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O ߜ.JUݴ5@ 80jf{AAE@ 9I.! -(nv &ʇ ^ۛQ% (= 3.0 with multiple data types
data_dir <- 'path/to/data/directory'
list.files(data_dir) # Should show barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz
data <- Read10X(data.dir = data_dir)
seurat_object = CreateSeuratObject(counts = data$`Gene Expression`)
seurat_object[['Protein']] = CreateAssayObject(counts = data$`Antibody Capture`)
}
}
Seurat/man/CustomDistance.Rd 0000644 0001762 0000144 00000001462 13617632030 015510 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{CustomDistance}
\alias{CustomDistance}
\title{Run a custom distance function on an input data matrix}
\usage{
CustomDistance(my.mat, my.function, ...)
}
\arguments{
\item{my.mat}{A matrix to calculate distance on}
\item{my.function}{A function to calculate distance}
\item{...}{Extra parameters to my.function}
}
\value{
A distance matrix
}
\description{
Run a custom distance function on an input data matrix
}
\examples{
# Define custom distance matrix
manhattan.distance <- function(x, y) return(sum(abs(x-y)))
input.data <- GetAssayData(pbmc_small, assay.type = "RNA", slot = "scale.data")
cell.manhattan.dist <- CustomDistance(input.data, manhattan.distance)
}
\author{
Jean Fan
}
Seurat/man/AverageExpression.Rd 0000644 0001762 0000144 00000003211 13617632030 016207 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{AverageExpression}
\alias{AverageExpression}
\title{Averaged feature expression by identity class}
\usage{
AverageExpression(
object,
assays = NULL,
features = NULL,
return.seurat = FALSE,
add.ident = NULL,
slot = "data",
use.scale = FALSE,
use.counts = FALSE,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{Seurat object}
\item{assays}{Which assays to use. Default is all assays}
\item{features}{Features to analyze. Default is all features in the assay}
\item{return.seurat}{Whether to return the data as a Seurat object. Default is FALSE}
\item{add.ident}{Place an additional label on each cell prior to averaging (very useful if you want to observe cluster averages, separated by replicate, for example)}
\item{slot}{Slot to use; will be overriden by \code{use.scale} and \code{use.counts}}
\item{use.scale}{Use scaled values for feature expression}
\item{use.counts}{Use count values for feature expression}
\item{verbose}{Print messages and show progress bar}
\item{...}{Arguments to be passed to methods such as \code{\link{CreateSeuratObject}}}
}
\value{
Returns a matrix with genes as rows, identity classes as columns.
If return.seurat is TRUE, returns an object of class \code{\link{Seurat}}.
}
\description{
Returns expression for an 'average' single cell in each identity class
}
\details{
Output is in log-space when \code{return.seurat = TRUE}, otherwise it's in non-log space.
Averaging is done in non-log space.
}
\examples{
head(AverageExpression(object = pbmc_small))
}
Seurat/man/TF.IDF.Rd 0000644 0001762 0000144 00000001265 13617632030 013476 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{TF.IDF}
\alias{TF.IDF}
\title{Term frequency-inverse document frequency}
\usage{
TF.IDF(data, verbose = TRUE)
}
\arguments{
\item{data}{Matrix with the raw count data}
\item{verbose}{Print progress}
}
\value{
Returns a matrix with the normalized data
}
\description{
Normalize binary data per cell using the term frequency-inverse document frequency
normalization method (TF-IDF).
This is suitable for the normalization of binary ATAC peak datasets.
}
\examples{
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
mat_norm <- TF.IDF(data = mat)
}
Seurat/man/RunUMAP.Rd 0000644 0001762 0000144 00000016600 13617632030 014012 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{RunUMAP}
\alias{RunUMAP}
\alias{RunUMAP.default}
\alias{RunUMAP.Graph}
\alias{RunUMAP.Seurat}
\title{Run UMAP}
\usage{
RunUMAP(object, ...)
\method{RunUMAP}{default}(
object,
assay = NULL,
umap.method = "uwot",
n.neighbors = 30L,
n.components = 2L,
metric = "cosine",
n.epochs = NULL,
learning.rate = 1,
min.dist = 0.3,
spread = 1,
set.op.mix.ratio = 1,
local.connectivity = 1L,
repulsion.strength = 1,
negative.sample.rate = 5,
a = NULL,
b = NULL,
uwot.sgd = FALSE,
seed.use = 42,
metric.kwds = NULL,
angular.rp.forest = FALSE,
reduction.key = "UMAP_",
verbose = TRUE,
...
)
\method{RunUMAP}{Graph}(
object,
assay = NULL,
umap.method = "umap-learn",
n.components = 2L,
metric = "correlation",
n.epochs = 0L,
learning.rate = 1,
min.dist = 0.3,
spread = 1,
repulsion.strength = 1,
negative.sample.rate = 5L,
a = NULL,
b = NULL,
uwot.sgd = FALSE,
seed.use = 42L,
metric.kwds = NULL,
verbose = TRUE,
reduction.key = "UMAP_",
...
)
\method{RunUMAP}{Seurat}(
object,
dims = NULL,
reduction = "pca",
features = NULL,
graph = NULL,
assay = "RNA",
umap.method = "uwot",
n.neighbors = 30L,
n.components = 2L,
metric = "cosine",
n.epochs = NULL,
learning.rate = 1,
min.dist = 0.3,
spread = 1,
set.op.mix.ratio = 1,
local.connectivity = 1L,
repulsion.strength = 1,
negative.sample.rate = 5L,
a = NULL,
b = NULL,
uwot.sgd = FALSE,
seed.use = 42L,
metric.kwds = NULL,
angular.rp.forest = FALSE,
verbose = TRUE,
reduction.name = "umap",
reduction.key = "UMAP_",
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods and UMAP}
\item{assay}{Assay to pull data for when using \code{features}, or assay used to construct Graph
if running UMAP on a Graph}
\item{umap.method}{UMAP implementation to run. Can be
\describe{
\item{\code{uwot}:}{Runs umap via the uwot R package}
\item{\code{umap-learn}:}{Run the Seurat wrapper of the python umap-learn package}
}}
\item{n.neighbors}{This determines the number of neighboring points used in
local approximations of manifold structure. Larger values will result in more
global structure being preserved at the loss of detailed local structure. In
general this parameter should often be in the range 5 to 50.}
\item{n.components}{The dimension of the space to embed into.}
\item{metric}{metric: This determines the choice of metric used to measure
distance in the input space. A wide variety of metrics are already coded, and
a user defined function can be passed as long as it has been JITd by numba.}
\item{n.epochs}{he number of training epochs to be used in optimizing the low dimensional
embedding. Larger values result in more accurate embeddings. If NULL is specified, a value will
be selected based on the size of the input dataset (200 for large datasets, 500 for small).}
\item{learning.rate}{The initial learning rate for the embedding optimization.}
\item{min.dist}{This controls how tightly the embedding is allowed compress points together.
Larger values ensure embedded points are moreevenly distributed, while smaller values allow the
algorithm to optimise more accurately with regard to local structure. Sensible values are in
the range 0.001 to 0.5.}
\item{spread}{The effective scale of embedded points. In combination with min.dist this
determines how clustered/clumped the embedded points are.}
\item{set.op.mix.ratio}{Interpolate between (fuzzy) union and intersection as the set operation
used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets. Both fuzzy
set operations use the product t-norm. The value of this parameter should be between 0.0 and
1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection.}
\item{local.connectivity}{The local connectivity required - i.e. the number of nearest neighbors
that should be assumed to be connected at a local level. The higher this value the more connected
the manifold becomes locally. In practice this should be not more than the local intrinsic
dimension of the manifold.}
\item{repulsion.strength}{Weighting applied to negative samples in low dimensional embedding
optimization. Values higher than one will result in greater weight being given to negative
samples.}
\item{negative.sample.rate}{The number of negative samples to select per positive sample in the
optimization process. Increasing this value will result in greater repulsive force being applied,
greater optimization cost, but slightly more accuracy.}
\item{a}{More specific parameters controlling the embedding. If NULL, these values are set
automatically as determined by min. dist and spread. Parameter of differentiable approximation of
right adjoint functor.}
\item{b}{More specific parameters controlling the embedding. If NULL, these values are set
automatically as determined by min. dist and spread. Parameter of differentiable approximation of
right adjoint functor.}
\item{uwot.sgd}{Set \code{uwot::umap(fast_sgd = TRUE)}; see \code{\link[uwot]{umap}} for more details}
\item{seed.use}{Set a random seed. By default, sets the seed to 42. Setting
NULL will not set a seed}
\item{metric.kwds}{A dictionary of arguments to pass on to the metric, such as the p value for
Minkowski distance. If NULL then no arguments are passed on.}
\item{angular.rp.forest}{Whether to use an angular random projection forest to initialise the
approximate nearest neighbor search. This can be faster, but is mostly on useful for metric that
use an angular style distance such as cosine, correlation etc. In the case of those metrics
angular forests will be chosen automatically.}
\item{reduction.key}{dimensional reduction key, specifies the string before
the number for the dimension names. UMAP by default}
\item{verbose}{Controls verbosity}
\item{dims}{Which dimensions to use as input features, used only if
\code{features} is NULL}
\item{reduction}{Which dimensional reduction (PCA or ICA) to use for the
UMAP input. Default is PCA}
\item{features}{If set, run UMAP on this subset of features (instead of running on a
set of reduced dimensions). Not set (NULL) by default; \code{dims} must be NULL to run
on features}
\item{graph}{Name of graph on which to run UMAP}
\item{reduction.name}{Name to store dimensional reduction under in the Seurat object}
}
\value{
Returns a Seurat object containing a UMAP representation
}
\description{
Runs the Uniform Manifold Approximation and Projection (UMAP) dimensional
reduction technique. To run, you must first install the umap-learn python
package (e.g. via \code{pip install umap-learn}). Details on this package can be
found here: \url{https://github.com/lmcinnes/umap}. For a more in depth
discussion of the mathematics underlying UMAP, see the ArXiv paper here:
\url{https://arxiv.org/abs/1802.03426}.
}
\examples{
\dontrun{
pbmc_small
# Run UMAP map on first 5 PCs
pbmc_small <- RunUMAP(object = pbmc_small, dims = 1:5)
# Plot results
DimPlot(object = pbmc_small, reduction = 'umap')
}
}
\references{
McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and
Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018
}
Seurat/man/ExpSD.Rd 0000644 0001762 0000144 00000000670 13617632030 013546 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{ExpSD}
\alias{ExpSD}
\title{Calculate the standard deviation of logged values}
\usage{
ExpSD(x)
}
\arguments{
\item{x}{A vector of values}
}
\value{
Returns the standard deviation in log-space
}
\description{
Calculate SD of logged values in non-log space (return answer in log-space)
}
\examples{
ExpSD(x = c(1, 2, 3))
}
Seurat/man/Tool.Rd 0000644 0001762 0000144 00000002716 13617632030 013503 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Tool}
\alias{Tool}
\alias{Tools}
\alias{Tool<-}
\alias{Tool.Seurat}
\alias{Tool<-.Seurat}
\title{Get and set additional tool data}
\usage{
Tool(object, ...)
Tool(object, ...) <- value
\method{Tool}{Seurat}(object, slot = NULL, ...)
\method{Tool}{Seurat}(object, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{Information to be added to tool list}
\item{slot}{Name of tool to pull}
}
\value{
If no additional arguments, returns the names of the tools in the
object; otherwise returns the data placed by the tool requested
}
\description{
Use \code{Tool} to get tool data. If no additional arguments are provided,
will return a vector with the names of tools in the object.
}
\note{
For developers: set tool data using \code{Tool<-}. \code{Tool<-} will
automatically set the name of the tool to the function that called \code{Tool<-},
so each function gets one entry in the tools list and cannot overwrite another
function's entry. The automatic naming will also remove any method identifiers
(eg. RunPCA.Seurat will become RunPCA); please plan accordingly.
}
\examples{
Tool(object = pbmc_small)
\dontrun{
sample.tool.output <- matrix(data = rnorm(n = 16), nrow = 4)
# must be run from within a function
Tool(object = pbmc_small) <- sample.tool.output
}
}
Seurat/man/RunLSI.Rd 0000644 0001762 0000144 00000003524 13617632030 013700 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{RunLSI}
\alias{RunLSI}
\alias{RunLSI.default}
\alias{RunLSI.Assay}
\alias{RunLSI.Seurat}
\title{Run Latent Semantic Indexing on binary count matrix}
\usage{
RunLSI(object, ...)
\method{RunLSI}{default}(
object,
assay = NULL,
n = 50,
reduction.key = "LSI_",
scale.max = NULL,
seed.use = 42,
verbose = TRUE,
...
)
\method{RunLSI}{Assay}(
object,
assay = NULL,
features = NULL,
n = 50,
reduction.key = "LSI_",
scale.max = NULL,
verbose = TRUE,
...
)
\method{RunLSI}{Seurat}(
object,
assay = NULL,
features = NULL,
n = 50,
reduction.key = "LSI_",
reduction.name = "lsi",
scale.max = NULL,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{Seurat object}
\item{...}{Arguments passed to other methods}
\item{assay}{Which assay to use. If NULL, use the default assay}
\item{n}{Number of singular values to compute}
\item{reduction.key}{Key for dimension reduction object}
\item{scale.max}{Clipping value for cell embeddings. Default (NULL) is no clipping.}
\item{seed.use}{Set a random seed. By default, sets the seed to 42. Setting
NULL will not set a seed.}
\item{verbose}{Print messages}
\item{features}{Which features to use. If NULL, use variable features}
\item{reduction.name}{Name for stored dimension reduction object. Default 'lsi'}
}
\description{
For details about stored LSI calculation parameters, see
\code{PrintLSIParams}.
}
\note{
RunLSI is being moved to Signac. Equivalent functionality can be
achieved via the Signac::RunTFIDF and Signac::RunSVD functions;
for more information on Signac, please see
\url{https://github.com/timoast/Signac}
}
\examples{
lsi <- RunLSI(object = pbmc_small, n = 5)
}
Seurat/man/Reductions.Rd 0000644 0001762 0000144 00000001220 13617632030 014672 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{Reductions}
\alias{Reductions}
\title{Pull DimReducs or DimReduc names}
\usage{
Reductions(object, slot = NULL)
}
\arguments{
\item{object}{A Seurat object}
\item{slot}{Name of DimReduc}
}
\value{
If \code{slot} is \code{NULL}, the names of all \code{DimReduc} objects
in this Seurat object. Otherwise, the \code{DimReduc} object requested
}
\description{
Lists the names of \code{\link{DimReduc}} objects present in
a Seurat object. If slot is provided, pulls specified DimReduc object.
}
\examples{
Reductions(object = pbmc_small)
}
Seurat/man/HoverLocator.Rd 0000644 0001762 0000144 00000001560 13617632030 015171 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{HoverLocator}
\alias{HoverLocator}
\title{Hover Locator}
\usage{
HoverLocator(plot, information = NULL, dark.theme = FALSE, ...)
}
\arguments{
\item{plot}{A ggplot2 plot}
\item{information}{An optional dataframe or matrix of extra information to be displayed on hover}
\item{dark.theme}{Plot using a dark theme?}
\item{...}{Extra parameters to be passed to \code{plotly::layout}}
}
\description{
Get quick information from a scatterplot by hovering over points
}
\examples{
\dontrun{
plot <- DimPlot(object = pbmc_small)
HoverLocator(plot = plot, information = FetchData(object = pbmc_small, vars = 'percent.mito'))
}
}
\seealso{
\code{\link[plotly]{layout}} \code{\link[ggplot2]{ggplot_build}}
\code{\link{DimPlot}} \code{\link{FeaturePlot}}
}
Seurat/man/WhichCells.Rd 0000644 0001762 0000144 00000003370 13617632030 014610 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{WhichCells}
\alias{WhichCells}
\alias{WhichCells.Assay}
\alias{WhichCells.Seurat}
\title{Identify cells matching certain criteria}
\usage{
WhichCells(object, ...)
\method{WhichCells}{Assay}(object, cells = NULL, expression, invert = FALSE, ...)
\method{WhichCells}{Seurat}(
object,
cells = NULL,
idents = NULL,
expression,
slot = "data",
invert = FALSE,
downsample = Inf,
seed = 1,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{cells}{Subset of cell names}
\item{expression}{A predicate expression for feature/variable expression, can
evalue anything that can be pulled by \code{FetchData}; please note, you may
need to wrap feature names in backticks (\code{``}) if dashes between numbers
are present in the feature name}
\item{invert}{Invert the selection of cells}
\item{idents}{A vector of identity classes to keep}
\item{slot}{Slot to pull feature data for}
\item{downsample}{Maximum number of cells per identity class, default is \code{Inf};
downsampling will happen after all other operations, including inverting the
cell selection}
\item{seed}{Random seed for downsampling. If NULL, does not set a seed}
}
\value{
A vector of cell names
}
\description{
Returns a list of cells that match a particular set of criteria such as
identity class, high/low values for particular PCs, ect..
}
\examples{
WhichCells(object = pbmc_small, idents = 2)
WhichCells(object = pbmc_small, expression = MS4A1 > 3)
levels(x = pbmc_small)
WhichCells(object = pbmc_small, idents = c(1, 2), invert = TRUE)
}
\seealso{
\code{\link{FetchData}}
}
Seurat/man/SetAssayData.Rd 0000644 0001762 0000144 00000002440 13617632030 015106 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{SetAssayData}
\alias{SetAssayData}
\alias{SetAssayData.Assay}
\alias{SetAssayData.Seurat}
\title{Setter for multimodal data}
\usage{
SetAssayData(object, ...)
\method{SetAssayData}{Assay}(object, slot, new.data, ...)
\method{SetAssayData}{Seurat}(object, slot = "data", new.data, assay = NULL, ...)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{slot}{Where to store the new data}
\item{new.data}{New data to insert}
\item{assay}{Name of assay whose data should be set}
}
\value{
object with the assay data set
}
\description{
Setter for multimodal data
}
\examples{
# Set an Assay slot directly
count.data <- GetAssayData(object = pbmc_small[["RNA"]], slot = "counts")
count.data <- as.matrix(x = count.data + 1)
new.assay <- SetAssayData(object = pbmc_small[["RNA"]], slot = "counts", new.data = count.data)
# Set an Assay slot through the Seurat object
count.data <- GetAssayData(object = pbmc_small[["RNA"]], slot = "counts")
count.data <- as.matrix(x = count.data + 1)
new.seurat.object <- SetAssayData(
object = pbmc_small,
slot = "counts",
new.data = count.data,
assay = "RNA"
)
}
Seurat/man/RenameAssays.Rd 0000644 0001762 0000144 00000000776 13617632030 015165 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{RenameAssays}
\alias{RenameAssays}
\title{Rename assays in a \code{Seurat} object}
\usage{
RenameAssays(object, ...)
