenoData} \alias{load_meta} \alias{load_biom} \alias{load_metaQ} \title{Depcrecated functions in the metagenomeSeq package.} \usage{ deprecated_metagenomeSeq_function(x, value, ...) } \arguments{ \item{x}{For assignment operators, the object that will undergo a replacement (object inside parenthesis).} \item{value}{For assignment operators, the value to replace with (the right side of the assignment).} \item{...}{For functions other than assignment operators, parameters to be passed to the modern version of the function (see table).} } \description{ These functions may be removed completely in the next release. } metagenomeSeq/man/metagenomeSeq-package.Rd0000644000175200017520000000201414710220170021602 0ustar00biocbuildbiocbuild\name{metagenomeSeq-package} \docType{package} \alias{metagenomeSeq} \alias{metagenomeSeq-package} \title{Statistical analysis for sparse high-throughput sequencing} \description{ metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. A user's guide is available, and can be opened by typing \code{vignette("metagenomeSeq")} The metagenomeSeq package implements novel normalization and statistical methodology in the following papers. } \author{ Paulson, JN ; Pop, M; Corrada Bravo, H } \references{ Paulson, Joseph N., O. Colin Stine, Hector Corrada Bravo, and Mihai Pop. "Differential abundance analysis for microbial marker-gene surveys." Nature methods (2013). } \keyword{package} metagenomeSeq/man/mouseData.Rd0000644000175200017520000000077514710220170017355 0ustar00biocbuildbiocbuild\name{mouseData} \docType{data} \alias{mouseData} \title{OTU abundance matrix of mice samples from a diet longitudinal study} \description{This is a list with a matrix of OTU counts, taxa annotations for each OTU, otu names, and vector of phenotypic data. Samples along the columns and OTUs along the rows.} \value{ MRexperiment-class object of 16S mouse samples. } %\usage{mouseData} \format{A list of OTU matrix, taxa, otus, and phenotypes} \references{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894525/}metagenomeSeq/man/newMRexperiment.Rd0000644000175200017520000000245014710220170020554 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \name{newMRexperiment} \alias{newMRexperiment} \title{Create a MRexperiment object} \usage{ newMRexperiment( counts, phenoData = NULL, featureData = NULL, libSize = NULL, normFactors = NULL ) } \arguments{ \item{counts}{A matrix or data frame of count data. The count data is representative of the number of reads annotated for a feature (be it gene, OTU, species, etc). Rows should correspond to features and columns to samples.} \item{phenoData}{An AnnotatedDataFrame with pertinent sample information.} \item{featureData}{An AnnotatedDataFrame with pertinent feature information.} \item{libSize}{libSize, library size, is the total number of reads for a particular sample.} \item{normFactors}{normFactors, the normalization factors used in either the model or as scaling factors of sample counts for each particular sample.} } \value{ an object of class MRexperiment } \description{ This function creates a MRexperiment object from a matrix or data frame of count data. } \details{ See \code{\link{MRexperiment-class}} and \code{eSet} (from the Biobase package) for the meaning of the various slots. } \examples{ cnts = matrix(abs(rnorm(1000)),nc=10) obj <- newMRexperiment(cnts) } \author{ Joseph N Paulson } metagenomeSeq/man/normFactors-set.Rd0000644000175200017520000000131514710220170020510 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{methods} \name{normFactors<-} \alias{normFactors<-} \alias{normFactors<-,MRexperiment,numeric-method} \title{Replace the normalization factors in a MRexperiment object} \usage{ \S4method{normFactors}{MRexperiment,numeric}(object) <- value } \arguments{ \item{object}{a \code{MRexperiment} object} \item{value}{vector of normalization scaling factors} } \value{ Normalization scaling factors } \description{ Function to replace the scaling factors, aka the normalization factors, of samples in a MRexperiment object. } \examples{ data(lungData) head(normFactors(lungData)<- rnorm(1)) } \author{ Joseph N. Paulson } metagenomeSeq/man/normFactors.Rd0000644000175200017520000000105114710220170017714 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{methods} \name{normFactors} \alias{normFactors} \title{Access the normalization factors in a MRexperiment object} \usage{ normFactors(object) } \arguments{ \item{object}{a \code{MRexperiment} object} } \value{ Normalization scaling factors } \description{ Function to access the scaling factors, aka the normalization factors, of samples in a MRexperiment object. } \examples{ data(lungData) head(normFactors(lungData)) } \author{ Joseph N. Paulson } metagenomeSeq/man/plotBubble.Rd0000644000175200017520000000322314710220170017514 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotBubble.R \name{plotBubble} \alias{plotBubble} \title{Basic plot of binned vectors.} \usage{ plotBubble( yvector, xvector, sigvector = NULL, nbreaks = 10, ybreak = quantile(yvector, p = seq(0, 1, length.out = nbreaks)), xbreak = quantile(xvector, p = seq(0, 1, length.out = nbreaks)), scale = 1, local = FALSE, ... ) } \arguments{ \item{yvector}{A vector of values represented along y-axis.} \item{xvector}{A vector of values represented along x-axis.} \item{sigvector}{A vector of the names of significant features (names should match x/yvector).} \item{nbreaks}{Number of bins to break yvector and xvector into.} \item{ybreak}{The values to break the yvector at.} \item{xbreak}{The values to break the xvector at.} \item{scale}{Scaling of circle bin sizes.} \item{local}{Boolean to shade by signficant bin numbers (TRUE) or overall proportion (FALSE).} \item{...}{Additional plot arguments.} } \value{ A matrix of features along rows, and the group membership along columns. } \description{ This function plots takes two vectors, calculates the contingency table and plots circles sized by the contingency table value. Optional significance vectors of the values significant will shade the circles by proportion of significance. } \examples{ data(mouseData) mouseData = mouseData[which(rowSums(mouseData)>139),] sparsity = rowMeans(MRcounts(mouseData)==0) lor = log(fitPA(mouseData,cl=pData(mouseData)[,3])$oddsRatio) plotBubble(lor,sparsity,main="lor ~ sparsity") # Example 2 x = runif(100000) y = runif(100000) plotBubble(y,x) } \seealso{ \code{\link{plotMRheatmap}} } metagenomeSeq/man/plotClassTimeSeries.Rd0000644000175200017520000000232514710220170021362 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{plotClassTimeSeries} \alias{plotClassTimeSeries} \title{Plot abundances by class} \usage{ plotClassTimeSeries( res, formula, xlab = "Time", ylab = "Abundance", color0 = "black", color1 = "red", include = c("1", "class", "time:class"), ... ) } \arguments{ \item{res}{Output of fitTimeSeries function} \item{formula}{Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value.} \item{xlab}{X-label.} \item{ylab}{Y-label.} \item{color0}{Color of samples from first group.} \item{color1}{Color of samples from second group.} \item{include}{Parameters to include in prediction.} \item{...}{Extra plotting arguments.} } \value{ Plot for abundances of each class using a spline approach on estimated null model. } \description{ Plot the abundance of values for each class using a spline approach on the estimated full model. } \examples{ data(mouseData) res = fitTimeSeries(obj=mouseData,feature="Actinobacteria", class="status",id="mouseID",time="relativeTime",lvl='class',B=10) plotClassTimeSeries(res,pch=21,bg=res$data$class,ylim=c(0,8)) } \seealso{ \code{\link{fitTimeSeries}} } metagenomeSeq/man/plotCorr.Rd0000644000175200017520000000206614710220170017232 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotCorr.R \name{plotCorr} \alias{plotCorr} \title{Basic correlation plot function for normalized or unnormalized counts.} \usage{ plotCorr(obj, n, norm = TRUE, log = TRUE, fun = cor, ...) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{n}{The number of features to plot. This chooses the "n" features with greatest variance.} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{fun}{Function to calculate pair-wise relationships. Default is pearson correlation} \item{...}{Additional plot arguments.} } \value{ plotted correlation matrix } \description{ This function plots a heatmap of the "n" features with greatest variance across rows. } \examples{ data(mouseData) plotCorr(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none",dendrogram="none", col = colorRampPalette(brewer.pal(9, "RdBu"))(50)) } \seealso{ \code{\link{cumNormMat}} } metagenomeSeq/man/plotFeature.Rd0000644000175200017520000000266214710220170017722 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotFeature.R \name{plotFeature} \alias{plotFeature} \title{Basic plot function of the raw or normalized data.} \usage{ plotFeature( obj, otuIndex, classIndex, col = "black", sort = TRUE, sortby = NULL, norm = TRUE, log = TRUE, sl = 1000, ... ) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{otuIndex}{The row to plot} \item{classIndex}{A list of the samples in their respective groups.} \item{col}{A vector to color samples by.} \item{sort}{Boolean, sort or not.} \item{sortby}{Default is sort by library size, alternative vector for sorting} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{sl}{Scaling factor - if MRexperiment and norm=TRUE.} \item{...}{Additional plot arguments.} } \value{ counts and classindex } \description{ This function plots the abundance of a particular OTU by class. The function is the typical manhattan plot of the abundances. } \examples{ data(mouseData) classIndex=list(Western=which(pData(mouseData)$diet=="Western")) classIndex$BK=which(pData(mouseData)$diet=="BK") otuIndex = 8770 par(mfrow=c(2,1)) dates = pData(mouseData)$date plotFeature(mouseData,norm=FALSE,log=FALSE,otuIndex,classIndex, col=dates,sortby=dates,ylab="Raw reads") } \seealso{ \code{\link{cumNorm}} } metagenomeSeq/man/plotGenus.Rd0000644000175200017520000000334514710220170017407 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotGenus.R \name{plotGenus} \alias{plotGenus} \alias{genusPlot} \title{Basic plot function of the raw or normalized data.} \usage{ plotGenus( obj, otuIndex, classIndex, norm = TRUE, log = TRUE, no = 1:length(otuIndex), labs = TRUE, xlab = NULL, ylab = NULL, jitter = TRUE, jitter.factor = 1, pch = 21, ... ) } \arguments{ \item{obj}{An MRexperiment object with count data.} \item{otuIndex}{A list of the otus with the same annotation.} \item{classIndex}{A list of the samples in their respective groups.} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{no}{Which of the otuIndex to plot.} \item{labs}{Whether to include group labels or not. (TRUE/FALSE)} \item{xlab}{xlabel for the plot.} \item{ylab}{ylabel for the plot.} \item{jitter}{Boolean to jitter the count data or not.} \item{jitter.factor}{Factor value for jitter} \item{pch}{Standard pch value for the plot command.} \item{...}{Additional plot arguments.} } \value{ plotted data } \description{ This function plots the abundance of a particular OTU by class. The function uses the estimated posterior probabilities to make technical zeros transparent. } \examples{ data(mouseData) classIndex=list(controls=which(pData(mouseData)$diet=="BK")) classIndex$cases=which(pData(mouseData)$diet=="Western") otuIndex = grep("Strep",fData(mouseData)$family) otuIndex=otuIndex[order(rowSums(MRcounts(mouseData)[otuIndex,]),decreasing=TRUE)] plotGenus(mouseData,otuIndex,classIndex,no=1:2,xaxt="n",norm=FALSE,ylab="Strep normalized log(cpt)") } \seealso{ \code{\link{cumNorm}} } metagenomeSeq/man/plotMRheatmap.Rd0000644000175200017520000000256214710220170020204 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotMRheatmap.R \name{plotMRheatmap} \alias{plotMRheatmap} \title{Basic heatmap plot function for normalized counts.