}
\arguments{
\item{object}{A \code{Seurat} object}
\item{...}{Named arguments as \code{old.assay = new.assay}}
}
\value{
\code{object} with assays renamed
}
\description{
Rename assays in a \code{Seurat} object
}
\examples{
RenameAssays(object = pbmc_small, RNA = 'rna')
}
Seurat/man/cc.genes.Rd 0000644 0001762 0000144 00000000753 13617632030 014252 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{cc.genes}
\alias{cc.genes}
\title{Cell cycle genes}
\format{A list of two vectors
\describe{
\item{s.genes}{Genes associated with S-phase}
\item{g2m.genes}{Genes associated with G2M-phase}
}}
\source{
\url{http://science.sciencemag.org/content/352/6282/189}
}
\usage{
cc.genes
}
\description{
A list of genes used in cell-cycle regression
}
\keyword{datasets}
Seurat/man/SampleUMI.Rd 0000644 0001762 0000144 00000001440 13617632030 014353 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{SampleUMI}
\alias{SampleUMI}
\title{Sample UMI}
\usage{
SampleUMI(data, max.umi = 1000, upsample = FALSE, verbose = FALSE)
}
\arguments{
\item{data}{Matrix with the raw count data}
\item{max.umi}{Number of UMIs to sample to}
\item{upsample}{Upsamples all cells with fewer than max.umi}
\item{verbose}{Display the progress bar}
}
\value{
Matrix with downsampled data
}
\description{
Downsample each cell to a specified number of UMIs. Includes
an option to upsample cells below specified UMI as well.
}
\examples{
counts = as.matrix(x = GetAssayData(object = pbmc_small, assay = "RNA", slot = "counts"))
downsampled = SampleUMI(data = counts)
head(x = downsampled)
}
Seurat/man/HTOHeatmap.Rd 0000644 0001762 0000144 00000003217 13617632030 014515 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{HTOHeatmap}
\alias{HTOHeatmap}
\title{Hashtag oligo heatmap}
\usage{
HTOHeatmap(
object,
assay = "HTO",
classification = paste0(assay, "_classification"),
global.classification = paste0(assay, "_classification.global"),
ncells = 5000,
singlet.names = NULL,
raster = TRUE
)
}
\arguments{
\item{object}{Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized, and demultiplexing has been run with HTODemux().}
\item{assay}{Hashtag assay name.}
\item{classification}{The naming for metadata column with classification result from HTODemux().}
\item{global.classification}{The slot for metadata column specifying a cell as singlet/doublet/negative.}
\item{ncells}{Number of cells to plot. Default is to choose 5000 cells by random subsampling, to avoid having to draw exceptionally large heatmaps.}
\item{singlet.names}{Namings for the singlets. Default is to use the same names as HTOs.}
\item{raster}{If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on
some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE
if you are encountering that issue (note that plots may take longer to produce/render).}
}
\value{
Returns a ggplot2 plot object.
}
\description{
Draws a heatmap of hashtag oligo signals across singlets/doublets/negative cells. Allows for the visualization of HTO demultiplexing results.
}
\examples{
\dontrun{
object <- HTODemux(object)
HTOHeatmap(object)
}
}
\seealso{
\code{\link{HTODemux}}
}
Seurat/man/Assay-class.Rd 0000644 0001762 0000144 00000002112 13617632030 014737 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{Assay-class}
\alias{Assay-class}
\alias{Assay}
\title{The Assay Class}
\description{
The Assay object is the basic unit of Seurat; each Assay stores raw, normalized, and scaled data
as well as cluster information, variable features, and any other assay-specific metadata.
Assays should contain single cell expression data such as RNA-seq, protein, or imputed expression
data.
}
\section{Slots}{
\describe{
\item{\code{counts}}{Unnormalized data such as raw counts or TPMs}
\item{\code{data}}{Normalized expression data}
\item{\code{scale.data}}{Scaled expression data}
\item{\code{key}}{Key for the Assay}
\item{\code{assay.orig}}{Original assay that this assay is based off of. Used to track
assay provenence}
\item{\code{var.features}}{Vector of features exhibiting high variance across single cells}
\item{\code{meta.features}}{Feature-level metadata}
\item{\code{misc}}{Utility slot for storing additional data associated with the assay}
}}
Seurat/man/IntegrationData-class.Rd 0000644 0001762 0000144 00000001670 13617632030 016744 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{IntegrationData-class}
\alias{IntegrationData-class}
\alias{IntegrationData}
\title{The IntegrationData Class}
\description{
The IntegrationData object is an intermediate storage container used internally throughout the
integration procedure to hold bits of data that are useful downstream.
}
\section{Slots}{
\describe{
\item{\code{neighbors}}{List of neighborhood information for cells (outputs of \code{RANN::nn2})}
\item{\code{weights}}{Anchor weight matrix}
\item{\code{integration.matrix}}{Integration matrix}
\item{\code{anchors}}{Anchor matrix}
\item{\code{offsets}}{The offsets used to enable cell look up in downstream functions}
\item{\code{objects.ncell}}{Number of cells in each object in the object.list}
\item{\code{sample.tree}}{Sample tree used for ordering multi-dataset integration}
}}
Seurat/man/CreateSeuratObject.Rd 0000644 0001762 0000144 00000004426 13617632030 016304 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{CreateSeuratObject}
\alias{CreateSeuratObject}
\title{Create a Seurat object}
\usage{
CreateSeuratObject(
counts,
project = "SeuratProject",
assay = "RNA",
min.cells = 0,
min.features = 0,
names.field = 1,
names.delim = "_",
meta.data = NULL
)
}
\arguments{
\item{counts}{Unnormalized data such as raw counts or TPMs}
\item{project}{Sets the project name for the Seurat object.}
\item{assay}{Name of the assay corresponding to the initial input data.}
\item{min.cells}{Include features detected in at least this many cells. Will
subset the counts matrix as well. To reintroduce excluded features, create a
new object with a lower cutoff.}
\item{min.features}{Include cells where at least this many features are
detected.}
\item{names.field}{For the initial identity class for each cell, choose this field from the
cell's name. E.g. If your cells are named as BARCODE_CLUSTER_CELLTYPE in the input matrix, set
names.field to 3 to set the initial identities to CELLTYPE.}
\item{names.delim}{For the initial identity class for each cell, choose this delimiter from the
cell's column name. E.g. If your cells are named as BARCODE-CLUSTER-CELLTYPE, set this to "-" to
separate the cell name into its component parts for picking the relevant field.}
\item{meta.data}{Additional cell-level metadata to add to the Seurat object. Should be a data
frame where the rows are cell names and the columns are additional metadata fields.}
}
\description{
Create a Seurat object from a feature (e.g. gene) expression matrix. The expected format of the
input matrix is features x cells.
}
\details{
Note: In previous versions (<3.0), this function also accepted a parameter to set the expression
threshold for a 'detected' feature (gene). This functionality has been removed to simplify the
initialization process/assumptions. If you would still like to impose this threshold for your
particular dataset, simply filter the input expression matrix before calling this function.
}
\examples{
pbmc_raw <- read.table(
file = system.file('extdata', 'pbmc_raw.txt', package = 'Seurat'),
as.is = TRUE
)
pbmc_small <- CreateSeuratObject(counts = pbmc_raw)
pbmc_small
}
Seurat/man/CellsByIdentities.Rd 0000644 0001762 0000144 00000001306 13617632030 016137 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{CellsByIdentities}
\alias{CellsByIdentities}
\title{Get cell names grouped by identity class}
\usage{
CellsByIdentities(object, idents = NULL, cells = NULL)
}
\arguments{
\item{object}{A Seurat object}
\item{idents}{A vector of identity class levels to limit resulting list to;
defaults to all identity class levels}
\item{cells}{A vector of cells to grouping to}
}
\value{
A named list where names are identity classes and values are vectors
of cells beloning to that class
}
\description{
Get cell names grouped by identity class
}
\examples{
CellsByIdentities(object = pbmc_small)
}
Seurat/man/Cells.Rd 0000644 0001762 0000144 00000000722 13617632030 013623 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Cells}
\alias{Cells}
\alias{Cells.default}
\alias{Cells.DimReduc}
\title{Get cells present in an object}
\usage{
Cells(x)
\method{Cells}{default}(x)
\method{Cells}{DimReduc}(x)
}
\arguments{
\item{x}{An object}
}
\value{
A vector of cell names
}
\description{
Get cells present in an object
}
\examples{
Cells(x = pbmc_small)
}
Seurat/man/ReadAlevin.Rd 0000644 0001762 0000144 00000001345 13617632030 014575 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{ReadAlevin}
\alias{ReadAlevin}
\title{Load in data from Alevin pipeline}
\usage{
ReadAlevin(base.path)
}
\arguments{
\item{base.path}{Directory containing the alevin/quant_mat*
files provided by Alevin.}
}
\value{
Returns a matrix with rows and columns labeled
}
\description{
Enables easy loading of binary format matrix provided by Alevin
}
\examples{
\dontrun{
data_dir <- 'path/to/output/directory'
list.files(data_dir) # Should show alevin/quants_mat* files
expression_matrix <- ReadAlevin(base.path = data_dir)
seurat_object = CreateSeuratObject(counts = expression_matrix)
}
}
\author{
Avi Srivastava
}
Seurat/man/LogSeuratCommand.Rd 0000644 0001762 0000144 00000001167 13617632030 015771 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{LogSeuratCommand}
\alias{LogSeuratCommand}
\title{Log a command}
\usage{
LogSeuratCommand(object, return.command = FALSE)
}
\arguments{
\item{object}{Name of Seurat object}
\item{return.command}{Return a \link{SeuratCommand} object instead}
}
\value{
If \code{return.command}, returns a SeuratCommand object. Otherwise,
returns the Seurat object with command stored
}
\description{
Logs command run, storing the name, timestamp, and argument list. Stores in
the Seurat object
}
\seealso{
\code{\link{Command}}
}
Seurat/man/Read10X_h5.Rd 0000644 0001762 0000144 00000001325 13617632030 014321 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{Read10X_h5}
\alias{Read10X_h5}
\title{Read 10X hdf5 file}
\usage{
Read10X_h5(filename, use.names = TRUE, unique.features = TRUE)
}
\arguments{
\item{filename}{Path to h5 file}
\item{use.names}{Label row names with feature names rather than ID numbers.}
\item{unique.features}{Make feature names unique (default TRUE)}
}
\value{
Returns a sparse matrix with rows and columns labeled. If multiple
genomes are present, returns a list of sparse matrices (one per genome).
}
\description{
Read count matrix from 10X CellRanger hdf5 file.
This can be used to read both scATAC-seq and scRNA-seq matrices.
}
Seurat/man/subset.Seurat.Rd 0000644 0001762 0000144 00000002361 13617632030 015331 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{[.Seurat}
\alias{[.Seurat}
\alias{subset.Seurat}
\alias{subset}
\title{Subset a Seurat object}
\usage{
\method{[}{Seurat}(x, i, j, ...)
\method{subset}{Seurat}(x, subset, cells = NULL, features = NULL, idents = NULL, ...)
}
\arguments{
\item{x}{Seurat object to be subsetted}
\item{i, features}{A vector of features to keep}
\item{j, cells}{A vector of cells to keep}
\item{...}{Extra parameters passed to \code{\link{WhichCells}},
such as \code{slot}, \code{invert}, or \code{downsample}}
\item{subset}{Logical expression indicating features/variables to keep}
\item{idents}{A vector of identity classes to keep}
}
\value{
A subsetted Seurat object
}
\description{
Subset a Seurat object
}
\examples{
pbmc_small[VariableFeatures(object = pbmc_small), ]
pbmc_small[, 1:10]
subset(x = pbmc_small, subset = MS4A1 > 4)
subset(x = pbmc_small, subset = `DLGAP1-AS1` > 2)
subset(x = pbmc_small, idents = '0', invert = TRUE)
subset(x = pbmc_small, subset = MS4A1 > 3, slot = 'counts')
subset(x = pbmc_small, features = VariableFeatures(object = pbmc_small))
}
\seealso{
\code{\link[base]{subset}} \code{\link{WhichCells}}
}
Seurat/man/OldWhichCells.Rd 0000644 0001762 0000144 00000003701 13617632030 015245 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{OldWhichCells}
\alias{OldWhichCells}
\alias{OldWhichCells.Assay}
\alias{OldWhichCells.Seurat}
\title{Identify cells matching certain criteria}
\usage{
OldWhichCells(object, ...)
\method{OldWhichCells}{Assay}(
object,
cells,
subset.name = NULL,
low.threshold = -Inf,
high.threshold = Inf,
accept.value = NULL,
...
)
\method{OldWhichCells}{Seurat}(
object,
cells = NULL,
subset.name = NULL,
low.threshold = -Inf,
high.threshold = Inf,
accept.value = NULL,
ident.keep = NULL,
ident.remove = NULL,
max.cells.per.ident = Inf,
random.seed = 1,
assay = NULL,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods and \code{FetchData}}
\item{cells}{Subset of cell names}
\item{subset.name}{Parameter to subset on. Eg, the name of a gene, PC_1, a
column name in object@meta.data, etc. Any argument that can be retreived
using FetchData}
\item{low.threshold}{Low cutoff for the parameter (default is -Inf)}
\item{high.threshold}{High cutoff for the parameter (default is Inf)}
\item{accept.value}{Returns all cells with the subset name equal to this value}
\item{ident.keep}{Create a cell subset based on the provided identity classes}
\item{ident.remove}{Subtract out cells from these identity classes (used for
filtration)}
\item{max.cells.per.ident}{Can be used to downsample the data to a certain
max per cell ident. Default is INF.}
\item{random.seed}{Random seed for downsampling}
\item{assay}{Which assay to filter on}
}
\value{
A vector of cell names
}
\description{
Returns a list of cells that match a particular set of criteria such as
identity class, high/low values for particular PCs, ect..
}
\examples{
\dontrun{
OldWhichCells(object = pbmc_small, ident.keep = 2)
}
}
\seealso{
\code{\link{FetchData}}
}
Seurat/man/GetAssayData.Rd 0000644 0001762 0000144 00000002200 13617632030 015064 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{GetAssayData}
\alias{GetAssayData}
\alias{GetAssayData.Assay}
\alias{GetAssayData.Seurat}
\title{General accessor function for the Assay class}
\usage{
GetAssayData(object, ...)
\method{GetAssayData}{Assay}(object, slot = "data", ...)
\method{GetAssayData}{Seurat}(object, slot = "data", assay = NULL, ...)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{slot}{Specific information to pull (i.e. counts, data, scale.data, ...)}
\item{assay}{Name of assay to pull data from}
}
\value{
Returns info from requested slot
}
\description{
This function can be used to pull information from any of the slots in the Assay class. For
example, pull one of the data matrices("counts", "data", or "scale.data").
}
\examples{
# Get the data directly from an Assay object
GetAssayData(object = pbmc_small[["RNA"]], slot = "data")[1:5,1:5]
# Get the data from a specific Assay in a Seurat object
GetAssayData(object = pbmc_small, assay = "RNA", slot = "data")[1:5,1:5]
}
Seurat/man/SeuratTheme.Rd 0000644 0001762 0000144 00000006102 13617632030 015005 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{SeuratTheme}
\alias{SeuratTheme}
\alias{DarkTheme}
\alias{FontSize}
\alias{NoAxes}
\alias{NoLegend}
\alias{NoGrid}
\alias{SeuratAxes}
\alias{SpatialTheme}
\alias{RestoreLegend}
\alias{RotatedAxis}
\alias{BoldTitle}
\alias{WhiteBackground}
\title{Seurat Themes}
\usage{
SeuratTheme()
DarkTheme(...)
FontSize(
x.text = NULL,
y.text = NULL,
x.title = NULL,
y.title = NULL,
main = NULL,
...
)
NoAxes(..., keep.text = FALSE, keep.ticks = FALSE)
NoLegend(...)
NoGrid(...)
SeuratAxes(...)
SpatialTheme(...)
RestoreLegend(..., position = "right")
RotatedAxis(...)
BoldTitle(...)
WhiteBackground(...)
}
\arguments{
\item{...}{Extra parameters to be passed to \code{theme}}
\item{x.text, y.text}{X and Y axis text sizes}
\item{x.title, y.title}{X and Y axis title sizes}
\item{main}{Plot title size}
\item{keep.text}{Keep axis text}
\item{keep.ticks}{Keep axis ticks}
\item{position}{A position to restore the legend to}
}
\value{
A ggplot2 theme object
}
\description{
Various themes to be applied to ggplot2-based plots
\describe{
\item{\code{SeuratTheme}}{The curated Seurat theme, consists of ...}
\item{\code{DarkTheme}}{A dark theme, axes and text turn to white, the background becomes black}
\item{\code{NoAxes}}{Removes axis lines, text, and ticks}
\item{\code{NoLegend}}{Removes the legend}
\item{\code{FontSize}}{Sets axis and title font sizes}
\item{\code{NoGrid}}{Removes grid lines}
\item{\code{SeuratAxes}}{Set Seurat-style axes}
\item{\code{SpatialTheme}}{A theme designed for spatial visualizations (eg \code{\link{PolyFeaturePlot}}, \code{\link{PolyDimPlot}})}
\item{\code{RestoreLegend}}{Restore a legend after removal}
\item{\code{RotatedAxis}}{Rotate X axis text 45 degrees}
\item{\code{BoldTitle}}{Enlarges and emphasizes the title}
}
}
\examples{
# Generate a plot with a dark theme
library(ggplot2)
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
p + DarkTheme(legend.position = 'none')
# Generate a plot with no axes
library(ggplot2)
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
p + NoAxes()
# Generate a plot with no legend
library(ggplot2)
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
p + NoLegend()
# Generate a plot with no grid lines
library(ggplot2)
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
p + NoGrid()
}
\seealso{
\code{\link[ggplot2]{theme}}
}
Seurat/man/FindNeighbors.Rd 0000644 0001762 0000144 00000007217 13617632030 015310 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/clustering.R
\name{FindNeighbors}
\alias{FindNeighbors}
\alias{FindNeighbors.default}
\alias{FindNeighbors.Assay}
\alias{FindNeighbors.dist}
\alias{FindNeighbors.Seurat}
\title{SNN Graph Construction}
\usage{
FindNeighbors(object, ...)
\method{FindNeighbors}{default}(
object,
distance.matrix = FALSE,
k.param = 20,
compute.SNN = TRUE,
prune.SNN = 1/15,
nn.method = "rann",
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
force.recalc = FALSE,
...
)
\method{FindNeighbors}{Assay}(
object,
features = NULL,
k.param = 20,
compute.SNN = TRUE,
prune.SNN = 1/15,
nn.method = "rann",
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
force.recalc = FALSE,
...
)
\method{FindNeighbors}{dist}(
object,
k.param = 20,
compute.SNN = TRUE,
prune.SNN = 1/15,
nn.method = "rann",
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
force.recalc = FALSE,
...