} \usage{ plotMRheatmap(obj, n, norm = TRUE, log = TRUE, fun = sd, ...) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{n}{The number of features to plot. This chooses the 'n' features of greatest positive statistic.} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{fun}{Function to select top 'n' features.} \item{...}{Additional plot arguments.} } \value{ plotted matrix } \description{ This function plots a heatmap of the 'n' features with greatest variance across rows (or other statistic). } \examples{ data(mouseData) trials = pData(mouseData)$diet heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))]; heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50) #### version using sd plotMRheatmap(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none", col = heatmapCols,ColSideColors = heatmapColColors) #### version using MAD plotMRheatmap(obj=mouseData,n=50,fun=mad,cexRow = 0.4,cexCol = 0.4,trace="none", col = heatmapCols,ColSideColors = heatmapColColors) } \seealso{ \code{\link{cumNormMat}} } metagenomeSeq/man/plotOTU.Rd0000644000175200017520000000302614710220170016771 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotOTU.R \name{plotOTU} \alias{plotOTU} \title{Basic plot function of the raw or normalized data.} \usage{ plotOTU( obj, otu, classIndex, log = TRUE, norm = TRUE, jitter.factor = 1, pch = 21, labs = TRUE, xlab = NULL, ylab = NULL, jitter = TRUE, ... ) } \arguments{ \item{obj}{A MRexperiment object with count data.} \item{otu}{The row number/OTU to plot.} \item{classIndex}{A list of the samples in their respective groups.} \item{log}{Whether or not to log2 transform the counts - if MRexperiment object.} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{jitter.factor}{Factor value for jitter.} \item{pch}{Standard pch value for the plot command.} \item{labs}{Whether to include group labels or not. (TRUE/FALSE)} \item{xlab}{xlabel for the plot.} \item{ylab}{ylabel for the plot.} \item{jitter}{Boolean to jitter the count data or not.} \item{...}{Additional plot arguments.} } \value{ Plotted values } \description{ This function plots the abundance of a particular OTU by class. The function uses the estimated posterior probabilities to make technical zeros transparent. } \examples{ data(mouseData) classIndex=list(controls=which(pData(mouseData)$diet=="BK")) classIndex$cases=which(pData(mouseData)$diet=="Western") # you can specify whether or not to normalize, and to what level plotOTU(mouseData,otu=9083,classIndex,norm=FALSE,main="9083 feature abundances") } \seealso{ \code{\link{cumNorm}} } metagenomeSeq/man/plotOrd.Rd0000644000175200017520000000265414710220170017054 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotOrd.R \name{plotOrd} \alias{plotOrd} \title{Plot of either PCA or MDS coordinates for the distances of normalized or unnormalized counts.} \usage{ plotOrd( obj, tran = TRUE, comp = 1:2, norm = TRUE, log = TRUE, usePCA = TRUE, useDist = FALSE, distfun = stats::dist, dist.method = "euclidian", n = NULL, ... ) } \arguments{ \item{obj}{A MRexperiment object or count matrix.} \item{tran}{Transpose the matrix.} \item{comp}{Which components to display} \item{norm}{Whether or not to normalize the counts - if MRexperiment object.} \item{log}{Whether or not to log2 the counts - if MRexperiment object.} \item{usePCA}{TRUE/FALSE whether to use PCA or MDS coordinates (TRUE is PCA).} \item{useDist}{TRUE/FALSE whether to calculate distances.} \item{distfun}{Distance function, default is stats::dist} \item{dist.method}{If useDist==TRUE, what method to calculate distances.} \item{n}{Number of features to make use of in calculating your distances.} \item{...}{Additional plot arguments.} } \value{ coordinates } \description{ This function plots the PCA / MDS coordinates for the "n" features of interest. Potentially uncovering batch effects or feature relationships. } \examples{ data(mouseData) cl = pData(mouseData)[,3] plotOrd(mouseData,tran=TRUE,useDist=TRUE,pch=21,bg=factor(cl),usePCA=FALSE) } \seealso{ \code{\link{cumNormMat}} } metagenomeSeq/man/plotRare.Rd0000644000175200017520000000171714710220170017220 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotRare.R \name{plotRare} \alias{plotRare} \title{Plot of rarefaction effect} \usage{ plotRare(obj, cl = NULL, ...) } \arguments{ \item{obj}{A MRexperiment object with count data or matrix.} \item{cl}{Vector of classes for various samples.} \item{...}{Additional plot arguments.} } \value{ Library size and number of detected features } \description{ This function plots the number of observed features vs. the depth of coverage. } \examples{ data(mouseData) cl = factor(pData(mouseData)[,3]) res = plotRare(mouseData,cl=cl,pch=21,bg=cl) tmp=lapply(levels(cl), function(lv) lm(res[,"ident"]~res[,"libSize"]-1, subset=cl==lv)) for(i in 1:length(levels(cl))){ abline(tmp[[i]], col=i) } legend("topleft", c("Diet 1","Diet 2"), text.col=c(1,2),box.col=NA) } \seealso{ \code{\link{plotOrd}}, \code{\link{plotMRheatmap}}, \code{\link{plotCorr}}, \code{\link{plotOTU}}, \code{\link{plotGenus}} } metagenomeSeq/man/plotTimeSeries.Rd0000644000175200017520000000171314710220170020374 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{plotTimeSeries} \alias{plotTimeSeries} \title{Plot difference function for particular bacteria} \usage{ plotTimeSeries( res, C = 0, xlab = "Time", ylab = "Difference in abundance", main = "SS difference function prediction", ... ) } \arguments{ \item{res}{Output of fitTimeSeries function} \item{C}{Value for which difference function has to be larger or smaller than (default 0).} \item{xlab}{X-label.} \item{ylab}{Y-label.} \item{main}{Main label.} \item{...}{Extra plotting arguments.} } \value{ Plot of difference in abundance for significant features. } \description{ Plot the difference in abundance for significant features. } \examples{ data(mouseData) res = fitTimeSeries(obj=mouseData,feature="Actinobacteria", class="status",id="mouseID",time="relativeTime",lvl='class',B=10) plotTimeSeries(res) } \seealso{ \code{\link{fitTimeSeries}} } metagenomeSeq/man/posteriorProbs.Rd0000644000175200017520000000203114710220170020452 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \docType{methods} \name{posteriorProbs} \alias{posteriorProbs} \alias{posteriorProbs,MRexperiment-method} \title{Access the posterior probabilities that results from analysis} \usage{ posteriorProbs(obj) } \arguments{ \item{obj}{a \code{MRexperiment} object.} } \value{ Matrix of posterior probabilities } \description{ Accessing the posterior probabilities following a run through \code{\link{fitZig}} } \examples{ # This is a simple demonstration data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] k = which(rowSums(MRcounts(lungTrim)>0)<30) lungTrim = cumNorm(lungTrim) lungTrim = lungTrim[-k,] smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) # The maxit is not meant to be 1 -- this is for demonstration/speed settings = zigControl(maxit=1,verbose=FALSE) fit = fitZig(obj = lungTrim,mod=mod,control=settings) head(posteriorProbs(lungTrim)) } \author{ Joseph N. Paulson } metagenomeSeq/man/returnAppropriateObj.Rd0000644000175200017520000000123714710220170021606 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allClasses.R \name{returnAppropriateObj} \alias{returnAppropriateObj} \title{Check if MRexperiment or matrix and return matrix} \usage{ returnAppropriateObj(obj, norm, log, sl = 1000) } \arguments{ \item{obj}{a \code{MRexperiment} or \code{matrix} object} \item{norm}{return a normalized \code{MRexperiment} matrix} \item{log}{return a log transformed \code{MRexperiment} matrix} \item{sl}{scaling value} } \value{ Matrix } \description{ Function to check if object is a MRexperiment class or matrix } \examples{ data(lungData) head(returnAppropriateObj(lungData,norm=FALSE,log=FALSE)) } metagenomeSeq/man/ssFit.Rd0000644000175200017520000000252414710220170016515 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{ssFit} \alias{ssFit} \title{smoothing-splines anova fit} \usage{ ssFit( formula, abundance, class, time, id, include = c("class", "time:class"), pd, ... ) } \arguments{ \item{formula}{Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value.} \item{abundance}{Numeric vector of abundances.} \item{class}{Class membership (factor of group membership).} \item{time}{Time point vector of relative times (same length as abundance).} \item{id}{Sample / patient id.} \item{include}{Parameters to include in prediction.} \item{pd}{Extra variable.} \item{...}{Extra parameters for ssanova function (see ?ssanova).} } \value{ \itemize{A list containing: \item data : Inputed data \item fit : The interpolated / fitted values for timePoints \item se : The standard error for CI intervals \item timePoints : The time points interpolated over } } \description{ Sets up a data-frame with the feature abundance, class information, time points, sample ids and returns the fitted values for the fitted model. } \examples{ # Not run } \seealso{ \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssPermAnalysis}} \code{\link{ssPerm}} \code{\link{ssIntervalCandidate}} } metagenomeSeq/man/ssIntervalCandidate.Rd0000644000175200017520000000163014710220170021351 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{ssIntervalCandidate} \alias{ssIntervalCandidate} \title{calculate interesting time intervals} \usage{ ssIntervalCandidate(fit, standardError, timePoints, positive = TRUE, C = 0) } \arguments{ \item{fit}{SS-Anova fits.} \item{standardError}{SS-Anova se estimates.} \item{timePoints}{Time points interpolated over.} \item{positive}{Positive region or negative region (difference in abundance is positive/negative).} \item{C}{Value for which difference function has to be larger or smaller than (default 0).} } \value{ Matrix of time point intervals of interest } \description{ Calculates time intervals of interest using SS-Anova fitted confidence intervals. } \examples{ # Not run } \seealso{ \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssFit}} \code{\link{ssPerm}} \code{\link{ssPermAnalysis}} } metagenomeSeq/man/ssPerm.Rd0000644000175200017520000000122514710220170016673 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{ssPerm} \alias{ssPerm} \title{class permutations for smoothing-spline time series analysis} \usage{ ssPerm(df, B) } \arguments{ \item{df}{Data frame containing class membership and sample/patient id label.} \item{B}{Number of permutations.} } \value{ A list of permutted class memberships } \description{ Creates a list of permuted class memberships for the time series permuation tests. } \examples{ # Not run } \seealso{ \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssFit}} \code{\link{ssPermAnalysis}} \code{\link{ssIntervalCandidate}} } metagenomeSeq/man/ssPermAnalysis.Rd0000644000175200017520000000216514710220170020403 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{ssPermAnalysis} \alias{ssPermAnalysis} \title{smoothing-splines anova fits for each permutation} \usage{ ssPermAnalysis( data, formula, permList, intTimes, timePoints, include = c("class", "time:class"), ... ) } \arguments{ \item{data}{Data used in estimation.