)
\method{FindNeighbors}{Seurat}(
object,
reduction = "pca",
dims = 1:10,
assay = NULL,
features = NULL,
k.param = 20,
compute.SNN = TRUE,
prune.SNN = 1/15,
nn.method = "rann",
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
force.recalc = FALSE,
do.plot = FALSE,
graph.name = NULL,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{distance.matrix}{Boolean value of whether the provided matrix is a
distance matrix; note, for objects of class \code{dist}, this parameter will
be set automatically}
\item{k.param}{Defines k for the k-nearest neighbor algorithm}
\item{compute.SNN}{also compute the shared nearest neighbor graph}
\item{prune.SNN}{Sets the cutoff for acceptable Jaccard index when
computing the neighborhood overlap for the SNN construction. Any edges with
values less than or equal to this will be set to 0 and removed from the SNN
graph. Essentially sets the strigency of pruning (0 --- no pruning, 1 ---
prune everything).}
\item{nn.method}{Method for nearest neighbor finding. Options include: rann,
annoy}
\item{annoy.metric}{Distance metric for annoy. Options include: euclidean,
cosine, manhattan, and hamming}
\item{nn.eps}{Error bound when performing nearest neighbor seach using RANN;
default of 0.0 implies exact nearest neighbor search}
\item{verbose}{Whether or not to print output to the console}
\item{force.recalc}{Force recalculation of SNN.}
\item{features}{Features to use as input for building the SNN}
\item{reduction}{Reduction to use as input for building the SNN}
\item{dims}{Dimensions of reduction to use as input}
\item{assay}{Assay to use in construction of SNN}
\item{do.plot}{Plot SNN graph on tSNE coordinates}
\item{graph.name}{Optional naming parameter for stored SNN graph. Default is
assay.name_snn.}
}
\value{
Returns the object with object@snn filled
}
\description{
Constructs a Shared Nearest Neighbor (SNN) Graph for a given dataset. We
first determine the k-nearest neighbors of each cell. We use this knn graph
to construct the SNN graph by calculating the neighborhood overlap
(Jaccard index) between every cell and its k.param nearest neighbors.
}
\examples{
pbmc_small
# Compute an SNN on the gene expression level
pbmc_small <- FindNeighbors(pbmc_small, features = VariableFeatures(object = pbmc_small))
# More commonly, we build the SNN on a dimensionally reduced form of the data
# such as the first 10 principle components.
pbmc_small <- FindNeighbors(pbmc_small, reduction = "pca", dims = 1:10)
}
Seurat/man/StopCellbrowser.Rd 0000644 0001762 0000144 00000000462 13617632030 015713 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{StopCellbrowser}
\alias{StopCellbrowser}
\title{Stop Cellbrowser web server}
\usage{
StopCellbrowser()
}
\description{
Stop Cellbrowser web server
}
\examples{
\dontrun{
StopCellbrowser()
}
}
Seurat/man/Seurat-class.Rd 0000644 0001762 0000144 00000003701 13617632030 015127 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{Seurat-class}
\alias{Seurat-class}
\alias{Seurat}
\title{The Seurat Class}
\description{
The Seurat object is a representation of single-cell expression data for R; each Seurat
object revolves around a set of cells and consists of one or more \code{\link{Assay-class}}
objects, or individual representations of expression data (eg. RNA-seq, ATAC-seq, etc).
These assays can be reduced from their high-dimensional state to a lower-dimension state
and stored as \code{\link{DimReduc-class}} objects. Seurat objects also store additional
meta data, both at the cell and feature level (contained within individual assays). The
object was designed to be as self-contained as possible, and easily extendible to new methods.
}
\section{Slots}{
\describe{
\item{\code{assays}}{A list of assays for this project}
\item{\code{meta.data}}{Contains meta-information about each cell, starting with number of genes detected (nGene)
and the original identity class (orig.ident); more information is added using \code{AddMetaData}}
\item{\code{active.assay}}{Name of the active, or default, assay; settable using \code{\link{DefaultAssay}}}
\item{\code{active.ident}}{The active cluster identity for this Seurat object; settable using \code{\link{Idents}}}
\item{\code{graphs}}{A list of \code{\link{Graph-class}} objects}
\item{\code{neighbors}}{...}
\item{\code{reductions}}{A list of dimmensional reduction objects for this object}
\item{\code{project.name}}{Name of the project}
\item{\code{misc}}{A list of miscellaneous information}
\item{\code{version}}{Version of Seurat this object was built under}
\item{\code{commands}}{A list of logged commands run on this \code{Seurat} object}
\item{\code{tools}}{A list of miscellaneous data generated by other tools, should be filled by developers only using \code{\link{Tool}<-}}
}}
Seurat/man/AddMetaData.Rd 0000644 0001762 0000144 00000003034 13617632030 014651 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{AddMetaData}
\alias{AddMetaData}
\alias{SeuratAccess}
\alias{AddMetaData.Assay}
\alias{AddMetaData.Seurat}
\alias{[[<-,Assay-method}
\alias{[[<-,Seurat-method}
\title{Add in metadata associated with either cells or features.}
\usage{
AddMetaData(object, metadata, col.name = NULL)
\method{AddMetaData}{Assay}(object, metadata, col.name = NULL)
\method{AddMetaData}{Seurat}(object, metadata, col.name = NULL)
\S4method{[[}{Assay}(x, i, j, ...) <- value
\S4method{[[}{Seurat}(x, i, j, ...) <- value
}
\arguments{
\item{x, object}{An object}
\item{i, col.name}{Name to store metadata or object as}
\item{j}{Ignored}
\item{...}{Arguments passed to other methods}
\item{value, metadata}{Metadata or object to add}
}
\value{
An object with metadata or and object added
}
\description{
Adds additional data to the object. Can be any piece of information
associated with a cell (examples include read depth, alignment rate,
experimental batch, or subpopulation identity) or feature (ENSG name,
variance). To add cell level information, add to the Seurat object. If adding
feature-level metadata, add to the Assay object (e.g. object[["RNA"]]))
}
\examples{
cluster_letters <- LETTERS[Idents(object = pbmc_small)]
names(cluster_letters) <- colnames(x = pbmc_small)
pbmc_small <- AddMetaData(
object = pbmc_small,
metadata = cluster_letters,
col.name = 'letter.idents'
)
head(x = pbmc_small[[]])
}
Seurat/man/DoHeatmap.Rd 0000644 0001762 0000144 00000004513 13617632030 014425 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{DoHeatmap}
\alias{DoHeatmap}
\title{Feature expression heatmap}
\usage{
DoHeatmap(
object,
features = NULL,
cells = NULL,
group.by = "ident",
group.bar = TRUE,
group.colors = NULL,
disp.min = -2.5,
disp.max = NULL,
slot = "scale.data",
assay = NULL,
label = TRUE,
size = 5.5,
hjust = 0,
angle = 45,
raster = TRUE,
draw.lines = TRUE,
lines.width = NULL,
group.bar.height = 0.02,
combine = TRUE
)
}
\arguments{
\item{object}{Seurat object}
\item{features}{A vector of features to plot, defaults to \code{VariableFeatures(object = object)}}
\item{cells}{A vector of cells to plot}
\item{group.by}{A vector of variables to group cells by; pass 'ident' to group by cell identity classes}
\item{group.bar}{Add a color bar showing group status for cells}
\item{group.colors}{Colors to use for the color bar}
\item{disp.min}{Minimum display value (all values below are clipped)}
\item{disp.max}{Maximum display value (all values above are clipped); defaults to 2.5
if \code{slot} is 'scale.data', 6 otherwise}
\item{slot}{Data slot to use, choose from 'raw.data', 'data', or 'scale.data'}
\item{assay}{Assay to pull from}
\item{label}{Label the cell identies above the color bar}
\item{size}{Size of text above color bar}
\item{hjust}{Horizontal justification of text above color bar}
\item{angle}{Angle of text above color bar}
\item{raster}{If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on
some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE
if you are encountering that issue (note that plots may take longer to produce/render).}
\item{draw.lines}{Include white lines to separate the groups}
\item{lines.width}{Integer number to adjust the width of the separating white lines.
Corresponds to the number of "cells" between each group.}
\item{group.bar.height}{Scale the height of the color bar}
\item{combine}{Combine plots into a single gg object; note that if TRUE; themeing will not work
when plotting multiple dimensions}
}
\value{
A ggplot object
}
\description{
Draws a heatmap of single cell feature expression.
}
\examples{
DoHeatmap(object = pbmc_small)
}
Seurat/man/CalculateBarcodeInflections.Rd 0000644 0001762 0000144 00000004577 13617632030 020150 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{CalculateBarcodeInflections}
\alias{CalculateBarcodeInflections}
\title{Calculate the Barcode Distribution Inflection}
\usage{
CalculateBarcodeInflections(
object,
barcode.column = "nCount_RNA",
group.column = "orig.ident",
threshold.low = NULL,
threshold.high = NULL
)
}
\arguments{
\item{object}{Seurat object}
\item{barcode.column}{Column to use as proxy for barcodes ("nCount_RNA" by default)}
\item{group.column}{Column to group by ("orig.ident" by default)}
\item{threshold.low}{Ignore barcodes of rank below this threshold in inflection calculation}
\item{threshold.high}{Ignore barcodes of rank above thisf threshold in inflection calculation}
}
\value{
Returns Seurat object with a new list in the `tools` slot, `CalculateBarcodeInflections` with values:
* `barcode_distribution` - contains the full barcode distribution across the entire dataset
* `inflection_points` - the calculated inflection points within the thresholds
* `threshold_values` - the provided (or default) threshold values to search within for inflections
* `cells_pass` - the cells that pass the inflection point calculation
}
\description{
This function calculates an adaptive inflection point ("knee") of the barcode distribution
for each sample group. This is useful for determining a threshold for removing
low-quality samples.
}
\details{
The function operates by calculating the slope of the barcode number vs. rank
distribution, and then finding the point at which the distribution changes most
steeply (the "knee"). Of note, this calculation often must be restricted as to the
range at which it performs, so `threshold` parameters are provided to restrict the
range of the calculation based on the rank of the barcodes. [BarcodeInflectionsPlot()]
is provided as a convenience function to visualize and test different thresholds and
thus provide more sensical end results.
See [BarcodeInflectionsPlot()] to visualize the calculated inflection points and
[SubsetByBarcodeInflections()] to subsequently subset the Seurat object.
}
\examples{
CalculateBarcodeInflections(pbmc_small, group.column = 'groups')
}
\seealso{
\code{\link{BarcodeInflectionsPlot}} \code{\link{SubsetByBarcodeInflections}}
}
\author{
Robert A. Amezquita, \email{robert.amezquita@fredhutch.org}
}
Seurat/man/CollapseSpeciesExpressionMatrix.Rd 0000644 0001762 0000144 00000003000 13617632030 021074 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{CollapseSpeciesExpressionMatrix}
\alias{CollapseSpeciesExpressionMatrix}
\title{Slim down a multi-species expression matrix, when only one species is primarily of interenst.}
\usage{
CollapseSpeciesExpressionMatrix(
object,
prefix = "HUMAN_",
controls = "MOUSE_",
ncontrols = 100
)
}
\arguments{
\item{object}{A UMI count matrix. Should contain rownames that start with
the ensuing arguments prefix.1 or prefix.2}
\item{prefix}{The prefix denoting rownames for the species of interest.
Default is "HUMAN_". These rownames will have this prefix removed in the returned matrix.}
\item{controls}{The prefix denoting rownames for the species of 'negative
control' cells. Default is "MOUSE_".}
\item{ncontrols}{How many of the most highly expressed (average) negative
control features (by default, 100 mouse genes), should be kept? All other
rownames starting with prefix.2 are discarded.}
}
\value{
A UMI count matrix. Rownames that started with \code{prefix} have this
prefix discarded. For rownames starting with \code{controls}, only the
\code{ncontrols} most highly expressed features are kept, and the
prefix is kept. All other rows are retained.
}
\description{
Valuable for CITE-seq analyses, where we typically spike in rare populations of 'negative control' cells from a different species.
}
\examples{
\dontrun{
cbmc.rna.collapsed <- CollapseSpeciesExpressionMatrix(cbmc.rna)
}
}
Seurat/man/PCASigGenes.Rd 0000644 0001762 0000144 00000002052 13617632030 014607 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dimensional_reduction.R
\name{PCASigGenes}
\alias{PCASigGenes}
\title{Significant genes from a PCA}
\usage{
PCASigGenes(
object,
pcs.use,
pval.cut = 0.1,
use.full = FALSE,
max.per.pc = NULL
)
}
\arguments{
\item{object}{Seurat object}
\item{pcs.use}{PCS to use.}
\item{pval.cut}{P-value cutoff}
\item{use.full}{Use the full list of genes (from the projected PCA). Assumes
that \code{ProjectDim} has been run. Currently, must be set to FALSE.}
\item{max.per.pc}{Maximum number of genes to return per PC. Used to avoid genes from one PC dominating the entire analysis.}
}
\value{
A vector of genes whose p-values are statistically significant for
at least one of the given PCs.
}
\description{
Returns a set of genes, based on the JackStraw analysis, that have
statistically significant associations with a set of PCs.
}
\examples{
PCASigGenes(pbmc_small, pcs.use = 1:2)
}
\seealso{
\code{\link{ProjectDim}} \code{\link{JackStraw}}
}
Seurat/man/RunCCA.Rd 0000644 0001762 0000144 00000005040 13617632030 013632 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{RunCCA}
\alias{RunCCA}
\alias{RunCCA.default}
\alias{RunCCA.Seurat}
\title{Perform Canonical Correlation Analysis}
\usage{
RunCCA(object1, object2, ...)
\method{RunCCA}{default}(
object1,
object2,
standardize = TRUE,
num.cc = 20,
seed.use = 42,
verbose = FALSE,
...
)
\method{RunCCA}{Seurat}(
object1,
object2,
assay1 = NULL,
assay2 = NULL,
num.cc = 20,
features = NULL,
renormalize = FALSE,
rescale = FALSE,
compute.gene.loadings = TRUE,
add.cell.id1 = NULL,
add.cell.id2 = NULL,
verbose = TRUE,
...
)
}
\arguments{
\item{object1}{First Seurat object}
\item{object2}{Second Seurat object.}
\item{...}{Extra parameters (passed onto MergeSeurat in case with two objects
passed, passed onto ScaleData in case with single object and rescale.groups
set to TRUE)}
\item{standardize}{Standardize matrices - scales columns to have unit variance
and mean 0}
\item{num.cc}{Number of canonical vectors to calculate}
\item{seed.use}{Random seed to set. If NULL, does not set a seed}
\item{verbose}{Show progress messages}
\item{assay1, assay2}{Assays to pull from in the first and second objects, respectively}
\item{features}{Set of genes to use in CCA. Default is the union of both
the variable features sets present in both objects.}
\item{renormalize}{Renormalize raw data after merging the objects. If FALSE,
merge the data matrices also.}
\item{rescale}{Rescale the datasets prior to CCA. If FALSE, uses existing data in the scale data slots.}
\item{compute.gene.loadings}{Also compute the gene loadings. NOTE - this will
scale every gene in the dataset which may impose a high memory cost.}
\item{add.cell.id1, add.cell.id2}{Add ...}
}
\value{
Returns a combined Seurat object with the CCA results stored.
}
\description{
Runs a canonical correlation analysis using a diagonal implementation of CCA.
For details about stored CCA calculation parameters, see
\code{PrintCCAParams}.
}
\examples{
pbmc_small
# As CCA requires two datasets, we will split our test object into two just for this example
pbmc1 <- subset(pbmc_small, cells = colnames(pbmc_small)[1:40])
pbmc2 <- subset(pbmc_small, cells = colnames(x = pbmc_small)[41:80])
pbmc1[["group"]] <- "group1"
pbmc2[["group"]] <- "group2"
pbmc_cca <- RunCCA(object1 = pbmc1, object2 = pbmc2)
# Print results
print(x = pbmc_cca[["cca"]])
}
\seealso{
\code{\link{merge.Seurat}}
}
Seurat/man/ALRAChooseKPlot.Rd 0000644 0001762 0000144 00000002316 13617632030 015414 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{ALRAChooseKPlot}
\alias{ALRAChooseKPlot}
\title{ALRA Approximate Rank Selection Plot}
\usage{
ALRAChooseKPlot(object, start = 0, combine = TRUE)
}
\arguments{
\item{object}{Seurat object}
\item{start}{Index to start plotting singular value spacings from.
The transition from "signal" to "noise" in the is hard to see because the
first singular value spacings are so large. Nicer visualizations result from
skipping the first few. If set to 0 (default) starts from k/2.}
\item{combine}{Combine plots into a single gg object; note that if TRUE,
themeing will not work when plotting multiple features}
}
\value{
A list of 3 ggplot objects splotting the singular values, the
spacings of the singular values, and the p-values of the singular values.
}
\description{
Plots the results of the approximate rank selection process for ALRA.
}
\note{
ALRAChooseKPlot and associated functions are being moved to SeuratWrappers;
for more information on SeuratWrappers, please see \url{https://github.com/satijalab/seurat-wrappers}
}
\seealso{
\code{\link{RunALRA}}
}
\author{
Jun Zhao, George Linderman
}
Seurat/man/oldseurat-class.Rd 0000644 0001762 0000144 00000004761 13617632030 015675 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{seurat-class}
\alias{seurat-class}
\alias{seurat}
\title{The Seurat Class}
\description{
The Seurat object is the center of each single cell analysis. It stores all information
associated with the dataset, including data, annotations, analyes, etc. All that is needed
to construct a Seurat object is an expression matrix (rows are genes, columns are cells), which
should be log-scale
}
\details{
Each Seurat object has a number of slots which store information. Key slots to access
are listed below.
}
\section{Slots}{
\describe{
\item{\code{raw.data}}{The raw project data}
\item{\code{data}}{The normalized expression matrix (log-scale)}
\item{\code{scale.data}}{scaled (default is z-scoring each gene) expression matrix; used for dimmensional reduction and heatmap visualization}
\item{\code{var.genes}}{Vector of genes exhibiting high variance across single cells}
\item{\code{is.expr}}{Expression threshold to determine if a gene is expressed (0 by default)}
\item{\code{ident}}{THe 'identity class' for each cell}
\item{\code{meta.data}}{Contains meta-information about each cell, starting with number of genes detected (nGene)
and the original identity class (orig.ident); more information is added using \code{AddMetaData}}
\item{\code{project.name}}{Name of the project (for record keeping)}
\item{\code{dr}}{List of stored dimmensional reductions; named by technique}
\item{\code{assay}}{List of additional assays for multimodal analysis; named by technique}
\item{\code{hvg.info}}{The output of the mean/variability analysis for all genes}
\item{\code{imputed}}{Matrix of imputed gene scores}
\item{\code{cell.names}}{Names of all single cells (column names of the expression matrix)}
\item{\code{cluster.tree}}{List where the first element is a phylo object containing the phylogenetic tree relating different identity classes}
\item{\code{snn}}{Spare matrix object representation of the SNN graph}
\item{\code{calc.params}}{Named list to store all calculation-related parameter choices}
\item{\code{kmeans}}{Stores output of gene-based clustering from \code{DoKMeans}}
\item{\code{spatial}}{Stores internal data and calculations for spatial mapping of single cells}
\item{\code{misc}}{Miscellaneous spot to store any data alongisde the object (for example, gene lists)}
\item{\code{version}}{Version of package used in object creation}
}}
Seurat/man/GetIntegrationData.Rd 0000644 0001762 0000144 00000001001 13617632030 016265 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{GetIntegrationData}
\alias{GetIntegrationData}
\title{Get integation data}
\usage{
GetIntegrationData(object, integration.name, slot)
}
\arguments{
\item{object}{Seurat object}
\item{integration.name}{Name of integration object}
\item{slot}{Which slot in integration object to get}
}
\value{
Returns data from the requested slot within the integrated object
}
\description{
Get integation data
}
Seurat/man/SubsetData.Rd 0000644 0001762 0000144 00000004243 13617632030 014622 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{SubsetData}
\alias{SubsetData}
\alias{SubsetData.Assay}
\alias{SubsetData.Seurat}
\title{Return a subset of the Seurat object}
\usage{
SubsetData(object, ...)