} \item{formula}{Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value.} \item{permList}{A list of permutted class memberships} \item{intTimes}{Interesting time intervals.} \item{timePoints}{Time points to interpolate over.} \item{include}{Parameters to include in prediction.} \item{...}{Options for ssanova} } \value{ A matrix of permutted area estimates for time intervals of interest. } \description{ Calculates the fit for each permutation and estimates the area under the null (permutted) model for interesting time intervals of differential abundance. } \examples{ # Not run } \seealso{ \code{\link{cumNorm}} \code{\link{fitTimeSeries}} \code{\link{ssFit}} \code{\link{ssPerm}} \code{\link{ssIntervalCandidate}} } metagenomeSeq/man/trapz.Rd0000644000175200017520000000153114710220170016562 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{trapz} \alias{trapz} \title{Trapezoidal Integration} \usage{ trapz(x, y) } \arguments{ \item{x}{x-coordinates of points on the x-axis} \item{y}{y-coordinates of function values} } \value{ Approximated integral of the function from 'min(x)' to 'max(x)'. Or a matrix of the same size as 'y'. } \description{ Compute the area of a function with values 'y' at the points 'x'. Function comes from the pracma package. } \examples{ # Calculate the area under the sine curve from 0 to pi: n <- 101 x <- seq(0, pi, len = n) y <- sin(x) trapz(x, y) #=> 1.999835504 # Use a correction term at the boundary: -h^2/12*(f'(b)-f'(a)) h <- x[2] - x[1] ca <- (y[2]-y[1]) / h cb <- (y[n]-y[n-1]) / h trapz(x, y) - h^2/12 * (cb - ca) #=> 1.999999969 } metagenomeSeq/man/ts2MRexperiment.Rd0000644000175200017520000000271114710220170020473 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitTimeSeries.R \name{ts2MRexperiment} \alias{ts2MRexperiment} \title{With a list of fitTimeSeries results, generate an MRexperiment that can be plotted with metavizr} \usage{ ts2MRexperiment( obj, sampleNames = NULL, sampleDescription = "timepoints", taxonomyLevels = NULL, taxonomyHierarchyRoot = "bacteria", taxonomyDescription = "taxonomy", featuresOfInterest = NULL, featureDataOfInterest = NULL ) } \arguments{ \item{obj}{Output of fitMultipleTimeSeries} \item{sampleNames}{Sample names for plot} \item{sampleDescription}{Description of samples for plot axis label} \item{taxonomyLevels}{Feature names for plot} \item{taxonomyHierarchyRoot}{Root of feature hierarchy for MRexperiment} \item{taxonomyDescription}{Description of features for plot axis label} \item{featuresOfInterest}{The features to select from the fitMultipleTimeSeries output} \item{featureDataOfInterest}{featureData for the resulting MRexperiment} } \value{ MRexperiment that contains fitTimeSeries data, featureData, and phenoData } \description{ With a list of fitTimeSeries results, generate an MRexperiment that can be plotted with metavizr } \examples{ data(mouseData) res = fitMultipleTimeSeries(obj=mouseData,lvl='phylum',class="status", id="mouseID",time="relativeTime",B=1) obj = ts2MRexperiment(res) obj } \seealso{ \code{\link{fitTimeSeries}} \code{\link{fitMultipleTimeSeries}} } metagenomeSeq/man/uniqueFeatures.Rd0000644000175200017520000000145414710220170020433 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/misc.R \name{uniqueFeatures} \alias{uniqueFeatures} \title{Table of features unique to a group} \usage{ uniqueFeatures(obj, cl, nsamples = 0, nreads = 0) } \arguments{ \item{obj}{Either a MRexperiment object or matrix.} \item{cl}{A vector representing assigning samples to a group.} \item{nsamples}{The minimum number of positive samples.} \item{nreads}{The minimum number of raw reads.} } \value{ Table of features unique to a group } \description{ Creates a table of features, their index, number of positive samples in a group, and the number of reads in a group. Can threshold features by a minimum no. of reads or no. of samples. } \examples{ data(mouseData) head(uniqueFeatures(mouseData[1:100,],cl=pData(mouseData)[,3])) } metagenomeSeq/man/wrenchNorm.Rd0000644000175200017520000000154014710220170017544 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wrenchNorm.R \name{wrenchNorm} \alias{wrenchNorm} \title{Computes normalization factors using wrench instead of cumNorm} \usage{ wrenchNorm(obj, condition) } \arguments{ \item{obj}{an MRexperiment object} \item{condition}{case control label that wrench uses to calculate normalization factors} } \value{ an MRexperiment object with updated normalization factors. Accessible by \code{\link{normFactors}}. } \description{ Calculates normalization factors using method published by M. Sentil Kumar et al. (2018) to compute normalization factors which considers compositional bias introduced by sequencers. } \examples{ data(mouseData) mouseData <- wrenchNorm(mouseData, condition = mouseData$diet) head(normFactors(mouseData)) } \seealso{ \code{\link{cumNorm}} \code{\link{fitZig}} } metagenomeSeq/man/zigControl.Rd0000644000175200017520000000206114710220170017553 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/zigControl.R \name{zigControl} \alias{zigControl} \alias{settings2} \title{Settings for the fitZig function} \usage{ zigControl( tol = 1e-04, maxit = 10, verbose = TRUE, dfMethod = "modified", pvalMethod = "default" ) } \arguments{ \item{tol}{The tolerance for the difference in negative log likelihood estimates for a feature to remain active.} \item{maxit}{The maximum number of iterations for the expectation-maximization algorithm.} \item{verbose}{Whether to display iterative step summary statistics or not.} \item{dfMethod}{Either 'default' or 'modified' (by responsibilities).} \item{pvalMethod}{Either 'default' or 'bootstrap'.} } \value{ The value for the tolerance, maximum no. of iterations, and the verbose warning. } \description{ Settings for the fitZig function } \note{ \code{\link{fitZig}} makes use of zigControl. } \examples{ control = zigControl(tol=1e-10,maxit=10,verbose=FALSE) } \seealso{ \code{\link{fitZig}} \code{\link{cumNorm}} \code{\link{plotOTU}} } metagenomeSeq/tests/0000755000175200017520000000000014710220170015522 5ustar00biocbuildbiocbuildmetagenomeSeq/tests/testthat/0000755000175200017520000000000014710220170017362 5ustar00biocbuildbiocbuildmetagenomeSeq/tests/testthat.R0000644000175200017520000000176114710220170017512 0ustar00biocbuildbiocbuildlibrary("testthat") packageVersion("metagenomeSeq") # As suggested for opt-out option on testing by users, # recommended by CRAN: http://adv-r.had.co.nz/Testing.html # Previously, best practice was to put all test files in inst/tests # and ensure that R CMD check ran them by putting the following code in tests/test-all.R: # >library(testthat) # >library(yourpackage) # >test_package("yourpackage") # Now, recommended practice is to put your tests in tests/testthat, # and ensure R CMD check runs them by putting the following code in tests/test-all.R: # >library(testthat) # >test_check("yourpackage") # The advantage of this new structure is that the user has control over whether or not tests are installed using the –install-tests parameter to # R CMD install, or INSTALL_opts = c(“–install-tests”) argument to install.packages(). I’m not sure why you wouldn’t want to install the tests, # but now you have the flexibility as requested by CRAN maintainers. test_check("metagenomeSeq") metagenomeSeq/tests/testthat/test-fitZig.R0000644000175200017520000000513514710220170021722 0ustar00biocbuildbiocbuild################################################################################ # metagenomeSeq plot functions unit tests ################################################################################ context("Testing fitZig") library("metagenomeSeq"); library("testthat"); test_that("`fitZig` function provides expected values prior to split", { # uses the lung data and pre-calculated fitZig result from # prior to this separation data(lungData) path = system.file("extdata", package = "metagenomeSeq") fit = readRDS(file.path(path,"lungfit.rds")) # run the same fit k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] k = which(rowSums(MRcounts(lungTrim)>0)<30) lungTrim = cumNorm(lungTrim) lungTrim = lungTrim[-k,] smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) settings = zigControl(maxit=1,verbose=FALSE) fit2 = fitZig(obj = lungTrim,mod=mod,control=settings) # because the ordering is wrong expect_failure(expect_equal(fit,fit2)) # check that they're equal now #fit2 = fit2[names(fit)] # old way setAs("fitZigResults", "list", function(from) { list(call = from@call, fit = from@fit, countResiduals = from@countResiduals, z = from@z, eb = from@eb, taxa = from@taxa, counts = from@counts, zeroMod = from@zeroMod, stillActive = from@stillActive, stillActiveNLL = from@stillActiveNLL, zeroCoef = from@zeroCoef, dupcor = from@dupcor) }) # new way fit2 = as(fit2, "list") expect_equal(fit,fit2) }) test_that("`fitZig` function treats a matrix the same", { # uses the lung data and pre-calculated fitZig result from # prior to this separation data(lungData) path = system.file("extdata", package = "metagenomeSeq") fit = readRDS(file.path(path,"lungfit.rds")) # run the same fit k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] k = which(rowSums(MRcounts(lungTrim)>0)<30) lungTrim = cumNorm(lungTrim) lungTrim = lungTrim[-k,] smokingStatus = pData(lungTrim)$SmokingStatus scalingFactor = log2(normFactors(lungTrim)/1000 +1) mod = model.matrix(~smokingStatus) mod = cbind(mod,scalingFactor) settings = zigControl(maxit=1,verbose=FALSE) cnts = MRcounts(lungTrim) fit2 = fitZig(obj = lungTrim,mod=mod,control=settings,useCSSoffset=FALSE) #fit2 = fit2[names(fit)] # old way # new way - turning fitZigResults back to list fit2 = as(fit2, "list") # expecting failure because of call expect_failure(expect_equal(fit,fit2)) fit2$call = "123" fit$call = "123" # check that they're equal expect_equal(fit,fit2) }) metagenomeSeq/tests/testthat/test-norm.R0000644000175200017520000000303514710220170021436 0ustar00biocbuildbiocbuild################################################################################ # metagenomeSeq plot functions unit tests ################################################################################ context("Testing norm factor calculation") library("metagenomeSeq"); library("testthat") test_that("`calcNormFactors` function provides expected values", { # uses the lung data and pre-calculated normalization factors # for various values of p data(lungData) point25 = c(29,2475,2198,836,722,1820,79,1171,1985,710,145,742,848,89,1981) point = c(43,2475,2198,836,722,1820,119,1171,1985,710,145,742,848,89,1981) point100=as.numeric(unlist(libSize(lungData[,1:15]))) expect_equal(as.numeric(unlist(calcNormFactors(lungData[,1:15]))),point) expect_equal(as.numeric(unlist(calcNormFactors(lungData[,1:15],p=.25))),point25) expect_equal(as.numeric(unlist(calcNormFactors(lungData[,1:15],p=1))),point100) }) test_that("`cumNorm` returns the same object as defined in the package", { data(lungData); data(mouseData) expect_equal(cumNorm(mouseData,p=.5), mouseData) expect_equal(cumNorm(lungData), lungData) }) test_that("`cumNormStat` returns the correct value", { data(lungData); data(mouseData); expect_equal(as.numeric(cumNormStat(lungData)),0.7014946) expect_equal(as.numeric(cumNormStat(mouseData)),0.5) }) test_that("`cumNormStatFast` returns the correct value", { data(lungData); data(mouseData); expect_equal(as.numeric(cumNormStatFast(lungData)),0.7014946) expect_equal(as.numeric(cumNormStatFast(mouseData)),0.5) }) metagenomeSeq/tests/testthat/test-wrenchNorm.R0000644000175200017520000000153214710220170022605 