\method{SubsetData}{Assay}(
object,
cells = NULL,
subset.name = NULL,
low.threshold = -Inf,
high.threshold = Inf,
accept.value = NULL,
...
)
\method{SubsetData}{Seurat}(
object,
assay = NULL,
cells = NULL,
subset.name = NULL,
ident.use = NULL,
ident.remove = NULL,
low.threshold = -Inf,
high.threshold = Inf,
accept.value = NULL,
max.cells.per.ident = Inf,
random.seed = 1,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{cells}{A vector of cell names to use as a subset. If NULL
(default), then this list will be computed based on the next three
arguments. Otherwise, will return an object consissting only of these cells}
\item{subset.name}{Parameter to subset on. Eg, the name of a gene, PC_1, a
column name in object@meta.data, etc. Any argument that can be retreived
using FetchData}
\item{low.threshold}{Low cutoff for the parameter (default is -Inf)}
\item{high.threshold}{High cutoff for the parameter (default is Inf)}
\item{accept.value}{Returns cells with the subset name equal to this value}
\item{assay}{Assay to subset on}
\item{ident.use}{Create a cell subset based on the provided identity classes}
\item{ident.remove}{Subtract out cells from these identity classes (used for
filtration)}
\item{max.cells.per.ident}{Can be used to downsample the data to a certain
max per cell ident. Default is INF.}
\item{random.seed}{Random seed for downsampling}
}
\value{
Returns a Seurat object containing only the relevant subset of cells
}
\description{
Creates a Seurat object containing only a subset of the cells in the
original object. Takes either a list of cells to use as a subset, or a
parameter (for example, a gene), to subset on.
}
\examples{
\dontrun{
pbmc1 <- SubsetData(object = pbmc_small, cells = colnames(x = pbmc_small)[1:40])
pbmc1
}
}
Seurat/man/DiscretePalette.Rd 0000644 0001762 0000144 00000001771 13617632030 015647 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{DiscretePalette}
\alias{DiscretePalette}
\title{Discrete colour palettes from the pals package}
\usage{
DiscretePalette(n, palette = NULL)
}
\arguments{
\item{n}{Number of colours to be generated.}
\item{palette}{Options are
"alphabet", "alphabet2", "glasbey", "polychrome", and "stepped".
Can be omitted and the function will use the one based on the requested n.}
}
\value{
A vector of colors
}
\description{
These are included here because pals depends on a number of compiled
packages, and this can lead to increases in run time for Travis,
and generally should be avoided when possible.
}
\details{
These palettes are a much better default for data with many classes
than the default ggplot2 palette.
Many thanks to Kevin Wright for writing the pals package.
Taken from the pals package (Licence: GPL-3).
\url{https://cran.r-project.org/package=pals}
Credit: Kevin Wright
}
Seurat/man/SetIntegrationData.Rd 0000644 0001762 0000144 00000001026 13617632030 016310 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{SetIntegrationData}
\alias{SetIntegrationData}
\title{Set integation data}
\usage{
SetIntegrationData(object, integration.name, slot, new.data)
}
\arguments{
\item{object}{Seurat object}
\item{integration.name}{Name of integration object}
\item{slot}{Which slot in integration object to set}
\item{new.data}{New data to insert}
}
\value{
Returns a \code{\link{Seurat}} object
}
\description{
Set integation data
}
Seurat/man/RelativeCounts.Rd 0000644 0001762 0000144 00000001440 13617632030 015526 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{RelativeCounts}
\alias{RelativeCounts}
\title{Normalize raw data to fractions}
\usage{
RelativeCounts(data, scale.factor = 1, verbose = TRUE)
}
\arguments{
\item{data}{Matrix with the raw count data}
\item{scale.factor}{Scale the result. Default is 1}
\item{verbose}{Print progress}
}
\value{
Returns a matrix with the relative counts
}
\description{
Normalize count data to relative counts per cell by dividing by the total
per cell. Optionally use a scale factor, e.g. for counts per million (CPM)
use \code{scale.factor = 1e6}.
}
\examples{
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
mat
mat_norm <- RelativeCounts(data = mat)
mat_norm
}
Seurat/man/TopFeatures.Rd 0000644 0001762 0000144 00000001732 13617632030 015024 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{TopFeatures}
\alias{TopFeatures}
\title{Find features with highest scores for a given dimensional reduction technique}
\usage{
TopFeatures(
object,
dim = 1,
nfeatures = 20,
projected = FALSE,
balanced = FALSE,
...
)
}
\arguments{
\item{object}{DimReduc object}
\item{dim}{Dimension to use}
\item{nfeatures}{Number of features to return}
\item{projected}{Use the projected feature loadings}
\item{balanced}{Return an equal number of features with both + and - scores.}
\item{...}{Extra parameters passed to \code{\link{Loadings}}}
}
\value{
Returns a vector of features
}
\description{
Return a list of features with the strongest contribution to a set of components
}
\examples{
pbmc_small
TopFeatures(object = pbmc_small[["pca"]], dim = 1)
# After projection:
TopFeatures(object = pbmc_small[["pca"]], dim = 1, projected = TRUE)
}
Seurat/man/ExpVar.Rd 0000644 0001762 0000144 00000000657 13617632030 013775 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{ExpVar}
\alias{ExpVar}
\title{Calculate the variance of logged values}
\usage{
ExpVar(x)
}
\arguments{
\item{x}{A vector of values}
}
\value{
Returns the variance in log-space
}
\description{
Calculate variance of logged values in non-log space (return answer in
log-space)
}
\examples{
ExpVar(x = c(1, 2, 3))
}
Seurat/man/SelectIntegrationFeatures.Rd 0000644 0001762 0000144 00000002061 13617632030 017701 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{SelectIntegrationFeatures}
\alias{SelectIntegrationFeatures}
\title{Select integration features}
\usage{
SelectIntegrationFeatures(
object.list,
nfeatures = 2000,
assay = NULL,
verbose = TRUE,
fvf.nfeatures = 2000,
...
)
}
\arguments{
\item{object.list}{List of seurat objects}
\item{nfeatures}{Number of features to return}
\item{assay}{Name of assay from which to pull the variable features.}
\item{verbose}{Print messages}
\item{fvf.nfeatures}{nfeatures for FindVariableFeatures. Used if
VariableFeatures have not been set for any object in object.list.}
\item{...}{Additional parameters to \code{\link{FindVariableFeatures}}}
}
\value{
A vector of selected features
}
\description{
Choose the features to use when integrating multiple datasets. This function
ranks features by the number of datasets they appear in, breaking ties by the
median rank across datasets. It returns the highest features by this ranking.
}
Seurat/man/CustomPalette.Rd 0000644 0001762 0000144 00000002263 13617632030 015354 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{BlackAndWhite}
\alias{BlackAndWhite}
\alias{BlueAndRed}
\alias{CustomPalette}
\alias{PurpleAndYellow}
\title{Create a custom color palette}
\usage{
BlackAndWhite(mid = NULL, k = 50)
BlueAndRed(k = 50)
CustomPalette(low = "white", high = "red", mid = NULL, k = 50)
PurpleAndYellow(k = 50)
}
\arguments{
\item{mid}{middle color. Optional.}
\item{k}{number of steps (colors levels) to include between low and high values}
\item{low}{low color}
\item{high}{high color}
}
\value{
A color palette for plotting
}
\description{
Creates a custom color palette based on low, middle, and high color values
}
\examples{
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
plot(df, col = BlackAndWhite())
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
plot(df, col = BlueAndRed())
myPalette <- CustomPalette()
myPalette
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
plot(df, col = PurpleAndYellow())
}
Seurat/man/IsGlobal.Rd 0000644 0001762 0000144 00000001626 13617632030 014261 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{IsGlobal}
\alias{IsGlobal}
\alias{IsGlobal.default}
\alias{IsGlobal.DimReduc}
\title{Is an object global/persistent?}
\usage{
IsGlobal(object, ...)
\method{IsGlobal}{default}(object, ...)
\method{IsGlobal}{DimReduc}(object, ...)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
}
\value{
\code{TRUE} if the object is global/persistent otherwise \code{FALSE}
}
\description{
Typically, when removing \code{Assay} objects from an \code{Seurat} object,
all associated objects (eg. \code{DimReduc}, \code{Graph}, and \code{SeuratCommand} objects)
are removed as well. If an associated object is marked as global/persistent,
the associated object will remain even if its original assay was deleted
}
\examples{
IsGlobal(pbmc_small[['pca']])
}
Seurat/man/FindIntegrationAnchors.Rd 0000644 0001762 0000144 00000006005 13617632030 017163 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{FindIntegrationAnchors}
\alias{FindIntegrationAnchors}
\title{Find integration anchors}
\usage{
FindIntegrationAnchors(
object.list = NULL,
assay = NULL,
reference = NULL,
anchor.features = 2000,
scale = TRUE,
normalization.method = c("LogNormalize", "SCT"),
sct.clip.range = NULL,
reduction = c("cca", "rpca"),
l2.norm = TRUE,
dims = 1:30,
k.anchor = 5,
k.filter = 200,
k.score = 30,
max.features = 200,
nn.method = "rann",
eps = 0,
verbose = TRUE
)
}
\arguments{
\item{object.list}{A list of objects between which to find anchors for
downstream integration.}
\item{assay}{A vector of assay names specifying which assay to use when
constructing anchors. If NULL, the current default assay for each object is
used.}
\item{reference}{A vector specifying the object/s to be used as a reference
during integration. If NULL (default), all pairwise anchors are found (no
reference/s). If not NULL, the corresponding objects in \code{object.list}
will be used as references. When using a set of specified references, anchors
are first found between each query and each reference. The references are
then integrated through pairwise integration. Each query is then mapped to
the integrated reference.}
\item{anchor.features}{Can be either:
\itemize{
\item{A numeric value. This will call \code{\link{SelectIntegrationFeatures}}
to select the provided number of features to be used in anchor finding}
\item{A vector of features to be used as input to the anchor finding process}
}}
\item{scale}{Whether or not to scale the features provided. Only set to FALSE
if you have previously scaled the features you want to use for each object in
the object.list}
\item{normalization.method}{Name of normalization method used: LogNormalize
or SCT}
\item{sct.clip.range}{Numeric of length two specifying the min and max values
the Pearson residual will be clipped to}
\item{reduction}{Dimensional reduction to perform when finding anchors. Can
be one of:
\itemize{
\item{cca: Canonical correlation analysis}
\item{rpca: Reciprocal PCA}
}}
\item{l2.norm}{Perform L2 normalization on the CCA cell embeddings after
dimensional reduction}
\item{dims}{Which dimensions to use from the CCA to specify the neighbor
search space}
\item{k.anchor}{How many neighbors (k) to use when picking anchors}
\item{k.filter}{How many neighbors (k) to use when filtering anchors}
\item{k.score}{How many neighbors (k) to use when scoring anchors}
\item{max.features}{The maximum number of features to use when specifying the
neighborhood search space in the anchor filtering}
\item{nn.method}{Method for nearest neighbor finding. Options include: rann,
annoy}
\item{eps}{Error bound on the neighbor finding algorithm (from RANN)}
\item{verbose}{Print progress bars and output}
}
\value{
Returns an AnchorSet object
}
\description{
Finds the integration anchors
}
Seurat/man/CellScatter.Rd 0000644 0001762 0000144 00000001760 13617632030 014771 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{CellScatter}
\alias{CellScatter}
\alias{CellPlot}
\title{Cell-cell scatter plot}
\usage{
CellScatter(
object,
cell1,
cell2,
features = NULL,
highlight = NULL,
cols = NULL,
pt.size = 1,
smooth = FALSE
)
}
\arguments{
\item{object}{Seurat object}
\item{cell1}{Cell 1 name}
\item{cell2}{Cell 2 name}
\item{features}{Features to plot (default, all features)}
\item{highlight}{Features to highlight}
\item{cols}{Colors to use for identity class plotting.}
\item{pt.size}{Size of the points on the plot}
\item{smooth}{Smooth the graph (similar to smoothScatter)}
}
\value{
A ggplot object
}
\description{
Creates a plot of scatter plot of features across two single cells. Pearson
correlation between the two cells is displayed above the plot.
}
\examples{
CellScatter(object = pbmc_small, cell1 = 'ATAGGAGAAACAGA', cell2 = 'CATCAGGATGCACA')
}
Seurat/man/ReadAlevinCsv.Rd 0000644 0001762 0000144 00000001421 13617632030 015244 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{ReadAlevinCsv}
\alias{ReadAlevinCsv}
\title{Load in data from Alevin pipeline}
\usage{
ReadAlevinCsv(base.path)
}
\arguments{
\item{base.path}{Directory containing the alevin/quant_mat*
files provided by Alevin.}
}
\value{
Returns a matrix with rows and columns labeled
}
\description{
Enables easy loading of csv format matrix provided by Alevin
ran with `--dumpCsvCounts` flags.
}
\examples{
\dontrun{
data_dir <- 'path/to/output/directory'
list.files(data_dir) # Should show alevin/quants_mat* files
expression_matrix <- ReadAlevinCsv(base.path = data_dir)
seurat_object = CreateSeuratObject(counts = expression_matrix)
}
}
\author{
Avi Srivastava
}
Seurat/man/as.Graph.Rd 0000644 0001762 0000144 00000001656 13617632030 014233 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{as.Graph}
\alias{as.Graph}
\alias{as.Graph.Matrix}
\alias{as.Graph.matrix}
\title{Convert a matrix (or Matrix) to the Graph class.}
\usage{
as.Graph(x, ...)
\method{as.Graph}{Matrix}(x, ...)
\method{as.Graph}{matrix}(x, ...)
}
\arguments{
\item{x}{The matrix to convert}
\item{...}{Arguments passed to other methods (ignored for now)}
}
\description{
Convert a matrix (or Matrix) to the Graph class.
}
\examples{
# converting sparse matrix
mat <- Matrix::rsparsematrix(nrow = 10, ncol = 10, density = 0.1)
rownames(x = mat) <- paste0("feature_", 1:10)
colnames(x = mat) <- paste0("cell_", 1:10)
g <- as.Graph(x = mat)
# converting dense matrix
mat <- matrix(data = 1:16, nrow = 4)
rownames(x = mat) <- paste0("feature_", 1:4)
colnames(x = mat) <- paste0("cell_", 1:4)
g <- as.Graph(x = mat)
}
Seurat/man/LocalStruct.Rd 0000644 0001762 0000144 00000002451 13617632030 015021 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{LocalStruct}
\alias{LocalStruct}
\title{Calculate the local structure preservation metric}
\usage{
LocalStruct(
object,
grouping.var,
idents = NULL,
neighbors = 100,
reduction = "pca",
reduced.dims = 1:10,
orig.dims = 1:10,
verbose = TRUE
)
}
\arguments{
\item{object}{Seurat object}
\item{grouping.var}{Grouping variable}
\item{idents}{Optionally specify a set of idents to compute metric for}
\item{neighbors}{Number of neighbors to compute in pca/corrected pca space}
\item{reduction}{Dimensional reduction to use for corrected space}
\item{reduced.dims}{Number of reduced dimensions to use}
\item{orig.dims}{Number of PCs to use in original space}
\item{verbose}{Display progress bar}
}
\value{
Returns the average preservation metric
}
\description{
Calculates a metric that describes how well the local structure of each group
prior to integration is preserved after integration. This procedure works as
follows: For each group, compute a PCA, compute the top num.neighbors in pca
space, compute the top num.neighbors in corrected pca space, compute the
size of the intersection of those two sets of neighbors.
Return the average over all groups.
}
Seurat/man/GetResidual.Rd 0000644 0001762 0000144 00000002556 13617632030 015000 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{GetResidual}
\alias{GetResidual}
\title{Calculate pearson residuals of features not in the scale.data}
\usage{
GetResidual(
object,
features,
assay = "SCT",
umi.assay = "RNA",
clip.range = NULL,
replace.value = FALSE,
verbose = TRUE
)
}
\arguments{
\item{object}{A seurat object}
\item{features}{Name of features to add into the scale.data}
\item{assay}{Name of the assay of the seurat object generated by SCTransform}
\item{umi.assay}{Name of the assay of the seurat object containing UMI matrix and the default is
RNA}
\item{clip.range}{Numeric of length two specifying the min and max values the Pearson residual
will be clipped to}
\item{replace.value}{Recalculate residuals for all features, even if they are already present.
Useful if you want to change the clip.range.}
\item{verbose}{Whether to print messages and progress bars}
}
\value{
Returns a Seurat object containing pearson residuals of added features in its scale.data
}
\description{
This function calls sctransform::get_residuals.
}
\examples{
pbmc_small <- SCTransform(object = pbmc_small, variable.features.n = 20)
pbmc_small <- GetResidual(object = pbmc_small, features = c('MS4A1', 'TCL1A'))
}
\seealso{
\code{\link[sctransform]{get_residuals}}
}
Seurat/man/PrepSCTIntegration.Rd 0000644 0001762 0000144 00000002417 13617632030 016250 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{PrepSCTIntegration}
\alias{PrepSCTIntegration}
\title{Prepare an object list that has been run through SCTransform for integration}
\usage{
PrepSCTIntegration(
object.list,
assay = NULL,
anchor.features = 2000,
sct.clip.range = NULL,
verbose = TRUE
)
}
\arguments{
\item{object.list}{A list of objects to prep for integration}
\item{assay}{Name or vector of assay names (one for each object) that correspond
to the assay that SCTransform has been run on. If NULL, the current default
assay for each object is used.}
\item{anchor.features}{Can be either:
\itemize{
\item{A numeric value. This will call \code{\link{SelectIntegrationFeatures}}
to select the provided number of features to be used in anchor finding}
\item{A vector of features to be used as input to the anchor finding
process}
}}
\item{sct.clip.range}{Numeric of length two specifying the min and max values
the Pearson residual will be clipped to}
\item{verbose}{Display output/messages}
}
\value{
An object list with the \code{scale.data} slots set to the anchor
features
}
\description{
Prepare an object list that has been run through SCTransform for integration
}
Seurat/man/NormalizeData.Rd 0000644 0001762 0000144 00000004030 13617632030 015307 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/preprocessing.R
\name{NormalizeData}
\alias{NormalizeData}
\alias{NormalizeData.default}
\alias{NormalizeData.Assay}
\alias{NormalizeData.Seurat}
\title{Normalize Data}
\usage{
NormalizeData(object, ...)
\method{NormalizeData}{default}(
object,
normalization.method = "LogNormalize",
scale.factor = 10000,
margin = 1,
block.size = NULL,
verbose = TRUE,
...
)
\method{NormalizeData}{Assay}(
object,
normalization.method = "LogNormalize",
scale.factor = 10000,
margin = 1,
verbose = TRUE,
...
)
\method{NormalizeData}{Seurat}(
object,
assay = NULL,
normalization.method = "LogNormalize",
scale.factor = 10000,
margin = 1,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{normalization.method}{Method for normalization.
\itemize{
\item{LogNormalize: }{Feature counts for each cell are divided by the total
counts for that cell and multiplied by the scale.factor. This is then
natural-log transformed using log1p.}
\item{CLR: }{Applies a centered log ratio transformation}
\item{RC: }{Relative counts. Feature counts for each cell are divided by the total
counts for that cell and multiplied by the scale.factor. No log-transformation is applied.
For counts per million (CPM) set \code{scale.factor = 1e6}}
}}
\item{scale.factor}{Sets the scale factor for cell-level normalization}
\item{margin}{If performing CLR normalization, normalize across features (1) or cells (2)}
\item{block.size}{How many cells should be run in each chunk, will try to split evenly across threads}
\item{verbose}{display progress bar for normalization procedure}
\item{assay}{Name of assay to use}
}
\value{
Returns object after normalization
}
\description{
Normalize the count data present in a given assay.
}
\examples{
\dontrun{
pbmc_small
pmbc_small <- NormalizeData(object = pbmc_small)
}
}
Seurat/man/FindConservedMarkers.Rd 0000644 0001762 0000144 00000003623 13617632030 016642 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/differential_expression.R
\name{FindConservedMarkers}
\alias{FindConservedMarkers}
\title{Finds markers that are conserved between the groups}
\usage{
FindConservedMarkers(
object,
ident.1,
ident.2 = NULL,
grouping.var,
assay = "RNA",
slot = "data",
meta.method = minimump,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{An object}
\item{ident.1}{Identity class to define markers for}
\item{ident.2}{A second identity class for comparison. If NULL (default) -
use all other cells for comparison.}
\item{grouping.var}{grouping variable}
\item{assay}{of assay to fetch data for (default is RNA)}
\item{slot}{Slot to pull data from; note that if \code{test.use} is "negbinom", "poisson", or "DESeq2",
\code{slot} will be set to "counts"}
\item{meta.method}{method for combining p-values. Should be a function from
the metap package (NOTE: pass the function, not a string)}
\item{verbose}{Print a progress bar once expression testing begins}
\item{\dots}{parameters to pass to FindMarkers}
}
\value{
data.frame containing a ranked list of putative conserved markers, and
associated statistics (p-values within each group and a combined p-value
(such as Fishers combined p-value or others from the metap package),
percentage of cells expressing the marker, average differences). Name of group is appended to each
associated output column (e.g. CTRL_p_val). If only one group is tested in the grouping.var, max
and combined p-values are not returned.
}
\description{
Finds markers that are conserved between the groups
}
\examples{
\dontrun{
pbmc_small
# Create a simulated grouping variable
pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE)
FindConservedMarkers(pbmc_small, ident.1 = 0, ident.2 = 1, grouping.var = "groups")
}
}
Seurat/man/HVFInfo.Rd 0000644 0001762 0000144 00000002305 13617632030 014017 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{HVFInfo}
\alias{HVFInfo}
\alias{HVFInfo.Assay}
\alias{HVFInfo.Seurat}
\title{Get highly variable feature information}
\usage{
HVFInfo(object, ...)
\method{HVFInfo}{Assay}(object, selection.method, status = FALSE, ...)
\method{HVFInfo}{Seurat}(object, selection.method = NULL, assay = NULL, status = FALSE, ...)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{selection.method}{Which method to pull; choose one from \code{c('sctransform', 'sct')}
or \code{c('mean.var.plot', 'dispersion', 'mvp', 'disp')}}
\item{status}{Add variable status to the resulting data.frame}
\item{assay}{Name of assay to pull highly variable feature information for}
}
\value{
A dataframe with feature means, dispersion, and scaled dispersion
}
\description{
Get highly variable feature information
}
\examples{
# Get the HVF info directly from an Assay object
HVFInfo(object = pbmc_small[["RNA"]], selection.method = 'vst')[1:5, ]
# Get the HVF info from a specific Assay in a Seurat object
HVFInfo(object = pbmc_small, assay = "RNA")[1:5, ]
}
Seurat/man/Misc.Rd 0000644 0001762 0000144 00000002000 13617632030 013443 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Misc}
\alias{Misc}
\alias{Misc<-}
\alias{Misc.Assay}
\alias{Misc.Seurat}
\alias{Misc<-.Assay}
\alias{Misc<-.Seurat}
\title{Access miscellaneous data}
\usage{
Misc(object, ...)
Misc(object, ...) <- value
\method{Misc}{Assay}(object, slot = NULL, ...)
\method{Misc}{Seurat}(object, slot = NULL, ...)
\method{Misc}{Assay}(object, slot, ...) <- value
\method{Misc}{Seurat}(object, slot, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{Data to add}
\item{slot}{Name of specific bit of meta data to pull}
}
\value{
Miscellaneous data
An object with miscellaneous data added
}
\description{
Access miscellaneous data
Set miscellaneous data
}
\examples{
# Get the misc info
Misc(object = pbmc_small, slot = "example")
# Add misc info
Misc(object = pbmc_small, slot = "example") <- "testing_misc"
}
Seurat/man/Idents.Rd 0000644 0001762 0000144 00000007217 13617632030 014015 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Idents}
\alias{Idents}
\alias{Idents<-}
\alias{RenameIdents}
\alias{RenameIdent}
\alias{ReorderIdent}
\alias{SetIdent}
\alias{StashIdent}
\alias{Idents.Seurat}
\alias{Idents<-.Seurat}
\alias{ReorderIdent.Seurat}
\alias{RenameIdents.Seurat}
\alias{SetIdent.Seurat}
\alias{StashIdent.Seurat}
\alias{levels.Seurat}
\alias{levels<-.Seurat}
\title{Get, set, and manipulate an object's identity classes}
\usage{
Idents(object, ...)
Idents(object, ...) <- value
RenameIdents(object, ...)
ReorderIdent(object, var, ...)
SetIdent(object, ...)
StashIdent(object, save.name, ...)
\method{Idents}{Seurat}(object, ...)
\method{Idents}{Seurat}(object, cells = NULL, drop = FALSE, ...) <- value
\method{ReorderIdent}{Seurat}(
object,
var,
reverse = FALSE,
afxn = mean,
reorder.numeric = FALSE,
...
)
\method{RenameIdents}{Seurat}(object, ...)
\method{SetIdent}{Seurat}(object, cells = NULL, value, ...)
\method{StashIdent}{Seurat}(object, save.name = "orig.ident", ...)
\method{levels}{Seurat}(x)
\method{levels}{Seurat}(x) <- value
}
\arguments{
\item{...}{Arguments passed to other methods; for \code{RenameIdents}: named
arguments as \code{old.ident = new.ident}; for \code{ReorderIdent}: arguments
passed on to \code{\link{FetchData}}}
\item{value}{The name of the identites to pull from object metadata or the identities themselves}
\item{var}{Feature or variable to order on}
\item{save.name}{Store current identity information under this name}
\item{cells}{Set cell identities for specific cells}
\item{drop}{Drop unused levels}
\item{reverse}{Reverse ordering}
\item{afxn}{Function to evaluate each identity class based on; default is
\code{\link[base]{mean}}}
\item{reorder.numeric}{Rename all identity classes to be increasing numbers
starting from 1 (default is FALSE)}
\item{x, object}{An object}
}
\value{
\code{Idents}: The cell identies
\code{Idents<-}: An object with the cell identites changed
\code{RenameIdents}: An object with selected identity classes renamed
\code{ReorderIdent}: An object with
\code{SetIdent}: An object with new identity classes set
\code{StashIdent}: An object with the identities stashed
}
\description{
Get, set, and manipulate an object's identity classes
}
\examples{
# Get cell identity classes
Idents(object = pbmc_small)
# Set cell identity classes
# Can be used to set identities for specific cells to a new level
Idents(object = pbmc_small, cells = 1:4) <- 'a'
head(x = Idents(object = pbmc_small))
# Can also set idents from a value in object metadata
colnames(x = pbmc_small[[]])
Idents(object = pbmc_small) <- 'RNA_snn_res.1'
levels(x = pbmc_small)
# Rename cell identity classes
# Can provide an arbitrary amount of idents to rename
levels(x = pbmc_small)
pbmc_small <- RenameIdents(object = pbmc_small, '0' = 'A', '2' = 'C')
levels(x = pbmc_small)
\dontrun{
head(x = Idents(object = pbmc_small))
pbmc_small <- ReorderIdent(object = pbmc_small, var = 'PC_1')
head(x = Idents(object = pbmc_small))
}
# Set cell identity classes using SetIdent
cells.use <- WhichCells(object = pbmc_small, idents = '1')
pbmc_small <- SetIdent(object = pbmc_small, cells = cells.use, value = 'B')
head(x = pbmc_small[[]])
pbmc_small <- StashIdent(object = pbmc_small, save.name = 'idents')
head(x = pbmc_small[[]])
# Get the levels of identity classes of a Seurat object
levels(x = pbmc_small)
# Reorder identity classes
levels(x = pbmc_small)
levels(x = pbmc_small) <- c('C', 'A', 'B')
levels(x = pbmc_small)
}
Seurat/man/AnchorSet-class.Rd 0000644 0001762 0000144 00000002510 13617632030 015547 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{AnchorSet-class}
\alias{AnchorSet-class}
\alias{AnchorSet}
\title{The AnchorSet Class}
\description{
The AnchorSet class is an intermediate data storage class that stores the anchors and other
related information needed for performing downstream analyses - namely data integration
(\code{\link{IntegrateData}}) and data transfer (\code{\link{TransferData}}).
}
\section{Slots}{
\describe{
\item{\code{object.list}}{List of objects used to create anchors}
\item{\code{reference.cells}}{List of cell names in the reference dataset - needed when performing data
transfer.}
\item{\code{reference.objects}}{Position of reference object/s in object.list}
\item{\code{query.cells}}{List of cell names in the query dataset - needed when performing data transfer}
\item{\code{anchors}}{The anchor matrix. This contains the cell indices of both anchor pair cells, the
anchor score, and the index of the original dataset in the object.list for cell1 and cell2 of
the anchor.}
\item{\code{offsets}}{The offsets used to enable cell look up in downstream functions}
\item{\code{anchor.features}}{The features used when performing anchor finding.}
\item{\code{command}}{Store log of parameters that were used}
}}
Seurat/man/UpdateSymbolList.Rd 0000644 0001762 0000144 00000003447 13617632030 016034 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{UpdateSymbolList}
\alias{UpdateSymbolList}
\alias{GeneSymbolThesarus}
\title{Get updated synonyms for gene symbols}
\source{
\url{https://www.genenames.org/} \url{http://rest.genenames.org/}
}
\usage{
GeneSymbolThesarus(
symbols,
timeout = 10,
several.ok = FALSE,
verbose = TRUE,
...
)
UpdateSymbolList(
symbols,
timeout = 10,
several.ok = FALSE,
verbose = TRUE,
...
)
}
\arguments{
\item{symbols}{A vector of gene symbols}
\item{timeout}{Time to wait before cancelling query in seconds}
\item{several.ok}{Allow several current gene sybmols for each provided symbol}
\item{verbose}{Show a progress bar depicting search progress}
\item{...}{Extra parameters passed to \code{\link[httr]{GET}}}
}
\value{
For \code{GeneSymbolThesarus}, if \code{several.ok}, a named list
where each entry is the current symbol found for each symbol provided and the
names are the provided symbols. Otherwise, a named vector with the same information.
For \code{UpdateSymbolList}, \code{symbols} with updated symbols from
HGNC's gene names database
}
\description{
Find current gene symbols based on old or alias symbols using the gene
names database from the HUGO Gene Nomenclature Committee (HGNC)
}
\details{
For each symbol passed, we query the HGNC gene names database for
current symbols that have the provided symbol as either an alias
(\code{alias_symbol}) or old (\code{prev_symbol}) symbol. All other queries
are \strong{not} supported.
}
\note{
This function requires internet access
}
\examples{
\dontrun{
GeneSybmolThesarus(symbols = c("FAM64A"))
}
\dontrun{
UpdateSymbolList(symbols = cc.genes$s.genes)
}
}
\seealso{
\code{\link[httr]{GET}}
}
Seurat/man/LogVMR.Rd 0000644 0001762 0000144 00000000755 13617632030 013675 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{LogVMR}
\alias{LogVMR}
\title{Calculate the variance to mean ratio of logged values}
\usage{
LogVMR(x, ...)
}
\arguments{
\item{x}{A vector of values}
\item{...}{Other arguments (not used)}
}
\value{
Returns the VMR in log-space
}
\description{
Calculate the variance to mean ratio (VMR) in non-logspace (return answer in
log-space)
}
\examples{
LogVMR(x = c(1, 2, 3))
}
Seurat/man/RidgePlot.Rd 0000644 0001762 0000144 00000003271 13617632030 014454 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{RidgePlot}
\alias{RidgePlot}
\title{Single cell ridge plot}
\usage{
RidgePlot(
object,
features,
cols = NULL,
idents = NULL,
sort = FALSE,
assay = NULL,
group.by = NULL,
y.max = NULL,
same.y.lims = FALSE,
log = FALSE,
ncol = NULL,
combine = TRUE,
slot = "data",
...
)
}
\arguments{
\item{object}{Seurat object}
\item{features}{Features to plot (gene expression, metrics, PC scores,
anything that can be retreived by FetchData)}
\item{cols}{Colors to use for plotting}
\item{idents}{Which classes to include in the plot (default is all)}
\item{sort}{Sort identity classes (on the x-axis) by the average
expression of the attribute being potted, can also pass 'increasing' or 'decreasing' to change sort direction}
\item{assay}{Name of assay to use, defaults to the active assay}
\item{group.by}{Group (color) cells in different ways (for example, orig.ident)}
\item{y.max}{Maximum y axis value}
\item{same.y.lims}{Set all the y-axis limits to the same values}
\item{log}{plot the feature axis on log scale}
\item{ncol}{Number of columns if multiple plots are displayed}
\item{combine}{Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features}
\item{slot}{Use non-normalized counts data for plotting}
\item{...}{Extra parameters passed on to \code{\link{CombinePlots}}}
}
\value{
A ggplot object
}
\description{
Draws a ridge plot of single cell data (gene expression, metrics, PC
scores, etc.)
}
\examples{
RidgePlot(object = pbmc_small, features = 'PC_1')
}
Seurat/man/ExportToCellbrowser.Rd 0000644 0001762 0000144 00000004635 13617632030 016560 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{ExportToCellbrowser}
\alias{ExportToCellbrowser}
\title{Export Seurat object for UCSC cell browser}
\usage{
ExportToCellbrowser(
object,
dir,
dataset.name = Project(object = object),
reductions = "tsne",
markers.file = NULL,
cluster.field = "Cluster",
cb.dir = NULL,
port = NULL,
skip.expr.matrix = FALSE,
skip.metadata = FALSE,
skip.reductions = FALSE,
...
)
}
\arguments{
\item{object}{Seurat object}
\item{dir}{path to directory where to save exported files. These are:
exprMatrix.tsv, tsne.coords.tsv, meta.tsv, markers.tsv and a default cellbrowser.conf}
\item{dataset.name}{name of the dataset. Defaults to Seurat project name}
\item{reductions}{vector of reduction names to export}
\item{markers.file}{path to file with marker genes}
\item{cluster.field}{name of the metadata field containing cell cluster}
\item{cb.dir}{path to directory where to create UCSC cellbrowser static
website content root, e.g. an index.html, .json files, etc. These files
can be copied to any webserver. If this is specified, the cellbrowser
package has to be accessible from R via reticulate.}
\item{port}{on which port to run UCSC cellbrowser webserver after export}
\item{skip.expr.matrix}{whether to skip exporting expression matrix}
\item{skip.metadata}{whether to skip exporting metadata}
\item{skip.reductions}{whether to skip exporting reductions}
\item{...}{specifies the metadata fields to export. To supply field with
human readable name, pass name as \code{field="name"} parameter.}
}
\value{
This function exports Seurat object as a set of tsv files
to \code{dir} directory, copying the \code{markers.file} if it is
passed. It also creates the default \code{cellbrowser.conf} in the
directory. This directory could be read by \code{cbBuild} to
create a static website viewer for the dataset. If \code{cb.dir}
parameter is passed, the function runs \code{cbBuild} (if it is
installed) to create this static website in \code{cb.dir} directory.
If \code{port} parameter is passed, it also runs the webserver for
that directory and opens a browser.
}
\description{
Export Seurat object for UCSC cell browser
}
\examples{
\dontrun{
ExportToCellbrowser(object = pbmc_small, dataset.name = "PBMC", dir = "out")
}
}
\author{
Maximilian Haeussler, Nikolay Markov
}
Seurat/man/ElbowPlot.Rd 0000644 0001762 0000144 00000001421 13617632030 014465 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{ElbowPlot}
\alias{ElbowPlot}
\title{Quickly Pick Relevant Dimensions}
\usage{
ElbowPlot(object, ndims = 20, reduction = "pca")
}
\arguments{
\item{object}{Seurat object}
\item{ndims}{Number of dimensions to plot standard deviation for}
\item{reduction}{Reduction technique to plot standard deviation for}
}
\value{
A ggplot object
}
\description{
Plots the standard deviations (or approximate singular values if running PCAFast)
of the principle components for easy identification of an elbow in the graph.
This elbow often corresponds well with the significant dims and is much faster to run than
Jackstraw
}
\examples{
ElbowPlot(object = pbmc_small)
}
Seurat/man/RenameCells.Rd 0000644 0001762 0000144 00000003523 13617632030 014755 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{RenameCells}
\alias{RenameCells}
\alias{RenameCells.Assay}
\alias{RenameCells.DimReduc}
\alias{RenameCells.Seurat}
\title{Rename cells}
\usage{
RenameCells(object, ...)
\method{RenameCells}{Assay}(object, new.names = NULL, ...)
\method{RenameCells}{DimReduc}(object, new.names = NULL, ...)
\method{RenameCells}{Seurat}(
object,
add.cell.id = NULL,
new.names = NULL,
for.merge = FALSE,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{new.names}{vector of new cell names}
\item{add.cell.id}{prefix to add cell names}
\item{for.merge}{Only rename slots needed for merging Seurat objects.
Currently only renames the raw.data and meta.data slots.}
}
\value{
An object with new cell names
}
\description{
Change the cell names in all the different parts of an object. Can
be useful before combining multiple objects.
}
\details{
If \code{add.cell.id} is set a prefix is added to existing cell names. If
\code{new.names} is set these will be used to replace existing names.
}
\examples{
# Rename cells in an Assay
head(x = colnames(x = pbmc_small[["RNA"]]))
renamed.assay <- RenameCells(
object = pbmc_small[["RNA"]],
new.names = paste0("A_", colnames(x = pbmc_small[["RNA"]]))
)
head(x = colnames(x = renamed.assay))
# Rename cells in a DimReduc
head(x = Cells(x = pbmc_small[["pca"]]))
renamed.dimreduc <- RenameCells(
object = pbmc_small[["pca"]],
new.names = paste0("A_", Cells(x = pbmc_small[["pca"]]))
)
head(x = Cells(x = renamed.dimreduc))
# Rename cells in a Seurat object
head(x = colnames(x = pbmc_small))
pbmc_small <- RenameCells(object = pbmc_small, add.cell.id = "A")
head(x = colnames(x = pbmc_small))
}
Seurat/man/as.list.SeuratCommand.Rd 0000644 0001762 0000144 00000001151 13617632030 016674 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{as.list.SeuratCommand}
\alias{as.list.SeuratCommand}
\title{Coerce a SeuratCommand to a list}
\usage{
\method{as.list}{SeuratCommand}(x, complete = FALSE, ...)