0ustar00biocbuildbiocbuild## unit test for wrenchNorm context("Test that wrenchNorm functions properly") library("metagenomeSeq"); library("testthat"); test_that( "norm factors generated are correct",{ data("lungData"); data("mouseData"); mouseNF <- c(0.3364660,0.7051424,1.3295084,0.8530978,0.7545386,2.1273695,1.2158941,1.9025748,0.5382427,0.5841864) lungNF <- c(0.006551719,12.267861013,10.106967942,2.447679975,1.266012939,5.701245412,0.049474404,2.863477065,6.821474324,1.261155349) lungData <- lungData[, -which(is.na(pData(lungData)$SmokingStatus))] lungData2 <- wrenchNorm(lungData, condition = lungData$SmokingStatus) mouseData2 <- wrenchNorm(mouseData, condition = mouseData$diet) expect_equal(as.numeric(normFactors(lungData2)[1:10]), lungNF, tolerance=1e-03) expect_equal(as.numeric(unlist(normFactors(mouseData2)[1:10])), mouseNF, tolerance = 1e-03) }) metagenomeSeq/vignettes/0000755000175200017520000000000014735615226016411 5ustar00biocbuildbiocbuildmetagenomeSeq/vignettes/fitTimeSeries.Rnw0000644000175200017520000003412414710220170021640 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{fitTimeSeries: differential abundance analysis through time or location} %\VignetteEngine{knitr::knitr} \documentclass[a4paper,11pt]{article} \usepackage{url} \usepackage{afterpage} \usepackage{hyperref} \usepackage{geometry} \usepackage{cite} \geometry{hmargin=2.5cm, vmargin=2.5cm} \usepackage{graphicx} \usepackage{courier} \bibliographystyle{unsrt} \begin{document} <>= require(knitr) opts_chunk$set(concordance=TRUE,tidy=TRUE) @ \title{{\textbf{\texttt{fitTimeSeries}: Longitudinal differential abundance analysis for marker-gene surveys}}} \author{Hisham Talukder, Joseph N. Paulson, Hector Corrada Bravo\\[1em]\\ Applied Mathematics $\&$ Statistics, and Scientific Computation\\ Center for Bioinformatics and Computational Biology\\ University of Maryland, College Park\\[1em]\\ \texttt{jpaulson@umiacs.umd.edu}} \date{Modified: February 18, 2015. Compiled: \today} \maketitle \tableofcontents \newpage <>= options(width = 65) options(continue=" ") options(warn=-1) set.seed(42) @ \section{Introduction} \textbf{This is a vignette specifically for the fitTimeSeries function. For a full list of functions available in the package: help(package=metagenomeSeq). For more information about a particular function call: ?function.} Smoothing spline regression models~\cite{Wahba:1990} are commonly used to model longitudinal data and form the basis for methods used in a large number of applications ~\cite{networkped1,LongCrisp}. Specifically, an extension of the methodology called Smoothing-Spline ANOVA~\cite{Gu} is capable of directly estimating a smooth function of interest while incorporating other covariates in the model. A common approach to detect regions/times of interest in a genome or for differential abundance is to model differences between two groups with respect to the quantitative measurements as smooth functions and perform statistical inference on these models. In particular, widely used methods for region finding using DNA methylation data use local regression methods to estimate these smooth functions. An important aspect of these tools is their ability to incorporate sample characteristics as covariates in these models, e.g., sex and age in population studies, or technical factors like processing batches. Incorporating these sources of variability, both biological and technical is essential in high-throughput studies. Therefore, these methods require that the models used are capable of estimating both smooth functions and sample-specfic characteristics. We present fitTimeSeries - a method for estimating and detecting regions/times of interest due to differential abundance of a quantitative measurement (for example, normalized abundance). \subsection{Problem Formulation} We model data in the following form: $$ Y_{itk}= f_i(t,x_{k})+e_{tk} $$ where i represents group factor (diet, health status, etc.), $t$ represents series factor (for example, time or location), $k$ represents replicate observations, $x_{k}$ are covariates for sample $k$ (including an indicator for group membership $I\{k \in i\}$) and $e_{tk}$ are independent $N(0,\sigma^2)$ errors. We assume $f_i$ to be a smooth function, defined in an interval $[a,b]$, that can be parametric, non-parametric or a mixture of both. Our goal is to identify intervals where the absolute difference between two groups $\eta_d(t)=f_1(t, \cdot)-f_2(t, \cdot)$ is large, that is, regions, $R_{t_1,t_2}$, where: $R_{t_1,t_2}= \{t_1,t_2 \in x \textit{ such that } | \eta_{d}(x) | \ge C \}$ and $C$ is a predefined constant threshold. To identify these areas we use hypothesis testing using the area $A_{t_1,t_2}=\int_{R_{t_1,t_2}}\eta_d(t) dt$ under the estimated function of $\eta_d(t)$ as a statistic with null and alternative hypotheses $$ H_0: A_{t_1,t_2} \le K $$ $$ H_1: A_{t_1,t_2} > K $$ with $K$ some fixed threshold. We employ a permutation-based method to calculate a null distribution of the area statistics $A_(t1,t2)$'s. To do this, the group-membership indicator variables (0-1 binary variable) are randomly permuted $B$ times, e.g., $B=1000$ and the method above is used to estimate the difference function $\eta_d^b$ (in this case simulating the null hypothesis) and an area statistics $A_(t1,t2)^b$ for each random permutation. Estimates $A_(t1,t2)^b$ are then used to construct an empirical estimate of $A_(t1,t2)$ under the null hypothesis. The observed area, $A_(t1,t2)^*$, is compared to the empirical null distribution to calculate a p-value. Figure 1 illustrates the relationship between $R_(t1,t2)$ and $A_(t1,t2)$. The key is to estimate regions $R_(t1,t2)$ where point-wise confidence intervals would be appropriate. \section{Data preparation} Data should be preprocessed and prepared in tab-delimited files. Measurements are stored in a matrix with samples along the columns and features along the rows. For example, given $m$ features and $n$ samples, the entries in a marker-gene or metagenomic count matrix \textbf{C} ($m, n$), $c_{ij}$, are the number of reads annotated for a particular feature $i$ (whether it be OTU, species, genus, etc.) in sample $j$. Alternatively, the measurements could be some quantitative measurement such as methylation percentages or CD4 levels.\\ \begin{center} $\bordermatrix{ &sample_1&sample_2&\ldots &sample_n\cr feature_1&c_{11} & c_{12} & \ldots & c_{1n}\cr feature_2& c_{21} & c_{22} & \ldots & c_{2n}\cr \vdots & \vdots & \vdots & \ddots & \vdots\cr feature_m & c_{m1} & c_{m2} &\ldots & c_{mn}}$ \end{center} Data should be stored in a file (tab-delimited by default) with sample names along the first row, feature names in the first column and should be loaded into R and formatted into a MRexperiment object. To prepare the data please read the section on data preparation in the full metagenomeSeq vignette - \texttt{vignette("metagenomeSeq")}. \subsection{Example datasets} There is a time-series dataset included as an examples in the \texttt{metagenomeSeq} package. Data needs to be in a \texttt{MRexperiment} object format to normalize, run the statistical tests, and visualize. As an example, throughout the vignette we'll use the following datasets. To understand a \texttt{fitTimeSeries}'s usage or included data simply enter ?\texttt{fitTimeSeries}. <>= library(metagenomeSeq) library(gss) @ \begin{enumerate} \setcounter{enumi}{1} \item Humanized gnotobiotic mouse gut \cite{ts_mouse}: Twelve germ-free adult male C57BL/6J mice were fed a low-fat, plant polysaccharide-rich diet. Each mouse was gavaged with healthy adult human fecal material. Following the fecal transplant, mice remained on the low-fat, plant polysacchaaride-rich diet for four weeks, following which a subset of 6 were switched to a high-fat and high-sugar diet for eight weeks. Fecal samples for each mouse went through PCR amplification of the bacterial 16S rRNA gene V2 region weekly. Details of experimental protocols and further details of the data can be found in Turnbaugh et. al. Sequences and further information can be found at: \url{http://gordonlab.wustl.edu/TurnbaughSE_10_09/STM_2009.html} \end{enumerate} <>= data(mouseData) mouseData @ \subsection{Creating a \texttt{MRexperiment} object with other measurements} For a fitTimeSeries analysis a minimal MRexperiment-object is required and can be created using the function \texttt{newMRexperiment} which takes a count matrix described above and phenoData (annotated data frame). \texttt{Biobase} provides functions to create annotated data frames. <>= # Creating mock sample replicates sampleID = rep(paste("sample",1:10,sep=":"),times=20) # Creating mock class membership class = rep(c(rep(0,5),rep(1,5)),times=20) # Creating mock time time = rep(1:20,each=10) phenotypeData = AnnotatedDataFrame(data.frame(sampleID,class,time)) # Creating mock abundances set.seed(1) # No difference measurement1 = rnorm(200,mean=100,sd=1) # Some difference measurement2 = rnorm(200,mean=100,sd=1) measurement2[1:5]=measurement2[1:5] + 100 measurement2[11:15]=measurement2[11:15] + 100 measurement2[21:25]=measurement2[21:25] + 50 mat = rbind(measurement1,measurement2) colnames(mat) = 1:200 mat[1:2,1:10] @ If phylogenetic information exists for the features and there is a desire to aggregate measurements based on similar annotations choosing the featureData column name in lvl will aggregate measurements using the default parameters in the \texttt{aggregateByTaxonomy} function. <>= # This is an example of potential lvl's to aggregate by. data(mouseData) colnames(fData(mouseData)) @ Here we create the actual MRexperiment to run through fitTimeSeries. <>= obj = newMRexperiment(counts=mat,phenoData=phenotypeData) obj res1 = fitTimeSeries(obj,feature=1, class='class',time='time',id='sampleID', B=10,norm=FALSE,log=FALSE) res2 = fitTimeSeries(obj,feature=2, class='class',time='time',id='sampleID', B=10,norm=FALSE,log=FALSE) classInfo = factor(res1$data$class) @ <>= par(mfrow=c(3,1)) plotClassTimeSeries(res1,pch=21,bg=classInfo) plotTimeSeries(res2) plotClassTimeSeries(res2,pch=21,bg=classInfo) @ \section{Time series analysis} Implemented in the \texttt{fitTimeSeries} function is a method for calculating time intervals for which bacteria are differentially abundant. Fitting is performed using Smoothing Splines ANOVA (SS-ANOVA), as implemented in the \texttt{gss} package. Given observations at multiple time points for two groups the method calculates a function modeling the difference in abundance across all time. Using group membership permutations we estimate a null distribution of areas under the difference curve for the time intervals of interest and report significant intervals of time. Here we provide a real example from the microbiome of two groups of mice on different diets. The gnotobiotic mice come from a longitudinal study ideal for this type of analysis. We choose to perform our analysis at the class level and look for differentially abundant time intervals for "Actinobacteria". For demonstrations sake we perform only 10 permutations. If you find the method useful, please cite: "Longitudinal differential abundance analysis for marker-gene surveys" Talukder H*, Paulson JN*, Bravo HC. (Submitted) <>= res = fitTimeSeries(obj=mouseData,lvl="class",feature="Actinobacteria",class="status",id="mouseID",time="relativeTime",B=10) # We observe a time period of differential abundance for "Actinobacteria" res$timeIntervals str(res) @ For example, to test every class in the mouse dataset: <>= set.seed(123) classes = unique(fData(mouseData)[,"class"]) timeSeriesFits = lapply(classes,function(i){ fitTimeSeries(obj=mouseData, feature=i, class="status", id="mouseID", time="relativeTime", lvl='class', C=.3,# a cutoff for 'interesting' B=1) # B is the number of permutations and should clearly not be 1 }) names(timeSeriesFits) = classes # Removing classes of bacteria without a potentially # interesting time interval difference. timeSeriesFits = lapply(timeSeriesFits,function(i){i[[1]]})[-grep("No",timeSeriesFits)] # Naming the various interesting time intervals. for(i in 1:length(timeSeriesFits)){ rownames(timeSeriesFits[[i]]) = paste( paste(names(timeSeriesFits)[i]," interval",sep=""), 1:nrow(timeSeriesFits[[i]]),sep=":" ) } # Merging into a table. timeSeriesFits = do.call(rbind,timeSeriesFits) # Correcting for multiple testing. pvalues = timeSeriesFits[,"p.value"] adjPvalues = p.adjust(pvalues,"bonferroni") timeSeriesFits = cbind(timeSeriesFits,adjPvalues) head(timeSeriesFits) @ Please see the help page for \texttt{fitTimeSeries} for parameters. Note, only two groups can be compared to each other and the time parameter must be an actual value (currently no support for posix, etc.). \subsection{Paramaters} There are a number of parameters for the \texttt{fitTimeSeries} function. We list and provide a brief discussion below. For parameters influencing \texttt{ssanova}, \texttt{aggregateByTaxonomy}, \texttt{MRcounts} type ?function for more details. \begin{itemize} \item obj - the metagenomeSeq MRexperiment-class object. \item feature - Name or row of feature of interest. \item class - Name of column in phenoData of MRexperiment-class object for class memberhip. \item time - Name of column in phenoData of MRexperiment-class object for relative time. \item id - Name of column in phenoData of MRexperiment-class object for sample id. \item method - Method to estimate time intervals of differentially abundant bacteria (only ssanova method implemented currently). \item lvl - Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level). \item C - Value for which difference function has to be larger or smaller than (default 0). \item B - Number of permutations to perform (default 1000) \item norm - When aggregating counts to normalize or not. (see MRcounts) \item log - Log2 transform. (see MRcounts) \item sl - Scaling value. (see MRcounts) \item ... - Options for ssanova \end{itemize} \section{Visualization of features} To help with visualization and analysis of datasets \texttt{metagenomeSeq} has several plotting functions to gain insight of the model fits and the differentially abundant time intervals using \texttt{plotClassTimeSeries} and \texttt{plotTimeSeries} on the result. More plots will be updated. <>= par(mfrow=c(2,1)) plotClassTimeSeries(res,pch=21, bg=res$data$class,ylim=c(0,8)) plotTimeSeries(res) @ \section{Summary} \texttt{metagenomeSeq}'s \texttt{fitTimeSeries} is a novel methodology for differential abundance testing of longitudinal data. If you make use of the statistical method please cite our paper. If you made use of the manual/software, please cite the manual/software! \subsection{Citing fitTimeSeries} <>= citation("metagenomeSeq") @ \subsection{Session Info} <>= sessionInfo() @ \bibliography{fitTimeSeries} \end{document} metagenomeSeq/vignettes/fitTimeSeries.bib0000644000175200017520000000337614710220170021633 0ustar00biocbuildbiocbuild@BOOK{Wahba:1990, AUTHOR = {G. Wahba}, TITLE = {Spline Models in Statistics}, SERIES = {CBMS-NSF Regional Conference Series}, PUBLISHER = {SIAM}, ADDRESS = {Philadelphia, PA}, YEAR = {1990} } @ARTICLE{longcrisp, author = {H. Jaroslaw and N. Elena and M.L. Nan}, title = {LongCriSP: A test for Bumphunting in Longitudinal data}, journal = {Statistics in Medicine}, year = {2006}, volume = {26}, pages = {1383--1397} } @article{bumphunter, title={Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies}, author={Jaffe, Andrew E and Murakami, Peter and Lee, Hwajin and Leek, Jeffrey T and Fallin, M Daniele and Feinberg, Andrew P and Irizarry, Rafael A}, journal={International journal of epidemiology}, volume={41}, number={1}, pages={200--209}, year={2012}, publisher={IEA} } @book{networkped1, title={Graph-based data analysis: tree-structured covariance estimation, prediction by regularized kernel estimation and aggregate database query processing for probabilistic inference}, author={Bravo, H{\'e}ctor Corrada}, year={2008}, publisher={ProQuest} } @BOOK{Gu, author = {C. Gu}, title = {Smoothing Spline Anova Model}, series = {Springer Series in Statistics}, publisher = {Springer}, year = {2002} } @article{ts_mouse, title={The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice.}, volume={1}, number={6}, journal={Science translational medicine}, publisher={NIH Public Access}, author={Turnbaugh, Peter J and Ridaura, Vanessa K and Faith, Jeremiah J and Rey, Federico E and Knight, Rob and Gordon, Jeffrey I}, year={2009}, pages={6ra14}}metagenomeSeq/vignettes/metagenomeSeq.Rnw0000644000175200017520000012230214710220170021652 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{metagenomeSeq: statistical analysis for sparse high-throughput sequencing} %\VignetteEngine{knitr::knitr} \documentclass[a4paper,11pt]{article} \usepackage{url} \usepackage{afterpage} \usepackage{hyperref} \usepackage{geometry} \usepackage{cite} \geometry{hmargin=2.5cm, vmargin=2.5cm} \usepackage{graphicx} \usepackage{courier} \bibliographystyle{unsrt} \begin{document} <>= require(knitr) opts_chunk$set(concordance=TRUE,tidy=TRUE) @ \title{{\textbf{\texttt{metagenomeSeq}: Statistical analysis for sparse high-throughput sequencing}}} \author{Joseph Nathaniel Paulson\\[1em]\\ Applied Mathematics $\&$ Statistics, and Scientific Computation\\ Center for Bioinformatics and Computational Biology\\ University of Maryland, College Park\\[1em]\\ \texttt{jpaulson@umiacs.umd.edu}} \date{Modified: October 4, 2016. Compiled: \today} \maketitle \tableofcontents \newpage <>= options(width = 60) options(continue=" ") options(warn=-1) set.seed(42) @ \section{Introduction} \textbf{This is a vignette for pieces of an association study pipeline. For a full list of functions available in the package: help(package=metagenomeSeq). For more information about a particular function call: ?function.} See \textit{fitFeatureModel} for our latest development. To load the metagenomeSeq library: <>= library(metagenomeSeq) @ Metagenomics is the study of genetic material targeted directly from an environmental community. Originally focused on exploratory and validation projects, these studies now focus on understanding the differences in microbial communities caused by phenotypic differences. Analyzing high-throughput sequencing data has been a challenge to researchers due to the unique biological and technological biases that are present in marker-gene survey data. We present a R package, \texttt{metagenomeSeq}, that implements methods developed to account for previously unaddressed biases specific to high-throughput sequencing microbial marker-gene survey data. Our method implements a novel normalization technique and method to account for sparsity due to undersampling. Other methods include White \textit{et al.}'s Metastats and Segata \textit{et al.}'s LEfSe. The first is a non-parametric permutation test on $t$-statistics and the second is a non-parametric Kruskal-Wallis test followed by subsequent wilcox rank-sum tests on subgroups to guard against positive discoveries of differential abundance driven by potential confounders - neither address normalization nor sparsity. This vignette describes the basic protocol when using \texttt{metagenomeSeq}. A normalization method able to control for biases in measurements across taxonomic features and a mixture model that implements a zero-inflated Gaussian distribution to account for varying depths of coverage are implemented. Using a linear model methodology, it is easy to include confounding sources of variability and interpret results. Additionally, visualization functions are provided to examine discoveries. The software was designed to determine features (be it Operational Taxonomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. The software was also designed to address the effects of both normalization and undersampling of microbial communities on disease association detection and testing of feature correlations. \begin{figure} \centerline{\includegraphics[width=.55\textwidth]{overview.pdf}} \caption{General overview. metagenomeSeq requires the user to convert their data into MRexperiment objects. Using those MRexperiment objects, one can normalize their data, run statistical tests (abundance or presence-absence), and visualize or save results.} \end{figure} \newpage \section{Data preparation} Microbial marker-gene sequence data is preprocessed and counts are algorithmically defined from project-specific sequence data by clustering reads according to read similarity. Given $m$ features and $n$ samples, the elements in a count matrix \textbf{C} ($m, n$), $c_{ij}$, are the number of reads annotated for a particular feature $i$ (whether it be OTU, species, genus, etc.) in sample $j$. \\ \begin{center} $\bordermatrix{ &sample_1&sample_2&\ldots &sample_n\cr feature_1&c_{11} & c_{12} & \ldots & c_{1n}\cr feature_2& c_{21} & c_{22} & \ldots & c_{2n}\cr \vdots & \vdots & \vdots & \ddots & \vdots\cr feature_m & c_{m1} & c_{m2} &\ldots & c_{mn}}$ \end{center} Count data should be stored in a delimited (tab by default) file with sample names along the first row and feature names along the first column. Data is prepared and formatted as a \texttt{MRexperiment} object. For an overview of the internal structure please see Appendix A. \subsection{Biom-Format} You can load in BIOM file format data, the output of many commonly used, using the \texttt{loadBiom} function. The \texttt{biom2MRexperiment} and \texttt{MRexperiment2biom} functions serve as a gateway between the \texttt{biom-class} object defined in the \textbf{biom} package and a \texttt{MRexperiment-class} object. BIOM format files IO is available thanks to the \texttt{biomformat} package. As an example, we show how one can read in a BIOM file and convert it to a \texttt{MRexperiment} object. <>= # reading in a biom file library(biomformat) biom_file <- system.file("extdata", "min_sparse_otu_table.biom", package = "biomformat") b <- read_biom(biom_file) biom2MRexperiment(b) @ As an example, we show how one can write a \texttt{MRexperiment} object out as a BIOM file. Here is an example writing out the mouseData \texttt{MRexperiment} object to a BIOM file. <>= data(mouseData) # options include to normalize or not b <- MRexperiment2biom(mouseData) write_biom(b,biom_file="~/Desktop/otu_table.biom") @ \subsection{Loading count data} Following preprocessing and annotation of sequencing data \texttt{metagenomeSeq} requires a count matrix with features along rows and samples along the columns. \texttt{metagenomeSeq} includes functions for loading delimited files of counts \texttt{loadMeta} and phenodata \texttt{loadPhenoData}. As an example, a portion of the lung microbiome \cite{charlson} OTU matrix is provided in \texttt{metagenomeSeq}'s library "extdata" folder. The OTU matrix is stored as a tab delimited file. \texttt{loadMeta} loads the taxa and