}
\arguments{
\item{x}{object to be coerced or tested.}
\item{complete}{Include slots besides just parameters (eg. call string, name, timestamp)}
\item{...}{objects, possibly named.}
}
\value{
A list with the parameters and, if \code{complete = TRUE}, the call string, name, and timestamp
}
\description{
Coerce a SeuratCommand to a list
}
Seurat/man/SCTransform.Rd 0000644 0001762 0000144 00000006462 13617632030 014771 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{SCTransform}
\alias{SCTransform}
\title{Use regularized negative binomial regression to normalize UMI count data}
\usage{
SCTransform(
object,
assay = "RNA",
new.assay.name = "SCT",
do.correct.umi = TRUE,
ncells = NULL,
variable.features.n = 3000,
variable.features.rv.th = 1.3,
vars.to.regress = NULL,
do.scale = FALSE,
do.center = TRUE,
clip.range = c(-sqrt(x = ncol(x = object[[assay]])/30), sqrt(x = ncol(x =
object[[assay]])/30)),
conserve.memory = FALSE,
return.only.var.genes = TRUE,
seed.use = 1448145,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{A seurat object}
\item{assay}{Name of assay to pull the count data from; default is 'RNA'}
\item{new.assay.name}{Name for the new assay containing the normalized data}
\item{do.correct.umi}{Place corrected UMI matrix in assay counts slot; default is TRUE}
\item{ncells}{Number of subsampling cells used to build NB regression; default is NULL}
\item{variable.features.n}{Use this many features as variable features after
ranking by residual variance; default is 3000}
\item{variable.features.rv.th}{Instead of setting a fixed number of variable features,
use this residual variance cutoff; this is only used when \code{variable.features.n}
is set to NULL; default is 1.3}
\item{vars.to.regress}{Variables to regress out in a second non-regularized linear
regression. For example, percent.mito. Default is NULL}
\item{do.scale}{Whether to scale residuals to have unit variance; default is FALSE}
\item{do.center}{Whether to center residuals to have mean zero; default is TRUE}
\item{clip.range}{Range to clip the residuals to; default is \code{c(-sqrt(n/30), sqrt(n/30))},
where n is the number of cells}
\item{conserve.memory}{If set to TRUE the residual matrix for all genes is never
created in full; useful for large data sets, but will take longer to run;
this will also set return.only.var.genes to TRUE; default is FALSE}
\item{return.only.var.genes}{If set to TRUE the scale.data matrices in output assay are
subset to contain only the variable genes; default is TRUE}
\item{seed.use}{Set a random seed. By default, sets the seed to 1448145. Setting
NULL will not set a seed.}
\item{verbose}{Whether to print messages and progress bars}
\item{...}{Additional parameters passed to \code{sctransform::vst}}
}
\value{
Returns a Seurat object with a new assay (named SCT by default) with
counts being (corrected) counts, data being log1p(counts), scale.data being
pearson residuals; sctransform::vst intermediate results are saved in misc
slot of the new assay.
}
\description{
This function calls sctransform::vst. The sctransform package is available at
https://github.com/ChristophH/sctransform.
Use this function as an alternative to the NormalizeData,
FindVariableFeatures, ScaleData workflow. Results are saved in a new assay
(named SCT by default) with counts being (corrected) counts, data being log1p(counts),
scale.data being pearson residuals; sctransform::vst intermediate results are saved
in misc slot of new assay.
}
\examples{
SCTransform(object = pbmc_small)
}
\seealso{
\code{\link[sctransform]{correct_counts}} \code{\link[sctransform]{get_residuals}}
}
Seurat/man/VariableFeatures.Rd 0000644 0001762 0000144 00000002070 13617632030 016003 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{VariableFeatures}
\alias{VariableFeatures}
\alias{VariableFeatures<-}
\alias{VariableFeatures.Assay}
\alias{VariableFeatures.Seurat}
\alias{VariableFeatures<-.Assay}
\alias{VariableFeatures<-.Seurat}
\title{Get and set variable feature information}
\usage{
VariableFeatures(object, ...)
VariableFeatures(object, ...) <- value
\method{VariableFeatures}{Assay}(object, selection.method = NULL, ...)
\method{VariableFeatures}{Seurat}(object, assay = NULL, selection.method = NULL, ...)
\method{VariableFeatures}{Assay}(object, ...) <- value
\method{VariableFeatures}{Seurat}(object, assay = NULL, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{A character vector of variable features}
\item{selection.method}{Method used to set variable features}
\item{assay}{Name of assay to pull variable features for}
}
\description{
Get and set variable feature information
}
Seurat/man/Stdev.Rd 0000644 0001762 0000144 00000001434 13617632030 013647 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Stdev}
\alias{Stdev}
\alias{Stdev.DimReduc}
\alias{Stdev.Seurat}
\title{Get the standard deviations for an object}
\usage{
Stdev(object, ...)
\method{Stdev}{DimReduc}(object, ...)
\method{Stdev}{Seurat}(object, reduction = "pca", ...)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{reduction}{Name of reduction to use}
}
\description{
Get the standard deviations for an object
}
\examples{
# Get the standard deviations for each PC from the DimReduc object
Stdev(object = pbmc_small[["pca"]])
# Get the standard deviations for each PC from the Seurat object
Stdev(object = pbmc_small, reduction = "pca")
}
Seurat/man/LogNormalize.Rd 0000644 0001762 0000144 00000001245 13617632030 015164 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{LogNormalize}
\alias{LogNormalize}
\title{Normalize raw data}
\usage{
LogNormalize(data, scale.factor = 10000, verbose = TRUE)
}
\arguments{
\item{data}{Matrix with the raw count data}
\item{scale.factor}{Scale the data. Default is 1e4}
\item{verbose}{Print progress}
}
\value{
Returns a matrix with the normalize and log transformed data
}
\description{
Normalize count data per cell and transform to log scale
}
\examples{
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
mat
mat_norm <- LogNormalize(data = mat)
mat_norm
}
Seurat/man/Seurat-package.Rd 0000644 0001762 0000144 00000002036 13617632030 015415 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zzz.R
\docType{package}
\name{Seurat-package}
\alias{Seurat-package}
\title{Tools for single-cell genomics}
\description{
Tools for single-cell genomics
}
\section{Package options}{
Seurat uses the following [options()] to configure behaviour:
\describe{
\item{\code{Seurat.memsafe}}{global option to call gc() after many operations.
This can be helpful in cleaning up the memory status of the R session and
prevent use of swap space. However, it does add to the computational overhead
and setting to FALSE can speed things up if you're working in an environment
where RAM availabiliy is not a concern.}
\item{\code{Seurat.warn.umap.uwot}}{Show warning about the default backend
for \code{\link{RunUMAP}} changing from Python UMAP via reticulate to UWOT}
\item{\code{Seurat.checkdots}}{For functions that have ... as a parameter,
this controls the behavior when an item isn't used. Can be one of warn,
stop, or silent.}
}
}
Seurat/man/ScoreJackStraw.Rd 0000644 0001762 0000144 00000003101 13617632030 015440 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{ScoreJackStraw}
\alias{ScoreJackStraw}
\alias{ScoreJackStraw.JackStrawData}
\alias{ScoreJackStraw.DimReduc}
\alias{ScoreJackStraw.Seurat}
\title{Compute Jackstraw scores significance.}
\usage{
ScoreJackStraw(object, ...)
\method{ScoreJackStraw}{JackStrawData}(object, dims = 1:5, score.thresh = 1e-05, ...)
\method{ScoreJackStraw}{DimReduc}(object, dims = 1:5, score.thresh = 1e-05, ...)
\method{ScoreJackStraw}{Seurat}(
object,
reduction = "pca",
dims = 1:5,
score.thresh = 1e-05,
do.plot = FALSE,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{dims}{Which dimensions to examine}
\item{score.thresh}{Threshold to use for the proportion test of PC
significance (see Details)}
\item{reduction}{Reduction associated with JackStraw to score}
\item{do.plot}{Show plot. To return ggplot object, use \code{JackStrawPlot} after
running ScoreJackStraw.}
}
\value{
Returns a Seurat object
}
\description{
Significant PCs should show a p-value distribution that is
strongly skewed to the left compared to the null distribution.
The p-value for each PC is based on a proportion test comparing the number
of features with a p-value below a particular threshold (score.thresh), compared with the
proportion of features expected under a uniform distribution of p-values.
}
\seealso{
\code{\link{JackStrawPlot}}
\code{\link{JackStrawPlot}}
}
\author{
Omri Wurtzel
}
Seurat/man/SeuratCommand-class.Rd 0000644 0001762 0000144 00000001310 13617632030 016420 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{SeuratCommand-class}
\alias{SeuratCommand-class}
\alias{SeuratCommand}
\title{The SeuratCommand Class}
\description{
The SeuratCommand is used for logging commands that are run on a SeuratObject. It stores parameters and timestamps
}
\section{Slots}{
\describe{
\item{\code{name}}{Command name}
\item{\code{time.stamp}}{Timestamp of when command was tun}
\item{\code{assay.used}}{Optional name of assay used to generate \code{SeuratCommand} object}
\item{\code{call.string}}{String of the command call}
\item{\code{params}}{List of parameters used in the command call}
}}
Seurat/man/TransferData.Rd 0000644 0001762 0000144 00000003354 13617632030 015143 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{TransferData}
\alias{TransferData}
\title{Transfer Labels}
\usage{
TransferData(
anchorset,
refdata,
weight.reduction = "pcaproject",
l2.norm = FALSE,
dims = 1:30,
k.weight = 50,
sd.weight = 1,
eps = 0,
do.cpp = TRUE,
verbose = TRUE,
slot = "data"
)
}
\arguments{
\item{anchorset}{Results from FindTransferAnchors}
\item{refdata}{Data to transfer. Should be either a vector where the names
correspond to reference cells, or a matrix, where the column names correspond
to the reference cells.}
\item{weight.reduction}{Dimensional reduction to use for the weighting.
Options are:
\itemize{
\item{pcaproject: Use the projected PCA used for anchor building}
\item{pca: Use an internal PCA on the query only}
\item{cca: Use the CCA used for anchor building}
\item{custom DimReduc: User provided DimReduc object computed on the query
cells}
}}
\item{l2.norm}{Perform L2 normalization on the cell embeddings after
dimensional reduction}
\item{dims}{Number of PCs to use in the weighting procedure}
\item{k.weight}{Number of neighbors to consider when weighting}
\item{sd.weight}{Controls the bandwidth of the Gaussian kernel for weighting}
\item{eps}{Error bound on the neighbor finding algorithm (from RANN)}
\item{do.cpp}{Run cpp code where applicable}
\item{verbose}{Print progress bars and output}
\item{slot}{Slot to store the imputed data}
}
\value{
If refdata is a vector, returns a dataframe with label predictions.
If refdata is a matrix, returns an Assay object where the imputed data has
been stored in the provided slot.
}
\description{
Transfers the labels
}
Seurat/man/AddModuleScore.Rd 0000644 0001762 0000144 00000004057 13617632030 015420 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{AddModuleScore}
\alias{AddModuleScore}
\title{Calculate module scores for feature expression programs in single cells}
\usage{
AddModuleScore(
object,
features,
pool = NULL,
nbin = 24,
ctrl = 100,
k = FALSE,
assay = NULL,
name = "Cluster",
seed = 1,
search = FALSE,
...
)
}
\arguments{
\item{object}{Seurat object}
\item{features}{Feature expression programs in list}
\item{pool}{List of features to check expression levels agains, defaults to \code{rownames(x = object)}}
\item{nbin}{Number of bins of aggregate expression levels for all analyzed features}
\item{ctrl}{Number of control features selected from the same bin per analyzed feature}
\item{k}{Use feature clusters returned from DoKMeans}
\item{assay}{Name of assay to use}
\item{name}{Name for the expression programs}
\item{seed}{Set a random seed. If NULL, seed is not set.}
\item{search}{Search for symbol synonyms for features in \code{features} that
don't match features in \code{object}? Searches the HGNC's gene names database;
see \code{\link{UpdateSymbolList}} for more details}
\item{...}{Extra parameters passed to \code{\link{UpdateSymbolList}}}
}
\value{
Returns a Seurat object with module scores added to object meta data
}
\description{
Calculate the average expression levels of each program (cluster) on single cell level,
subtracted by the aggregated expression of control feature sets.
All analyzed features are binned based on averaged expression, and the control features are
randomly selected from each bin.
}
\examples{
\dontrun{
cd_features <- list(c(
'CD79B',
'CD79A',
'CD19',
'CD180',
'CD200',
'CD3D',
'CD2',
'CD3E',
'CD7',
'CD8A',
'CD14',
'CD1C',
'CD68',
'CD9',
'CD247'
))
pbmc_small <- AddModuleScore(
object = pbmc_small,
features = cd_features,
ctrl = 5,
name = 'CD_Features'
)
head(x = pbmc_small[])
}
}
\references{
Tirosh et al, Science (2016)
}
Seurat/man/Project.Rd 0000644 0001762 0000144 00000001234 13617632030 014166 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Project}
\alias{Project}
\alias{Project<-}
\alias{Project.Seurat}
\alias{Project<-.Seurat}
\title{Get and set project information}
\usage{
Project(object, ...)
Project(object, ...) <- value
\method{Project}{Seurat}(object, ...)
\method{Project}{Seurat}(object, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{Project information to set}
}
\value{
Project information
An object with project information added
}
\description{
Get and set project information
}
Seurat/man/Graph-class.Rd 0000644 0001762 0000144 00000000737 13617632030 014733 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{Graph-class}
\alias{Graph-class}
\alias{Graph}
\title{The Graph Class}
\description{
The Graph class inherits from dgCMatrix. We do this to enable future expandability of graphs.
}
\section{Slots}{
\describe{
\item{\code{assay.used}}{Optional name of assay used to generate \code{Graph} object}
}}
\seealso{
\code{\link[Matrix]{dgCMatrix-class}}
}
Seurat/man/MixingMetric.Rd 0000644 0001762 0000144 00000002364 13617632030 015164 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{MixingMetric}
\alias{MixingMetric}
\title{Calculates a mixing metric}
\usage{
MixingMetric(
object,
grouping.var,
reduction = "pca",
dims = 1:2,
k = 5,
max.k = 300,
eps = 0,
verbose = TRUE
)
}
\arguments{
\item{object}{Seurat object}
\item{grouping.var}{Grouping variable for dataset}
\item{reduction}{Which dimensionally reduced space to use}
\item{dims}{Dimensions to use}
\item{k}{Neighbor number to examine per group}
\item{max.k}{Maximum size of local neighborhood to compute}
\item{eps}{Error bound on the neighbor finding algorithm (from RANN)}
\item{verbose}{Displays progress bar}
}
\value{
Returns a vector of values representing the entropy metric from each
bootstrapped iteration.
}
\description{
Here we compute a measure of how well mixed a composite dataset is. To
compute, we first examine the local neighborhood for each cell (looking at
max.k neighbors) and determine for each group (could be the dataset after
integration) the k nearest neighbor and what rank that neighbor was in the
overall neighborhood. We then take the median across all groups as the mixing
metric per cell.
}
Seurat/man/FeatureScatter.Rd 0000644 0001762 0000144 00000003114 13617632030 015500 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{FeatureScatter}
\alias{FeatureScatter}
\alias{GenePlot}
\title{Scatter plot of single cell data}
\usage{
FeatureScatter(
object,
feature1,
feature2,
cells = NULL,
group.by = NULL,
cols = NULL,
pt.size = 1,
shape.by = NULL,
span = NULL,
smooth = FALSE,
slot = "data"
)
}
\arguments{
\item{object}{Seurat object}
\item{feature1}{First feature to plot. Typically feature expression but can also
be metrics, PC scores, etc. - anything that can be retreived with FetchData}
\item{feature2}{Second feature to plot.}
\item{cells}{Cells to include on the scatter plot.}
\item{group.by}{Name of one or more metadata columns to group (color) cells by
(for example, orig.ident); pass 'ident' to group by identity class}
\item{cols}{Colors to use for identity class plotting.}
\item{pt.size}{Size of the points on the plot}
\item{shape.by}{Ignored for now}
\item{span}{Spline span in loess function call, if \code{NULL}, no spline added}
\item{smooth}{Smooth the graph (similar to smoothScatter)}
\item{slot}{Slot to pull data from, should be one of 'counts', 'data', or 'scale.data'}
}
\value{
A ggplot object
}
\description{
Creates a scatter plot of two features (typically feature expression), across a
set of single cells. Cells are colored by their identity class. Pearson
correlation between the two features is displayed above the plot.
}
\examples{
FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')
}
Seurat/man/as.SingleCellExperiment.Rd 0000644 0001762 0000144 00000001144 13617632030 017244 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{as.SingleCellExperiment}
\alias{as.SingleCellExperiment}
\alias{as.SingleCellExperiment.Seurat}
\title{Convert objects to SingleCellExperiment objects}
\usage{
as.SingleCellExperiment(x, ...)
\method{as.SingleCellExperiment}{Seurat}(x, assay = NULL, ...)
}
\arguments{
\item{x}{An object to convert to class \code{SingleCellExperiment}}
\item{...}{Arguments passed to other methods}
\item{assay}{Assay to convert}
}
\description{
Convert objects to SingleCellExperiment objects
}
Seurat/man/RunTSNE.Rd 0000644 0001762 0000144 00000006103 13617632030 014016 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{RunTSNE}
\alias{RunTSNE}
\alias{RunTSNE.matrix}
\alias{RunTSNE.DimReduc}
\alias{RunTSNE.dist}
\alias{RunTSNE.Seurat}
\title{Run t-distributed Stochastic Neighbor Embedding}
\usage{
RunTSNE(object, ...)
\method{RunTSNE}{matrix}(
object,
assay = NULL,
seed.use = 1,
tsne.method = "Rtsne",
add.iter = 0,
dim.embed = 2,
reduction.key = "tSNE_",
...
)
\method{RunTSNE}{DimReduc}(
object,
cells = NULL,
dims = 1:5,
seed.use = 1,
tsne.method = "Rtsne",
add.iter = 0,
dim.embed = 2,
reduction.key = "tSNE_",
...
)
\method{RunTSNE}{dist}(
object,
assay = NULL,
seed.use = 1,
tsne.method = "Rtsne",
add.iter = 0,
dim.embed = 2,
reduction.key = "tSNE_",
...
)
\method{RunTSNE}{Seurat}(
object,
reduction = "pca",
cells = NULL,
dims = 1:5,
features = NULL,
seed.use = 1,
tsne.method = "Rtsne",
add.iter = 0,
dim.embed = 2,
distance.matrix = NULL,
reduction.name = "tsne",
reduction.key = "tSNE_",
...