counts into a list. <>= dataDirectory <- system.file("extdata", package="metagenomeSeq") lung = loadMeta(file.path(dataDirectory,"CHK_NAME.otus.count.csv")) dim(lung$counts) @ \subsection{Loading taxonomy} Next we want to load the annotated taxonomy. Check to make sure that your taxa annotations and OTUs are in the same order as your matrix rows. <>= taxa = read.delim(file.path(dataDirectory,"CHK_otus.taxonomy.csv"),stringsAsFactors=FALSE) @ As our OTUs appear to be in order with the count matrix we loaded earlier, the next step is to load phenodata. \textbf{Warning}: features need to have the same names as the rows of the count matrix when we create the MRexperiment object for provenance purposes. \subsection{Loading metadata} Phenotype data can be optionally loaded into \texttt{R} with \texttt{loadPhenoData}. This function loads the data as a list. <>= clin = loadPhenoData(file.path(dataDirectory,"CHK_clinical.csv"),tran=TRUE) ord = match(colnames(lung$counts),rownames(clin)) clin = clin[ord,] head(clin[1:2,]) @ \textbf{Warning}: phenotypes must have the same names as the columns on the count matrix when we create the MRexperiment object for provenance purposes. \subsection{Creating a \texttt{MRexperiment} object} Function \texttt{newMRexperiment} takes a count matrix, phenoData (annotated data frame), and featureData (annotated data frame) as input. \texttt{Biobase} provides functions to create annotated data frames. Library sizes (depths of coverage) and normalization factors are also optional inputs. <>= phenotypeData = AnnotatedDataFrame(clin) phenotypeData @ A feature annotated data frame. In this example it is simply the OTU numbers, but it can as easily be the annotated taxonomy at multiple levels. <>= OTUdata = AnnotatedDataFrame(taxa) OTUdata @ <>= obj = newMRexperiment(lung$counts,phenoData=phenotypeData,featureData=OTUdata) # Links to a paper providing further details can be included optionally. # experimentData(obj) = annotate::pmid2MIAME("21680950") obj @ \subsection{Example datasets} There are two datasets included as examples in the \texttt{metagenomeSeq} package. Data needs to be in a \texttt{MRexperiment} object format to normalize, run statistical tests, and visualize. As an example, throughout the vignette we'll use the following datasets. To understand a function's usage or included data simply enter ?functionName. \begin{enumerate} \item Human lung microbiome \cite{charlson}: The lung microbiome consists of respiratory flora sampled from six healthy individuals. Three healthy nonsmokers and three healthy smokers. The upper lung tracts were sampled by oral wash and oro-/nasopharyngeal swabs. Samples were taken using two bronchoscopes, serial bronchoalveolar lavage and lower airway protected brushes. \end{enumerate} <>= data(lungData) lungData @ \begin{enumerate} \setcounter{enumi}{1} \item Humanized gnotobiotic mouse gut \cite{ts_mouse}: Twelve germ-free adult male C57BL/6J mice were fed a low-fat, plant polysaccharide-rich diet. Each mouse was gavaged with healthy adult human fecal material. Following the fecal transplant, mice remained on the low-fat, plant polysacchaaride-rich diet for four weeks, following which a subset of 6 were switched to a high-fat and high-sugar diet for eight weeks. Fecal samples for each mouse went through PCR amplification of the bacterial 16S rRNA gene V2 region weekly. Details of experimental protocols and further details of the data can be found in Turnbaugh et. al. Sequences and further information can be found at: \url{http://gordonlab.wustl.edu/TurnbaughSE_10_09/STM_2009.html} \end{enumerate} <>= data(mouseData) mouseData @ \newpage \subsection{Useful commands} Phenotype information can be accessed with the \verb+phenoData+ and \verb+pData+ methods: <>= phenoData(obj) head(pData(obj),3) @ Feature information can be accessed with the \verb+featureData+ and \verb+fData+ methods: <>= featureData(obj) head(fData(obj)[,-c(2,10)],3) @ \newpage The raw or normalized counts matrix can be accessed with the \verb+MRcounts+ function: <>= head(MRcounts(obj[,1:2])) @ A \texttt{MRexperiment-class} object can be easily subsetted, for example: <<>>= featuresToKeep = which(rowSums(obj)>=100) samplesToKeep = which(pData(obj)$SmokingStatus=="Smoker") obj_smokers = obj[featuresToKeep,samplesToKeep] obj_smokers head(pData(obj_smokers),3) @ Alternative normalization scaling factors can be accessed or replaced with the \verb+normFactors+ method: <>= head(normFactors(obj)) normFactors(obj) <- rnorm(ncol(obj)) head(normFactors(obj)) @ Library sizes (sequencing depths) can be accessed or replaced with the \verb+libSize+ method: <>= head(libSize(obj)) libSize(obj) <- rnorm(ncol(obj)) head(libSize(obj)) @ \newpage Additionally, data can be filtered to maintain a threshold of minimum depth or OTU presence: <>= data(mouseData) filterData(mouseData,present=10,depth=1000) @ Two \texttt{MRexperiment-class} objects can be merged with the \texttt{mergeMRexperiments} function, e.g.: <>= data(mouseData) newobj = mergeMRexperiments(mouseData,mouseData) newobj @ \newpage \section{Normalization} Normalization is required due to varying depths of coverage across samples. \texttt{cumNorm} is a normalization method that calculates scaling factors equal to the sum of counts up to a particular quantile. Denote the $l$th quantile of sample $j$ as $q_j^l$, that is, in sample $j$ there are $l$ taxonomic features with counts smaller than $q_j^l$. For $l= \lfloor .95m \rfloor$ then $q_j^l$ corresponds to the 95th percentile of the count distribution for sample $j$. Denote $s_j^l= \sum_{(i|c_{ij}\leq q_j^l)}c_{ij}$ as the sum of counts for sample $j$ up to the $l$th quantile. Our normalization chooses a value $\hat{l}\leq m$ to define a normalization scaling factor for each sample to produce normalized counts $\tilde{c_{ij}}$ = $\frac{c_{ij}}{s_j^{\hat{l}}}N$ where $N$ is an appropriately chosen normalization constant. See Appendix C for more information on how our method calculates the proper percentile. These normalization factors are stored in the experiment summary slot. Functions to determine the proper percentile \texttt{cumNormStat}, save normalized counts \texttt{exportMat}, or save various sample statistics \texttt{exportStats} are also provided. Normalized counts can be called easily by \texttt{cumNormMat(MRexperimentObject)} or \texttt{MRcounts(MRexperimentObject,norm=TRUE,log=FALSE)}. \subsection{Calculating normalization factors} After defining a \texttt{MRexperiment} object, the first step is to calculate the proper percentile by which to normalize counts. There are several options in calculating and visualizing the relative differences in the reference. Figure 3 is an example from the lung dataset. <>= data(lungData) p=cumNormStatFast(lungData) @ \noindent To calculate the scaling factors we simply run \texttt{cumNorm} <>= lungData = cumNorm(lungData,p=p) @ The user can alternatively choose different percentiles for the normalization scheme by specifying $p$. There are other functions, including \texttt{normFactors}, \texttt{cumNormMat}, that return the normalization factors or a normalized matrix for a specified percentile. To see a full list of functions please refer to the manual and help pages. \subsubsection{Calculating normalization factors using Wrench} An alternative to normalizing counts using \texttt{cumNorm} is to use \texttt{wrenchNorm}. It behaves similarly to \texttt{cumNorm}, however, it takes the argument \texttt{condition} instead of \texttt{p}. \texttt{condition} is a factor with values that separate samples into phenotypic groups of interest. When appropriate, wrench normalization is preferrable over cumulative normalization (see https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-5160-5 for details). In the example below, \texttt{mouseData} samples are compared based on diet. <>= condition = mouseData$diet mouseData = wrenchNorm(mouseData,condition=condition) @ \subsection{Exporting data} To export normalized count matrices: <>= mat = MRcounts(lungData,norm=TRUE,log=TRUE)[1:5,1:5] exportMat(mat,file=file.path(dataDirectory,"tmp.tsv")) @ \noindent To save sample statistics (sample scaling factor, quantile value, number of identified features and library size): <>= exportStats(lungData[,1:5],file=file.path(dataDirectory,"tmp.tsv")) head(read.csv(file=file.path(dataDirectory,"tmp.tsv"),sep="\t")) @ <>= system(paste("rm",file.path(dataDirectory,"tmp.tsv"))) @ \newpage \section{Statistical testing} Now that we have taken care of normalization we can address the effects of under sampling on detecting differentially abundant features (OTUs, genes, etc). This is our latest development and we recommend \textit{fitFeatureModel} over \textit{fitZig}. \textit{MRcoefs}, \textit{MRtable} and \textit{MRfulltable} are useful summary tables of the model outputs. \subsection{Zero-inflated Log-Normal mixture model for each feature} By reparametrizing our zero-inflation model, we're able to fit a zero-inflated model for each specific OTU separately. We currently recommend using the zero-inflated log-normal model as implemented in \textit{fitFeatureModel}. \subsubsection{Example using fitFeatureModel for differential abundance testing} Here is an example comparing smoker's and non-smokers lung microbiome. <>= data(lungData) lungData = lungData[,-which(is.na(pData(lungData)$SmokingStatus))] lungData=filterData(lungData,present=30,depth=1) lungData <- cumNorm(lungData, p=.5) pd <- pData(lungData) mod <- model.matrix(~1+SmokingStatus, data=pd) lungres1 = fitFeatureModel(lungData,mod) head(MRcoefs(lungres1)) @ \subsection{Zero-inflated Gaussian mixture model} The depth of coverage in a sample is directly related to how many features are detected in a sample motivating our zero-inflated Gaussian (ZIG) mixture model. Figure 2 is representative of the linear relationship between depth of coverage and OTU identification ubiquitous in marker-gene survey datasets currently available. For a quick overview of the mathematical model see Appendix B. \begin{figure} \centerline{\includegraphics[width=.55\textwidth]{metagenomeSeq_figure1.png}} \caption{\footnotesize{The number of unique features is plotted against depth of coverage for samples from the Human Microbiome Project \cite{hmp}. Including the depth of coverage and the interaction of body site and sequencing site we are able to acheive an adjusted $\mathrm{R}^2$ of .94. The zero-inflated Gaussian mixture was developed to account for missing features.