)
}
\arguments{
\item{object}{Seurat object}
\item{...}{Arguments passed to other methods and to t-SNE call (most commonly used is perplexity)}
\item{assay}{Name of assay that that t-SNE is being run on}
\item{seed.use}{Random seed for the t-SNE. If NULL, does not set the seed}
\item{tsne.method}{Select the method to use to compute the tSNE. Available
methods are:
\itemize{
\item{Rtsne: }{Use the Rtsne package Barnes-Hut implementation of tSNE (default)}
\item{FIt-SNE: }{Use the FFT-accelerated Interpolation-based t-SNE. Based on
Kluger Lab code found here: https://github.com/KlugerLab/FIt-SNE}
}}
\item{add.iter}{If an existing tSNE has already been computed, uses the
current tSNE to seed the algorithm and then adds additional iterations on top
of this}
\item{dim.embed}{The dimensional space of the resulting tSNE embedding
(default is 2). For example, set to 3 for a 3d tSNE}
\item{reduction.key}{dimensional reduction key, specifies the string before the number for the dimension names. tSNE_ by default}
\item{cells}{Which cells to analyze (default, all cells)}
\item{dims}{Which dimensions to use as input features}
\item{reduction}{Which dimensional reduction (e.g. PCA, ICA) to use for
the tSNE. Default is PCA}
\item{features}{If set, run the tSNE on this subset of features
(instead of running on a set of reduced dimensions). Not set (NULL) by default;
\code{dims} must be NULL to run on features}
\item{distance.matrix}{If set, runs tSNE on the given distance matrix
instead of data matrix (experimental)}
\item{reduction.name}{dimensional reduction name, specifies the position in the object$dr list. tsne by default}
}
\description{
Run t-SNE dimensionality reduction on selected features. Has the option of
running in a reduced dimensional space (i.e. spectral tSNE, recommended),
or running based on a set of genes. For details about stored TSNE calculation
parameters, see \code{PrintTSNEParams}.
}
Seurat/man/MetaFeature.Rd 0000644 0001762 0000144 00000001765 13617632030 014773 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{MetaFeature}
\alias{MetaFeature}
\title{Aggregate expression of multiple features into a single feature}
\usage{
MetaFeature(
object,
features,
meta.name = "metafeature",
cells = NULL,
assay = NULL,
slot = "data"
)
}
\arguments{
\item{object}{A Seurat object}
\item{features}{List of features to aggregate}
\item{meta.name}{Name of column in metadata to store metafeature}
\item{cells}{List of cells to use (default all cells)}
\item{assay}{Which assay to use}
\item{slot}{Which slot to take data from (default data)}
}
\value{
Returns a \code{Seurat} object with metafeature stored in objct metadata
}
\description{
Calculates relative contribution of each feature to each cell
for given set of features.
}
\examples{
pbmc_small <- MetaFeature(
object = pbmc_small,
features = c("LTB", "EAF2"),
meta.name = 'var.aggregate'
)
head(pbmc_small[[]])
}
Seurat/man/VariableFeaturePlot.Rd 0000644 0001762 0000144 00000001721 13617632030 016461 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{VariableFeaturePlot}
\alias{VariableFeaturePlot}
\alias{VariableGenePlot}
\alias{MeanVarPlot}
\title{View variable features}
\usage{
VariableFeaturePlot(
object,
cols = c("black", "red"),
pt.size = 1,
log = NULL,
selection.method = NULL,
assay = NULL
)
}
\arguments{
\item{object}{Seurat object}
\item{cols}{Colors to specify non-variable/variable status}
\item{pt.size}{Size of the points on the plot}
\item{log}{Plot the x-axis in log scale}
\item{selection.method}{Which method to pull; choose one from \code{c('sctransform', 'sct')}
or \code{c('mean.var.plot', 'dispersion', 'mvp', 'disp')}}
\item{assay}{Assay to pull variable features from}
}
\value{
A ggplot object
}
\description{
View variable features
}
\examples{
VariableFeaturePlot(object = pbmc_small)
}
\seealso{
\code{\link{FindVariableFeatures}}
}
Seurat/man/CombinePlots.Rd 0000644 0001762 0000144 00000002014 13617632030 015153 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{CombinePlots}
\alias{CombinePlots}
\title{Combine ggplot2-based plots into a single plot}
\usage{
CombinePlots(plots, ncol = NULL, legend = NULL, ...)
}
\arguments{
\item{plots}{A list of gg objects}
\item{ncol}{Number of columns}
\item{legend}{Combine legends into a single legend
choose from 'right' or 'bottom'; pass 'none' to remove legends, or \code{NULL}
to leave legends as they are}
\item{...}{Extra parameters passed to plot_grid}
}
\value{
A combined plot
}
\description{
Combine ggplot2-based plots into a single plot
}
\examples{
pbmc_small[['group']] <- sample(
x = c('g1', 'g2'),
size = ncol(x = pbmc_small),
replace = TRUE
)
plots <- FeaturePlot(
object = pbmc_small,
features = c('MS4A1', 'FCN1'),
split.by = 'group',
combine = FALSE
)
CombinePlots(
plots = plots,
legend = 'none',
nrow = length(x = unique(x = pbmc_small[['group', drop = TRUE]]))
)
}
Seurat/man/JS.Rd 0000644 0001762 0000144 00000001545 13617632030 013101 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{JS}
\alias{JS}
\alias{JS<-}
\alias{JS.DimReduc}
\alias{JS.JackStrawData}
\alias{JS<-.DimReduc}
\alias{JS<-.JackStrawData}
\title{Get JackStraw information}
\usage{
JS(object, ...)
JS(object, ...) <- value
\method{JS}{DimReduc}(object, slot = NULL, ...)
\method{JS}{JackStrawData}(object, slot, ...)
\method{JS}{DimReduc}(object, slot = NULL, ...) <- value
\method{JS}{JackStrawData}(object, slot, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{JackStraw information}
\item{slot}{Name of slot to store JackStraw scores to
Can shorten to 'empirical', 'fake', 'full', or 'overall'}
}
\description{
Get JackStraw information
Set JackStraw information
}
Seurat/man/LabelPoints.Rd 0000644 0001762 0000144 00000002456 13617632030 015003 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{LabelPoints}
\alias{LabelPoints}
\alias{Labeler}
\title{Add text labels to a ggplot2 plot}
\usage{
LabelPoints(
plot,
points,
labels = NULL,
repel = FALSE,
xnudge = 0.3,
ynudge = 0.05,
...
)
}
\arguments{
\item{plot}{A ggplot2 plot with a GeomPoint layer}
\item{points}{A vector of points to label; if \code{NULL}, will use all points in the plot}
\item{labels}{A vector of labels for the points; if \code{NULL}, will use
rownames of the data provided to the plot at the points selected}
\item{repel}{Use \code{geom_text_repel} to create a nicely-repelled labels; this
is slow when a lot of points are being plotted. If using \code{repel}, set \code{xnudge}
and \code{ynudge} to 0}
\item{xnudge, ynudge}{Amount to nudge X and Y coordinates of labels by}
\item{...}{Extra parameters passed to \code{geom_text}}
}
\value{
A ggplot object
}
\description{
Add text labels to a ggplot2 plot
}
\examples{
ff <- TopFeatures(object = pbmc_small[['pca']])
cc <- TopCells(object = pbmc_small[['pca']])
plot <- FeatureScatter(object = pbmc_small, feature1 = ff[1], feature2 = ff[2])
LabelPoints(plot = plot, points = cc)
}
\seealso{
\code{\link[ggplot2]{geom_text}}
}
Seurat/man/BarcodeInflectionsPlot.Rd 0000644 0001762 0000144 00000002022 13617632030 017150 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{BarcodeInflectionsPlot}
\alias{BarcodeInflectionsPlot}
\title{Plot the Barcode Distribution and Calculated Inflection Points}
\usage{
BarcodeInflectionsPlot(object)
}
\arguments{
\item{object}{Seurat object}
}
\value{
Returns a `ggplot2` object showing the by-group inflection points and provided
(or default) rank threshold values in grey.
}
\description{
This function plots the calculated inflection points derived from the barcode-rank
distribution.
}
\details{
See [CalculateBarcodeInflections()] to calculate inflection points and
[SubsetByBarcodeInflections()] to subsequently subset the Seurat object.
}
\examples{
pbmc_small <- CalculateBarcodeInflections(pbmc_small, group.column = 'groups')
BarcodeInflectionsPlot(pbmc_small)
}
\seealso{
\code{\link{CalculateBarcodeInflections}} \code{\link{SubsetByBarcodeInflections}}
}
\author{
Robert A. Amezquita, \email{robert.amezquita@fredhutch.org}
}
Seurat/man/print.DimReduc.Rd 0000644 0001762 0000144 00000001310 13617632030 015402 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{print.DimReduc}
\alias{print.DimReduc}
\alias{print}
\title{Print the results of a dimensional reduction analysis}
\usage{
\method{print}{DimReduc}(x, dims = 1:5, nfeatures = 20, projected = FALSE, ...)
}
\arguments{
\item{x}{An object}
\item{dims}{Number of dimensions to display}
\item{nfeatures}{Number of genes to display}
\item{projected}{Use projected slot}
\item{...}{Arguments passed to other methods}
}
\value{
Set of features defining the components
}
\description{
Prints a set of features that most strongly define a set of components
}
\seealso{
\code{\link[base]{cat}}
}
Seurat/man/JackStraw.Rd 0000644 0001762 0000144 00000003311 13617632030 014447 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dimensional_reduction.R
\name{JackStraw}
\alias{JackStraw}
\title{Determine statistical significance of PCA scores.}
\usage{
JackStraw(
object,
reduction = "pca",
assay = NULL,
dims = 20,
num.replicate = 100,
prop.freq = 0.01,
verbose = TRUE,
maxit = 1000
)
}
\arguments{
\item{object}{Seurat object}
\item{reduction}{DimReduc to use. ONLY PCA CURRENTLY SUPPORTED.}
\item{assay}{Assay used to calculate reduction.}
\item{dims}{Number of PCs to compute significance for}
\item{num.replicate}{Number of replicate samplings to perform}
\item{prop.freq}{Proportion of the data to randomly permute for each
replicate}
\item{verbose}{Print progress bar showing the number of replicates
that have been processed.}
\item{maxit}{maximum number of iterations to be performed by the irlba function of RunPCA}
}
\value{
Returns a Seurat object where JS(object = object[['pca']], slot = 'empirical')
represents p-values for each gene in the PCA analysis. If ProjectPCA is
subsequently run, JS(object = object[['pca']], slot = 'full') then
represents p-values for all genes.
}
\description{
Randomly permutes a subset of data, and calculates projected PCA scores for
these 'random' genes. Then compares the PCA scores for the 'random' genes
with the observed PCA scores to determine statistical signifance. End result
is a p-value for each gene's association with each principal component.
}
\examples{
\dontrun{
pbmc_small = suppressWarnings(JackStraw(pbmc_small))
head(JS(object = pbmc_small[['pca']], slot = 'empirical'))
}
}
\references{
Inspired by Chung et al, Bioinformatics (2014)
}
Seurat/man/SplitObject.Rd 0000644 0001762 0000144 00000002152 13617632030 015002 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{SplitObject}
\alias{SplitObject}
\title{Splits object into a list of subsetted objects.}
\usage{
SplitObject(object, split.by = "ident")
}
\arguments{
\item{object}{Seurat object}
\item{split.by}{Attribute for splitting. Default is "ident". Currently
only supported for class-level (i.e. non-quantitative) attributes.}
}
\value{
A named list of Seurat objects, each containing a subset of cells
from the original object.
}
\description{
Splits object based on a single attribute into a list of subsetted objects,
one for each level of the attribute. For example, useful for taking an object
that contains cells from many patients, and subdividing it into
patient-specific objects.
}
\examples{
# Assign the test object a three level attribute
groups <- sample(c("group1", "group2", "group3"), size = 80, replace = TRUE)
names(groups) <- colnames(pbmc_small)
pbmc_small <- AddMetaData(object = pbmc_small, metadata = groups, col.name = "group")
obj.list <- SplitObject(pbmc_small, split.by = "group")
}
Seurat/man/LabelClusters.Rd 0000644 0001762 0000144 00000002232 13617632030 015323 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{LabelClusters}
\alias{LabelClusters}
\title{Label clusters on a ggplot2-based scatter plot}
\usage{
LabelClusters(
plot,
id,
clusters = NULL,
labels = NULL,
split.by = NULL,
repel = TRUE,
...
)
}
\arguments{
\item{plot}{A ggplot2-based scatter plot}
\item{id}{Name of variable used for coloring scatter plot}
\item{clusters}{Vector of cluster ids to label}
\item{labels}{Custom labels for the clusters}
\item{split.by}{Split labels by some grouping label, useful when using
\code{\link[ggplot2]{facet_wrap}} or \code{\link[ggplot2]{facet_grid}}}
\item{repel}{Use \code{geom_text_repel} to create nicely-repelled labels}
\item{...}{Extra parameters to \code{\link[ggrepel]{geom_text_repel}}, such as \code{size}}
}
\value{
A ggplot2-based scatter plot with cluster labels
}
\description{
Label clusters on a ggplot2-based scatter plot
}
\examples{
plot <- DimPlot(object = pbmc_small)
LabelClusters(plot = plot, id = 'ident')
}
\seealso{
\code{\link[ggrepel]{geom_text_repel}} \code{\link[ggplot2]{geom_text}}
}
Seurat/man/FindClusters.Rd 0000644 0001762 0000144 00000006420 13617632030 015167 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/clustering.R
\name{FindClusters}
\alias{FindClusters}
\alias{FindClusters.default}
\alias{FindClusters.Seurat}
\title{Cluster Determination}
\usage{
FindClusters(object, ...)
\method{FindClusters}{default}(
object,
modularity.fxn = 1,
initial.membership = NULL,
weights = NULL,
node.sizes = NULL,
resolution = 0.8,
method = "matrix",
algorithm = 1,
n.start = 10,
n.iter = 10,
random.seed = 0,
group.singletons = TRUE,
temp.file.location = NULL,
edge.file.name = NULL,
verbose = TRUE,
...
)
\method{FindClusters}{Seurat}(
object,
graph.name = NULL,
modularity.fxn = 1,
initial.membership = NULL,
weights = NULL,
node.sizes = NULL,
resolution = 0.8,
method = "matrix",
algorithm = 1,
n.start = 10,
n.iter = 10,
random.seed = 0,
group.singletons = TRUE,
temp.file.location = NULL,
edge.file.name = NULL,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{modularity.fxn}{Modularity function (1 = standard; 2 = alternative).}
\item{initial.membership, weights, node.sizes}{Parameters to pass to the Python leidenalg function.}
\item{resolution}{Value of the resolution parameter, use a value above
(below) 1.0 if you want to obtain a larger (smaller) number of communities.}
\item{method}{Method for running leiden (defaults to matrix which is fast for small datasets).
Enable method = "igraph" to avoid casting large data to a dense matrix.}
\item{algorithm}{Algorithm for modularity optimization (1 = original Louvain
algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM
algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python.}
\item{n.start}{Number of random starts.}
\item{n.iter}{Maximal number of iterations per random start.}
\item{random.seed}{Seed of the random number generator.}
\item{group.singletons}{Group singletons into nearest cluster. If FALSE, assign all singletons to
a "singleton" group}
\item{temp.file.location}{Directory where intermediate files will be written.
Specify the ABSOLUTE path.}
\item{edge.file.name}{Edge file to use as input for modularity optimizer jar.}
\item{verbose}{Print output}
\item{graph.name}{Name of graph to use for the clustering algorithm}
}
\value{
Returns a Seurat object where the idents have been updated with new cluster info;
latest clustering results will be stored in object metadata under 'seurat_clusters'.
Note that 'seurat_clusters' will be overwritten everytime FindClusters is run
}
\description{
Identify clusters of cells by a shared nearest neighbor (SNN) modularity
optimization based clustering algorithm. First calculate k-nearest neighbors
and construct the SNN graph. Then optimize the modularity function to
determine clusters. For a full description of the algorithms, see Waltman and
van Eck (2013) \emph{The European Physical Journal B}. Thanks to Nigel
Delaney (evolvedmicrobe@github) for the rewrite of the Java modularity
optimizer code in Rcpp!
}
\details{
To run Leiden algorithm, you must first install the leidenalg python
package (e.g. via pip install leidenalg), see Traag et al (2018).
}
Seurat/man/RunICA.Rd 0000644 0001762 0000144 00000004362 13617632030 013646 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{RunICA}
\alias{RunICA}
\alias{RunICA.default}
\alias{RunICA.Assay}
\alias{RunICA.Seurat}
\title{Run Independent Component Analysis on gene expression}
\usage{
RunICA(object, ...)
\method{RunICA}{default}(
object,
assay = NULL,
nics = 50,
rev.ica = FALSE,
ica.function = "icafast",
verbose = TRUE,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.name = "ica",
reduction.key = "ica_",
seed.use = 42,
...
)
\method{RunICA}{Assay}(
object,
assay = NULL,
features = NULL,
nics = 50,
rev.ica = FALSE,
ica.function = "icafast",
verbose = TRUE,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.name = "ica",
reduction.key = "ica_",
seed.use = 42,
...
)
\method{RunICA}{Seurat}(
object,
assay = NULL,
features = NULL,
nics = 50,
rev.ica = FALSE,
ica.function = "icafast",
verbose = TRUE,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.name = "ica",
reduction.key = "IC_",
seed.use = 42,
...
)
}
\arguments{
\item{object}{Seurat object}
\item{\dots}{Additional arguments to be passed to fastica}
\item{assay}{Name of Assay ICA is being run on}
\item{nics}{Number of ICs to compute}
\item{rev.ica}{By default, computes the dimensional reduction on the cell x
feature matrix. Setting to true will compute it on the transpose (feature x cell
matrix).}
\item{ica.function}{ICA function from ica package to run (options: icafast,
icaimax, icajade)}
\item{verbose}{Print the top genes associated with high/low loadings for
the ICs}
\item{ndims.print}{ICs to print genes for}
\item{nfeatures.print}{Number of genes to print for each IC}
\item{reduction.name}{dimensional reduction name}
\item{reduction.key}{dimensional reduction key, specifies the string before
the number for the dimension names.}
\item{seed.use}{Set a random seed. Setting NULL will not set a seed.}
\item{features}{Features to compute ICA on}
}
\description{
Run fastica algorithm from the ica package for ICA dimensionality reduction.
For details about stored ICA calculation parameters, see
\code{PrintICAParams}.
}
Seurat/man/DietSeurat.Rd 0000644 0001762 0000144 00000002076 13617632030 014636 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{DietSeurat}
\alias{DietSeurat}
\title{Slim down a Seurat object}
\usage{
DietSeurat(
object,
counts = TRUE,
data = TRUE,
scale.data = FALSE,
features = NULL,
assays = NULL,
dimreducs = NULL,
graphs = NULL
)
}
\arguments{
\item{object}{Seurat object}
\item{counts}{Preserve the count matrices for the assays specified}
\item{data}{Preserve the data slot for the assays specified}
\item{scale.data}{Preserve the scale.data slot for the assays specified}
\item{features}{Only keep a subset of features, defaults to all features}
\item{assays}{Only keep a subset of assays specified here}
\item{dimreducs}{Only keep a subset of DimReducs specified here (if NULL,
remove all DimReducs)}
\item{graphs}{Only keep a subset of Graphs specified here (if NULL, remove
all Graphs)}
}
\description{
Keep only certain aspects of the Seurat object. Can be useful in functions that utilize merge as
it reduces the amount of data in the merge.
}
Seurat/man/UpdateSeuratObject.Rd 0000644 0001762 0000144 00000001232 13617632030 016313 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{UpdateSeuratObject}
\alias{UpdateSeuratObject}
\title{Update old Seurat object to accomodate new features}
\usage{
UpdateSeuratObject(object)
}
\arguments{
\item{object}{Seurat object}
}
\value{
Returns a Seurat object compatible with latest changes
}
\description{
Updates Seurat objects to new structure for storing data/calculations.
For Seurat v3 objects, will validate object structure ensuring all keys and feature
names are formed properly.