}}\label{fig1} \end{figure} Function \texttt{fitZig} performs a complex mathematical optimization routine to estimate probabilities that a zero for a particular feature in a sample is a technical zero or not. The function relies heavily on the \texttt{limma} package \cite{limma}. Design matrices can be created in R by using the \texttt{model.matrix} function and are inputs for \texttt{fitZig}. For large survey studies it is often pertinent to include phenotype information or confounders into a design matrix when testing the association between the abundance of taxonomic features and a phenotype of interest (disease, for instance). Our linear model methodology can easily incorporate these confounding covariates in a straightforward manner. \texttt{fitZig} output includes weighted fits for each of the $m$ features. Results can be filtered and saved using \texttt{MRcoefs} or \texttt{MRtable}. \subsubsection{Example using fitZig for differential abundance testing} \textbf{Warning}: The user should restrict significant features to those with a minimum number of positive samples. What this means is that one should not claim features are significant unless the effective number of samples is above a particular percentage. For example, fold-change estimates might be unreliable if an entire group does not have a positive count for the feature in question. We recommend the user remove features based on the number of estimated effective samples, please see \texttt{calculateEffectiveSamples}. We recommend removing features with less than the average number of effective samples in all features. In essence, setting eff = .5 when using \texttt{MRcoefs}, \texttt{MRfulltable}, or \texttt{MRtable}. To find features absent from a group the function \texttt{uniqueFeatures} provides a table of the feature ids, the number of positive features and reads for each group. In our analysis of the lung microbiome data, we can remove features that are not present in many samples, controls, and calculate the normalization factors. The user needs to decide which metadata should be included in the linear model. <>= data(lungData) controls = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-controls] rareFeatures = which(rowSums(MRcounts(lungTrim)>0)<10) lungTrim = lungTrim[-rareFeatures,] lungp = cumNormStat(lungTrim,pFlag=TRUE,main="Trimmed lung data") lungTrim = cumNorm(lungTrim,p=lungp) @ After the user defines an appropriate model matrix for hypothesis testing there are optional inputs to \texttt{fitZig}, including settings determined by \texttt{zigControl}. We ask the user to review the help files for both \texttt{fitZig} and \texttt{zigControl}. For this example we include body site as covariates and want to test for the bacteria differentially abundant between smokers and non-smokers. <>= smokingStatus = pData(lungTrim)$SmokingStatus bodySite = pData(lungTrim)$SampleType normFactor = normFactors(lungTrim) normFactor = log2(normFactor/median(normFactor) + 1) mod = model.matrix(~smokingStatus+bodySite + normFactor) settings = zigControl(maxit=10,verbose=TRUE) fit = fitZig(obj = lungTrim,mod=mod,useCSSoffset = FALSE, control=settings) # The default, useCSSoffset = TRUE, automatically includes the CSS scaling normalization factor. @ The result, \texttt{fit}, is a list providing detailed estimates of the fits including a \texttt{limma} fit in \texttt{fit\$fit} and an \texttt{ebayes} statistical fit in \texttt{fit\$eb}. This data can be analyzed like any \texttt{limma} fit and in this example, the column of the fitted coefficients represents the fold-change for our "smoker" vs. "nonsmoker" analysis. Looking at the particular analysis just performed, there appears to be OTUs representing two \textit{Prevotella}, two \textit{Neisseria}, a \textit{Porphyromonas} and a \textit{Leptotrichia} that are differentially abundant. One should check that similarly annotated OTUs are not equally differentially abundant in controls. Alternatively, the user can input a model with their own normalization factors including them directly in the model matrix and specifying the option \texttt{useCSSoffset = FALSE} in fitZig. \subsubsection{Multiple groups} Assuming there are multiple groups it is possible to make use of Limma's topTable functions for F-tests and contrast functions to compare multiple groups and covariates of interest. The output of fitZig includes a 'MLArrayLM' Limma object that can be called on by other functions. When running fitZig by default there is an additional covariate added to the design matrix. The fit and the ultimate design matrix are crucial for contrasts. <>= # maxit=1 is for demonstration purposes settings = zigControl(maxit=1,verbose=FALSE) mod = model.matrix(~bodySite) colnames(mod) = levels(bodySite) # fitting the ZIG model res = fitZig(obj = lungTrim,mod=mod,control=settings) # The output of fitZig contains a list of various useful items. hint: names(res). # # Probably the most useful is the limma 'MLArrayLM' object called fit. zigFit = slot(res,"fit") finalMod = slot(res,"fit")$design contrast.matrix = makeContrasts(BAL.A-BAL.B,OW-PSB,levels=finalMod) fit2 = contrasts.fit(zigFit, contrast.matrix) fit2 = eBayes(fit2) topTable(fit2) # See help pages on decideTests, topTable, topTableF, vennDiagram, etc. @ Further specific details can be found in section 9.3 and beyond of the Limma user guide. The take home message is that to make use of any Limma functions one needs to extract the final model matrix used: \textit{res\$fit\$design} and the MLArrayLM Limma fit object: \textit{res\$fit}. \subsubsection{Exporting fits} Currently functions are being developed to wrap and output results more neatly, but \texttt{MRcoefs}, \texttt{MRtable}, \texttt{MRfulltable} can be used to view coefficient fits and related statistics and export the data with optional output values - see help files to learn how they differ. An important note is that the \texttt{by} variable controls which coefficients are of interest whereas \texttt{coef} determines the display.\\ To only consider features that are found in a large percentage of effectively positive (positive samples + the weight of zero counts included in the Gaussian mixture) use the \textbf{eff} option in the \texttt{MRtables}. <>= taxa = sapply(strsplit(as.character(fData(lungTrim)$taxa),split=";"), function(i){i[length(i)]}) head(MRcoefs(fit,taxa=taxa,coef=2)) @ \subsection{Time series analysis} Implemented in the \texttt{fitTimeSeries} function is a method for calculating time intervals for which bacteria are differentially abundant. Fitting is performed using Smoothing Splines ANOVA (SS-ANOVA), as implemented in the \texttt{gss} package. Given observations at multiple time points for two groups the method calculates a function modeling the difference in abundance across all time. Using group membership permutations weestimate a null distribution of areas under the difference curve for the time intervals of interest and report significant intervals of time. Use of the function for analyses should cite: "Finding regions of interest in high throughput genomics data using smoothing splines" Talukder H, Paulson JN, Bravo HC. (Submitted) For a description of how to perform a time-series / genome based analysis call the \texttt{fitTimeSeries} vignette. <>= # vignette("fitTimeSeries") @ \subsection{Log Normal permutation test} Included is a standard log normal linear model with permutation based p-values permutation. We show the fit for the same model as above using 10 permutations providing p-value resolution to the tenth. The \texttt{coef} parameter refers to the coefficient of interest to test. We first generate the list of significant features. <>= coeffOfInterest = 2 res = fitLogNormal(obj = lungTrim, mod = mod, useCSSoffset = FALSE, B = 10, coef = coeffOfInterest) # extract p.values and adjust for multiple testing # res$p are the p-values calculated through permutation adjustedPvalues = p.adjust(res$p,method="fdr") # extract the absolute fold-change estimates foldChange = abs(res$fit$coef[,coeffOfInterest]) # determine features still significant and order by the sigList = which(adjustedPvalues <= .05) sigList = sigList[order(foldChange[sigList])] # view the top taxa associated with the coefficient of interest. head(taxa[sigList]) @ \subsection{Presence-absence testing} The hypothesis for the implemented presence-absence test is that the proportion/odds of a given feature present is higher/lower among one group of individuals compared to another, and we want to test whether any difference in the proportions observed is significant. We use Fisher's exact test to create a 2x2 contingency table and calculate p-values, odd's ratios, and confidence intervals. \texttt{fitPA} calculates the presence-absence for each organism and returns a table of p-values, odd's ratios, and confidence intervals. The function will accept either a \texttt{MRexperiment} object or matrix. \texttt{MRfulltable} when sent a result of fitZig will also include the results of \texttt{fitPA}. <>= classes = pData(mouseData)$diet res = fitPA(mouseData[1:5,],cl=classes) # Warning - the p-value is calculating 1 despite a high odd's ratio. head(res) @ \subsection{Discovery odds ratio testing} The hypothesis for the implemented discovery test is that the proportion of observed counts for a feature of all counts are comparable between groups. We use Fisher's exact test to create a 2x2 contingency table and calculate p-values, odd's ratios, and confidence intervals. \texttt{fitDO} calculates the proportion of counts for each organism and returns a table of p-values, odd's ratios, and confidence intervals. The function will accept either a \texttt{MRexperiment} object or matrix. <>= classes = pData(mouseData)$diet res = fitDO(mouseData[1:100,],cl=classes,norm=FALSE,log=FALSE) head(res) @ \subsection{Feature correlations} To test the correlations of abundance features, or samples, in a pairwise fashion we have implemented \texttt{correlationTest} and \texttt{correctIndices}. The \texttt{correlationTest} function will calculate basic pearson, spearman, kendall correlation statistics for the rows of the input and report the associated p-values. If a vector of length ncol(obj) it will also calculate the correlation of each row with the associated vector. <>= cors = correlationTest(mouseData[55:60,],norm=FALSE,log=FALSE) head(cors) @ \textbf{Caution:} http://www.ncbi.nlm.nih.gov/pubmed/23028285 \subsection{Unique OTUs or features} To find features absent from any number of classes the function \texttt{uniqueFeatures} provides a table of the feature ids, the number of positive features and reads for each group. Thresholding for the number of positive samples or reads required are options. <>= cl = pData(mouseData)[["diet"]] uniqueFeatures(mouseData,cl,nsamples = 10,nreads = 100) @ \newpage \section{Aggregating counts} Normalization is recommended at the OTU level. However, functions are in place to aggregate the count matrix (normalized or not), based on a particular user defined level. Using the featureData information in the MRexperiment object, calling \texttt{aggregateByTaxonomy} or \texttt{aggTax} on a MRexperiment object and declaring particular featureData column name (i.e. 'genus') will aggregate counts to the desired level with the aggfun function (default colSums). Possible aggfun alternatives include colMeans and colMedians. <>= obj = aggTax(mouseData,lvl='phylum',out='matrix') head(obj[1:5,1:5]) @ Additionally, aggregating samples can be done using the phenoData information in the MRexperiment object. Calling \texttt{aggregateBySample} or \texttt{aggsamp} on a MRexperiment object and declaring a particular phenoData column name (i.e. 'diet') will aggregate counts with the aggfun function (default rowMeans). Possible aggfun alternatives include rowSums and rowMedians. <>= obj = aggSamp(mouseData,fct='mouseID',out='matrix') head(obj[1:5,1:5]) @ The \texttt{aggregateByTaxonomy},\texttt{aggregateBySample}, \texttt{aggTax} \texttt{aggSamp} functions are flexible enough to put in either 1) a matrix with a vector of labels or 2) a MRexperiment object with a vector of labels or featureData column name. The function can also output either a matrix or MRexperiment object. \newpage \section{Visualization of features} To help with visualization and analysis of datasets \texttt{metagenomeSeq} has several plotting functions to gain insight of the dataset's overall structure and particular individual features. An initial interactive exploration of the data can be displayed with the \texttt{display} function. For an overall look at the dataset we provide a number of plots including heatmaps of feature counts: \texttt{plotMRheatmap}, basic feature correlation structures: \texttt{plotCorr}, PCA/MDS coordinates of samples or features: \texttt{plotOrd}, rarefaction effects: \texttt{plotRare} and contingency table style plots: \texttt{plotBubble}. Other plotting functions look at particular features such as the abundance for a single feature: \texttt{plotOTU} and \texttt{plotFeature}, or of multiple features at once: \texttt{plotGenus}. Plotting multiple OTUs with similar annotations allows for additional control of false discoveries. \subsection{Interactive Display} Due to recent advances in the \texttt{interactiveDisplay} package, calling the \texttt{display} function on \texttt{MRexperiment} objects will bring up a browser to explore your data through several interactive visualizations. For more detailed interactive visualizations one might be interested in the shiny-phyloseq package. <>= # Calling display on the MRexperiment object will start a browser session with interactive plots. # require(interactiveDisplay) # display(mouseData) @ \subsection{Structural overview} Many studies begin by comparing the abundance composition across sample or feature phenotypes. Often a first step of data analysis is a heatmap, correlation or co-occurence plot or some other data exploratory method. The following functions have been implemented to provide a first step overview of the data: \begin{enumerate} \item \texttt{plotMRheatmap} - heatmap of abundance estimates (Fig. 4 left) \item \texttt{plotCorr} - heatmap of pairwise correlations (Fig. 4 right) \item \texttt{plotOrd} - PCA/CMDS components (Fig. 5 left) \item \texttt{plotRare} - rarefaction effect (Fig. 5 right) \item \texttt{plotBubble} - contingency table style plot (see help) \end{enumerate} \noindent Each of the above can include phenotypic information in helping to explore the data. Below we show an example of how to create a heatmap and hierarchical clustering of $\log_2$ transformed counts for the 200 OTUs with the largest overall variance. Red values indicate counts close to zero. Row color labels indicate OTU taxonomic class; column color labels indicate diet (green = high fat, yellow = low fat). Notice the samples cluster by diet in these cases and there are obvious clusters. We then plot a correlation matrix for the same features. <>= trials = pData(mouseData)$diet heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))]; heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50) # plotMRheatmap plotMRheatmap(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none", col = heatmapCols,ColSideColors = heatmapColColors) # plotCorr plotCorr(obj=mouseData,n=200,cexRow = 0.25,cexCol = 0.25, trace="none",dendrogram="none",col=heatmapCols) @ Below is an example of plotting CMDS plots of the data and the rarefaction effect at the OTU level. None of the data is removed (we recommend removing outliers typically). <>= cl = factor(pData(mouseData)$diet) # plotOrd - can load vegan and set distfun = vegdist and use dist.method="bray" plotOrd(mouseData,tran=TRUE,usePCA=FALSE,useDist=TRUE,bg=cl,pch=21) # plotRare res = plotRare(mouseData,cl=cl,pch=21,bg=cl) # Linear fits for plotRare / legend tmp=lapply(levels(cl), function(lv) lm(res[,"ident"]~res[,"libSize"]-1, subset=cl==lv)) for(i in 1:length(levels(cl))){ abline(tmp[[i]], col=i) } legend("topleft", c("Diet 1","Diet 2"), text.col=c(1,2),box.col=NA) @ \subsection{Feature specific} Reads clustered with high similarity represent functional or taxonomic units. However, it is possible that reads from the same organism get clustered into multiple OTUs. Following differential abundance analysis. It is important to confirm differential abundance. One way to limit false positives is ensure that the feature is actually abundant (enough positive samples). Another way is to plot the abundances of features similarly annotated. \begin{enumerate} \item \texttt{plotOTU} - abundances of a particular feature by group (Fig. 6 left) \item \texttt{plotGenus} - abundances for several features similarly annotated by group (Fig. 6 right) \item \texttt{plotFeature} - abundances of a particular feature by group (similar to plotOTU, Fig. 7) \end{enumerate} Below we use \texttt{plotOTU} to plot the normalized log(cpt) of a specific OTU annotated as \textit{Neisseria meningitidis}, in particular the 779th row of lungTrim's count matrix. Using \texttt{plotGenus} we plot the normalized log(cpt) of all OTUs annotated as \textit{Neisseria meningitidis}. It would appear that \textit{Neisseria meningitidis} is differentially more abundant in nonsmokers. <>= head(MRtable(fit,coef=2,taxa=1:length(fData(lungTrim)$taxa))) patients=sapply(strsplit(rownames(pData(lungTrim)),split="_"), function(i){ i[3] }) pData(lungTrim)$patients=patients classIndex=list(smoker=which(pData(lungTrim)$SmokingStatus=="Smoker")) classIndex$nonsmoker=which(pData(lungTrim)$SmokingStatus=="NonSmoker") otu = 779 # plotOTU plotOTU(lungTrim,otu=otu,classIndex,main="Neisseria meningitidis") # Now multiple OTUs annotated similarly x = fData(lungTrim)$taxa[otu] otulist = grep(x,fData(lungTrim)$taxa) # plotGenus plotGenus(lungTrim,otulist,classIndex,labs=FALSE, main="Neisseria meningitidis") lablist<- c("S","NS") axis(1, at=seq(1,6,by=1), labels = rep(lablist,times=3)) @ <>= classIndex=list(Western=which(pData(mouseData)$diet=="Western")) classIndex$BK=which(pData(mouseData)$diet=="BK") otuIndex = 8770 # par(mfrow=c(1,2)) dates = pData(mouseData)$date plotFeature(mouseData,norm=FALSE,log=FALSE,otuIndex,classIndex, col=dates,sortby=dates,ylab="Raw reads") @ \newpage \section{Summary} \texttt{metagenomeSeq} is specifically designed for sparse high-throughput sequencing experiments that addresses the analysis of differential abundance for marker-gene survey data. The package, while designed for marker-gene survey datasets, may be appropriate for other sparse data sets for which the zero-inflated Gaussian mixture model may apply. If you make use of the statistical method please cite our paper. If you made use of the manual/software, please cite the manual/software! \subsection{Citing metagenomeSeq} <>= citation("metagenomeSeq") @ \subsection{Session Info} <>= sessionInfo() @ \newpage \section{Appendix} \subsection{Appendix A: MRexperiment internals} The S4 class system in R allows for object oriented definitions. \texttt{metagenomeSeq} makes use of the \texttt{Biobase} package in Bioconductor and their virtual-class, \texttt{eSet}. Building off of \texttt{eSet}, the main S4 class in \texttt{metagenomeSeq} is termed \texttt{MRexperiment}. \texttt{MRexperiment} is a simple extension of \texttt{eSet}, adding a single slot, \texttt{expSummary}. The experiment summary slot is a data frame that includes the depth of coverage and the normalization factors for each sample. Future datasets can be formated as MRexperiment objects and analyzed with relative ease. A \texttt{MRexperiment} object is created by calling \texttt{newMRexperiment}, passing the counts, phenotype and feature data as parameters. We do not include normalization factors or library size in the currently available slot specified for the sample specific phenotype data. All matrices are organized in the \texttt{assayData} slot. All phenotype data (disease status, age, etc.) is stored in \texttt{phenoData} and feature data (OTUs, taxonomic assignment to varying levels, etc.) in \texttt{featureData}. Additional slots are available for reproducibility and annotation. \subsection{Appendix B: Mathematical model} Defining the class comparison of interest as $k(j)=I\{j \in \mathrm{ group } A\}$. The zero-inflated model is defined for the continuity-corrected $\log_2$ of the count data $y_{ij} = \log_2(c_{ij}+1)$ as a mixture of a point mass at zero $I_{\{0\}}(y_{ij})$ and a count distribution $f_{count}(y_{ij};\mu_i, \sigma_i^2) \sim N(\mu_i, \sigma_i^2)$. Given mixture parameters $\pi_{j}$, we have that the density of the zero-inflated Gaussian distribution for feature $i$, in sample $j$ with $S_{j}$ total counts is: \begin{equation} f_{zig}(y_{ij}; \theta ) = \pi_{j}(S_{j}) \cdot I_{\{0\}}(y_{ij}) + (1-\pi_{j}(S_{j})) \cdot f_{count}(y_{ij};\theta) \end{equation} Maximum-likelihood estimates are approximated using an EM algorithm, where we treat mixture membership $\Delta_{ij}=1$ if $y_{ij}$ is generated from the zero point mass as latent indicator variables\cite{EM}. We make use of an EM algorithm to account for the linear relationship between sparsity and depth of coverage. The user can specify within the \texttt{fitZig} function a non-default zero model that accounts for more than simply the depth of coverage (e.g. country, age, any metadata associated with sparsity, etc.). See Figure 8 for the graphical model. \begin{figure} \centerline{\includegraphics[width=.7\textwidth]{metagenomeSeq_figure2.png}} \caption{\footnotesize{Graphical model. Green nodes represent observed variables: $S_j$ is the total number of reads in sample $j$; $k_j$ the case-control status of sample $j$; and $y_{ij}$ the logged normalized counts for feature $i$ in sample $j$. Yellow nodes represent counts obtained from each mixture component: counts come from either a spike-mass at zero, $y_{ij}^0$, or the ``count'' distribution, $y_{ij}^1$. Grey nodes $b_{0i}$, $b_{1i}$ and $\sigma_{i}^2$ represent the estimated overall mean, fold-change and variance of the count distribution component for feature $i$. $\pi_j$, is the mixture proportion for sample $j$ which depends on sequencing depth via a linear model defined by parameters $\beta_0$ and $\beta_1$. The expected value of latent indicator variables $\Delta_{ij}$ give the posterior probability of a count being generated from a spike-mass at zero, i.e. $y_{ij}^0$. We assume $M$ features and $N$ samples.}} \end{figure} More information will be included later. For now, please see the online methods in: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.2658.html \subsection{Appendix C: Calculating the proper percentile} To be included: an overview of the two methods implemented for the data driven percentile calculation and more description below. The choice of the appropriate quantile given is crucial for ensuring that the normalization approach does not introduce normalization-related artifacts in the data. At a high level, the count distribution of samples should all be roughly equivalent and independent of each other up to this quantile under the assumption that, at this range, counts are derived from a common distribution. More information will be included later. For now, please see the online methods in: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.2658.html \newpage \bibliography{metagenomeSeq} \end{document} metagenomeSeq/vignettes/metagenomeSeq.bib0000644000175200017520000000775614710220170021657 0ustar00biocbuildbiocbuild@article{metastats, title={Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples}, volume={11}, journal={PLOS Comp Bio}, publisher={PLOS}, author={White, James and Nagaranjan, Niranjan and Pop, Mihai}, year={2009}} @article{lefse, abstract = {ABSTRACT: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities which is a central problem to the study of metagenomics. 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