}
\examples{
\dontrun{
updated_seurat_object = UpdateSeuratObject(object = old_seurat_object)
}
}
Seurat/man/Loadings.Rd 0000644 0001762 0000144 00000002453 13617632030 014324 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Loadings}
\alias{Loadings}
\alias{Loadings<-}
\alias{Loadings.DimReduc}
\alias{Loadings.Seurat}
\alias{Loadings<-.DimReduc}
\title{Get feature loadings}
\usage{
Loadings(object, ...)
Loadings(object, ...) <- value
\method{Loadings}{DimReduc}(object, projected = FALSE, ...)
\method{Loadings}{Seurat}(object, reduction = "pca", projected = FALSE, ...)
\method{Loadings}{DimReduc}(object, projected = TRUE, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{Feature loadings to add}
\item{projected}{Pull the projected feature loadings?}
\item{reduction}{Name of reduction to pull feature loadings for}
}
\description{
Get feature loadings
Add feature loadings
}
\examples{
# Get the feature loadings for a given DimReduc
Loadings(object = pbmc_small[["pca"]])[1:5,1:5]
# Get the feature loadings for a specified DimReduc in a Seurat object
Loadings(object = pbmc_small, reduction = "pca")[1:5,1:5]
# Set the feature loadings for a given DimReduc
new.loadings <- Loadings(object = pbmc_small[["pca"]])
new.loadings <- new.loadings + 0.01
Loadings(object = pbmc_small[["pca"]]) <- new.loadings
}
Seurat/man/DefaultAssay.Rd 0000644 0001762 0000144 00000002561 13617632030 015151 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{DefaultAssay}
\alias{DefaultAssay}
\alias{DefaultAssay<-}
\alias{DefaultAssay.Assay}
\alias{DefaultAssay.DimReduc}
\alias{DefaultAssay.Graph}
\alias{DefaultAssay.Seurat}
\alias{DefaultAssay.SeuratCommand}
\alias{DefaultAssay<-.Seurat}
\title{Get and set the default assay}
\usage{
DefaultAssay(object, ...)
DefaultAssay(object, ...) <- value
\method{DefaultAssay}{Assay}(object, ...)
\method{DefaultAssay}{DimReduc}(object, ...)
\method{DefaultAssay}{Graph}(object, ...)
\method{DefaultAssay}{Seurat}(object, ...)
\method{DefaultAssay}{SeuratCommand}(object, ...)
\method{DefaultAssay}{Seurat}(object, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{Name of assay to set as default}
}
\value{
The name of the default assay
An object with the new default assay
}
\description{
Get and set the default assay
}
\examples{
# Get current default assay
DefaultAssay(object = pbmc_small)
# Create dummy new assay to demo switching default assays
new.assay <- pbmc_small[["RNA"]]
Key(object = new.assay) <- "RNA2_"
pbmc_small[["RNA2"]] <- new.assay
# switch default assay to RNA2
DefaultAssay(object = pbmc_small) <- "RNA2"
DefaultAssay(object = pbmc_small)
}
Seurat/man/DotPlot.Rd 0000644 0001762 0000144 00000004337 13617632030 014154 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{DotPlot}
\alias{DotPlot}
\alias{SplitDotPlotGG}
\title{Dot plot visualization}
\usage{
DotPlot(
object,
assay = NULL,
features,
cols = c("lightgrey", "blue"),
col.min = -2.5,
col.max = 2.5,
dot.min = 0,
dot.scale = 6,
group.by = NULL,
split.by = NULL,
scale.by = "radius",
scale.min = NA,
scale.max = NA
)
}
\arguments{
\item{object}{Seurat object}
\item{assay}{Name of assay to use, defaults to the active assay}
\item{features}{Input vector of features}
\item{cols}{Colors to plot, can pass a single character giving the name of
a palette from \code{RColorBrewer::brewer.pal.info}}
\item{col.min}{Minimum scaled average expression threshold (everything smaller
will be set to this)}
\item{col.max}{Maximum scaled average expression threshold (everything larger
will be set to this)}
\item{dot.min}{The fraction of cells at which to draw the smallest dot
(default is 0). All cell groups with less than this expressing the given
gene will have no dot drawn.}
\item{dot.scale}{Scale the size of the points, similar to cex}
\item{group.by}{Factor to group the cells by}
\item{split.by}{Factor to split the groups by (replicates the functionality of the old SplitDotPlotGG);
see \code{\link{FetchData}} for more details}
\item{scale.by}{Scale the size of the points by 'size' or by 'radius'}
\item{scale.min}{Set lower limit for scaling, use NA for default}
\item{scale.max}{Set upper limit for scaling, use NA for default}
}
\value{
A ggplot object
}
\description{
Intuitive way of visualizing how feature expression changes across different
identity classes (clusters). The size of the dot encodes the percentage of
cells within a class, while the color encodes the AverageExpression level
across all cells within a class (blue is high).
}
\examples{
cd_genes <- c("CD247", "CD3E", "CD9")
DotPlot(object = pbmc_small, features = cd_genes)
pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE)
DotPlot(object = pbmc_small, features = cd_genes, split.by = 'groups')
}
\seealso{
\code{RColorBrewer::brewer.pal.info}
}
Seurat/man/PlotClusterTree.Rd 0000644 0001762 0000144 00000001125 13617632030 015657 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{PlotClusterTree}
\alias{PlotClusterTree}
\title{Plot clusters as a tree}
\usage{
PlotClusterTree(object, ...)
}
\arguments{
\item{object}{Seurat object}
\item{\dots}{Additional arguments to ape::plot.phylo}
}
\value{
Plots dendogram (must be precomputed using BuildClusterTree), returns no value
}
\description{
Plots previously computed tree (from BuildClusterTree)
}
\examples{
pbmc_small <- BuildClusterTree(object = pbmc_small)
PlotClusterTree(object = pbmc_small)
}
Seurat/man/FindMarkers.Rd 0000644 0001762 0000144 00000022350 13617632030 014767 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/differential_expression.R
\name{FindMarkers}
\alias{FindMarkers}
\alias{FindMarkersNode}
\alias{FindMarkers.default}
\alias{FindMarkers.Seurat}
\title{Gene expression markers of identity classes}
\usage{
FindMarkers(object, ...)
\method{FindMarkers}{default}(
object,
slot = "data",
counts = numeric(),
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
reduction = NULL,
logfc.threshold = 0.25,
test.use = "wilcox",
min.pct = 0.1,
min.diff.pct = -Inf,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
...
)
\method{FindMarkers}{Seurat}(
object,
ident.1 = NULL,
ident.2 = NULL,
group.by = NULL,
subset.ident = NULL,
assay = NULL,
slot = "data",
reduction = NULL,
features = NULL,
logfc.threshold = 0.25,
test.use = "wilcox",
min.pct = 0.1,
min.diff.pct = -Inf,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods and to specific DE methods}
\item{slot}{Slot to pull data from; note that if \code{test.use} is "negbinom", "poisson", or "DESeq2",
\code{slot} will be set to "counts"}
\item{counts}{Count matrix if using scale.data for DE tests. This is used for
computing pct.1 and pct.2 and for filtering features based on fraction
expressing}
\item{cells.1}{Vector of cell names belonging to group 1}
\item{cells.2}{Vector of cell names belonging to group 2}
\item{features}{Genes to test. Default is to use all genes}
\item{reduction}{Reduction to use in differential expression testing - will test for DE on cell embeddings}
\item{logfc.threshold}{Limit testing to genes which show, on average, at least
X-fold difference (log-scale) between the two groups of cells. Default is 0.25
Increasing logfc.threshold speeds up the function, but can miss weaker signals.}
\item{test.use}{Denotes which test to use. Available options are:
\itemize{
\item{"wilcox"} : Identifies differentially expressed genes between two
groups of cells using a Wilcoxon Rank Sum test (default)
\item{"bimod"} : Likelihood-ratio test for single cell gene expression,
(McDavid et al., Bioinformatics, 2013)
\item{"roc"} : Identifies 'markers' of gene expression using ROC analysis.
For each gene, evaluates (using AUC) a classifier built on that gene alone,
to classify between two groups of cells. An AUC value of 1 means that
expression values for this gene alone can perfectly classify the two
groupings (i.e. Each of the cells in cells.1 exhibit a higher level than
each of the cells in cells.2). An AUC value of 0 also means there is perfect
classification, but in the other direction. A value of 0.5 implies that
the gene has no predictive power to classify the two groups. Returns a
'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially
expressed genes.
\item{"t"} : Identify differentially expressed genes between two groups of
cells using the Student's t-test.
\item{"negbinom"} : Identifies differentially expressed genes between two
groups of cells using a negative binomial generalized linear model.
Use only for UMI-based datasets
\item{"poisson"} : Identifies differentially expressed genes between two
groups of cells using a poisson generalized linear model.
Use only for UMI-based datasets
\item{"LR"} : Uses a logistic regression framework to determine differentially
expressed genes. Constructs a logistic regression model predicting group
membership based on each feature individually and compares this to a null
model with a likelihood ratio test.
\item{"MAST"} : Identifies differentially expressed genes between two groups
of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST
package to run the DE testing.
\item{"DESeq2"} : Identifies differentially expressed genes between two groups
of cells based on a model using DESeq2 which uses a negative binomial
distribution (Love et al, Genome Biology, 2014).This test does not support
pre-filtering of genes based on average difference (or percent detection rate)
between cell groups. However, genes may be pre-filtered based on their
minimum detection rate (min.pct) across both cell groups. To use this method,
please install DESeq2, using the instructions at
https://bioconductor.org/packages/release/bioc/html/DESeq2.html
}}
\item{min.pct}{only test genes that are detected in a minimum fraction of
min.pct cells in either of the two populations. Meant to speed up the function
by not testing genes that are very infrequently expressed. Default is 0.1}
\item{min.diff.pct}{only test genes that show a minimum difference in the
fraction of detection between the two groups. Set to -Inf by default}
\item{verbose}{Print a progress bar once expression testing begins}
\item{only.pos}{Only return positive markers (FALSE by default)}
\item{max.cells.per.ident}{Down sample each identity class to a max number.
Default is no downsampling. Not activated by default (set to Inf)}
\item{random.seed}{Random seed for downsampling}
\item{latent.vars}{Variables to test, used only when \code{test.use} is one of
'LR', 'negbinom', 'poisson', or 'MAST'}
\item{min.cells.feature}{Minimum number of cells expressing the feature in at least one
of the two groups, currently only used for poisson and negative binomial tests}
\item{min.cells.group}{Minimum number of cells in one of the groups}
\item{pseudocount.use}{Pseudocount to add to averaged expression values when
calculating logFC. 1 by default.}
\item{ident.1}{Identity class to define markers for; pass an object of class
\code{phylo} or 'clustertree' to find markers for a node in a cluster tree;
passing 'clustertree' requires \code{\link{BuildClusterTree}} to have been run}
\item{ident.2}{A second identity class for comparison; if \code{NULL},
use all other cells for comparison; if an object of class \code{phylo} or
'clustertree' is passed to \code{ident.1}, must pass a node to find markers for}
\item{group.by}{Regroup cells into a different identity class prior to performing differential expression (see example)}
\item{subset.ident}{Subset a particular identity class prior to regrouping. Only relevant if group.by is set (see example)}
\item{assay}{Assay to use in differential expression testing}
}
\value{
data.frame with a ranked list of putative markers as rows, and associated
statistics as columns (p-values, ROC score, etc., depending on the test used (\code{test.use})). The following columns are always present:
\itemize{
\item \code{avg_logFC}: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group
\item \code{pct.1}: The percentage of cells where the gene is detected in the first group
\item \code{pct.2}: The percentage of cells where the gene is detected in the second group
\item \code{p_val_adj}: Adjusted p-value, based on bonferroni correction using all genes in the dataset
}
}
\description{
Finds markers (differentially expressed genes) for identity classes
}
\details{
p-value adjustment is performed using bonferroni correction based on
the total number of genes in the dataset. Other correction methods are not
recommended, as Seurat pre-filters genes using the arguments above, reducing
the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values
should be interpreted cautiously, as the genes used for clustering are the
same genes tested for differential expression.
}
\examples{
# Find markers for cluster 2
markers <- FindMarkers(object = pbmc_small, ident.1 = 2)
head(x = markers)
# Take all cells in cluster 2, and find markers that separate cells in the 'g1' group (metadata
# variable 'group')
markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2")
head(x = markers)
# Pass 'clustertree' or an object of class phylo to ident.1 and
# a node to ident.2 as a replacement for FindMarkersNode
pbmc_small <- BuildClusterTree(object = pbmc_small)
markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5)
head(x = markers)
}
\references{
McDavid A, Finak G, Chattopadyay PK, et al. Data exploration,
quality control and testing in single-cell qPCR-based gene expression experiments.
Bioinformatics. 2013;29(4):461-467. doi:10.1093/bioinformatics/bts714
Trapnell C, et al. The dynamics and regulators of cell fate
decisions are revealed by pseudotemporal ordering of single cells. Nature
Biotechnology volume 32, pages 381-386 (2014)
Andrew McDavid, Greg Finak and Masanao Yajima (2017). MAST: Model-based
Analysis of Single Cell Transcriptomics. R package version 1.2.1.
https://github.com/RGLab/MAST/
Love MI, Huber W and Anders S (2014). "Moderated estimation of
fold change and dispersion for RNA-seq data with DESeq2." Genome Biology.
https://bioconductor.org/packages/release/bioc/html/DESeq2.html
}
Seurat/man/CellSelector.Rd 0000644 0001762 0000144 00000002742 13617632030 015145 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{CellSelector}
\alias{CellSelector}
\alias{FeatureLocator}
\title{Cell selector}
\usage{
CellSelector(plot, object = NULL, ident = "SelectedCells", ...)
FeatureLocator(plot, ...)
}
\arguments{
\item{plot}{A ggplot2 plot}
\item{object}{An optional Seurat object; if passes, will return an object with
the identities of selected cells set to \code{ident}}
\item{ident}{An optional new identity class to assign the selected cells}
\item{...}{Extra parameters, such as dark.theme, recolor, or smooth for using a dark theme,
recoloring based on selected cells, or using a smooth scatterplot, respectively}
}
\value{
If \code{object} is \code{NULL}, the names of the points selected; otherwise,
a Seurat object with the selected cells identity classes set to \code{ident}
}
\description{
Select points on a scatterplot and get information about them
}
\examples{
\dontrun{
plot <- DimPlot(object = pbmc_small)
# Follow instructions in the terminal to select points
cells.located <- CellSelector(plot = plot)
cells.located
# Automatically set the identity class of selected cells and return a new Seurat object
pbmc_small <- CellSelector(plot = plot, object = pbmc_small, ident = 'SelectedCells')
}
}
\seealso{
\code{\link[graphics]{locator}} \code{\link[ggplot2]{ggplot_build}}
\code{\link[SDMTools]{pnt.in.poly}} \code{\link{DimPlot}} \code{\link{FeaturePlot}}
}
Seurat/man/PercentageFeatureSet.Rd 0000644 0001762 0000144 00000003232 13617632030 016625 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{PercentageFeatureSet}
\alias{PercentageFeatureSet}
\title{Calculate the percentage of all counts that belong to a given set of features}
\usage{
PercentageFeatureSet(
object,
pattern = NULL,
features = NULL,
col.name = NULL,
assay = NULL
)
}
\arguments{
\item{object}{A Seurat object}
\item{pattern}{A regex pattern to match features against}
\item{features}{A defined feature set. If features provided, will ignore the pattern matching}
\item{col.name}{Name in meta.data column to assign. If this is not null, returns a Seurat object
with the proportion of the feature set stored in metadata.}
\item{assay}{Assay to use}
}
\value{
Returns a vector with the proportion of the feature set or if md.name is set, returns a
Seurat object with the proportion of the feature set stored in metadata.
}
\description{
This function enables you to easily calculate the percentage of all the counts belonging to a
subset of the possible features for each cell. This is useful when trying to compute the percentage
of transcripts that map to mitochondrial genes for example. The calculation here is simply the
column sum of the matrix present in the counts slot for features belonging to the set divided by
the column sum for all features times 100.
}
\examples{
# Calculate the proportion of transcripts mapping to mitochondrial genes
# NOTE: The pattern provided works for human gene names. You may need to adjust depending on your
# system of interest
pbmc_small[["percent.mt"]] <- PercentageFeatureSet(object = pbmc_small, pattern = "^MT-")
}
Seurat/man/pbmc_small.Rd 0000644 0001762 0000144 00000002327 13617632030 014675 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{pbmc_small}
\alias{pbmc_small}
\title{A small example version of the PBMC dataset}
\format{A Seurat object with the following slots filled
\describe{
\item{assays}{
\itemize{Currently only contains one assay ("RNA" - scRNA-seq expression data)
\item{counts - Raw expression data}
\item{data - Normalized expression data}
\item{scale.data - Scaled expression data}
\item{var.features - names of the current features selected as variable}
\item{meta.features - Assay level metadata such as mean and variance}
}}
\item{meta.data}{Cell level metadata}
\item{active.assay}{Current default assay}
\item{active.ident}{Current default idents}
\item{graphs}{Neighbor graphs computed, currently stores the SNN}
\item{reductions}{Dimensional reductions: currently PCA and tSNE}
\item{version}{Seurat version used to create the object}
\item{commands}{Command history}
}}
\source{
\url{https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k}
}
\usage{
pbmc_small
}
\description{
A subsetted version of 10X Genomics' 3k PBMC dataset
}
\keyword{datasets}
Seurat/man/DimReduc-class.Rd 0000644 0001762 0000144 00000002441 13617632030 015360 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{DimReduc-class}
\alias{DimReduc-class}
\alias{DimReduc}
\title{The Dimmensional Reduction Class}
\description{
The DimReduc object stores a dimensionality reduction taken out in Seurat; each DimReduc
consists of a cell embeddings matrix, a feature loadings matrix, and a projected feature
loadings matrix.
}
\section{Slots}{
\describe{
\item{\code{cell.embeddings}}{Cell embeddings matrix (required)}
\item{\code{feature.loadings}}{Feature loadings matrix (optional)}
\item{\code{feature.loadings.projected}}{Projected feature loadings matrix (optional)}
\item{\code{assay.used}}{Name of assay used to generate \code{DimReduc} object}
\item{\code{global}}{Is this \code{DimReduc} global/persistent? If so, it will not be
removed when removing its associated assay}
\item{\code{stdev}}{A vector of standard deviations}
\item{\code{key}}{Key for the \code{DimReduc}, must be alphanumerics followed by an underscore}
\item{\code{jackstraw}}{A \code{\link{JackStrawData-class}} object associated with
this \code{DimReduc}}
\item{\code{misc}}{Utility slot for storing additional data associated with the
\code{DimReduc} (e.g. the total variance of the PCA)}
}}
Seurat/man/PolyFeaturePlot.Rd 0000644 0001762 0000144 00000003107 13617632030 015657 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{PolyFeaturePlot}
\alias{PolyFeaturePlot}
\title{Polygon FeaturePlot}
\usage{
PolyFeaturePlot(
object,
features,
cells = NULL,
poly.data = "spatial",
ncol = ceiling(x = length(x = features)/2),
min.cutoff = 0,
max.cutoff = NA,
common.scale = TRUE,
flip.coords = FALSE
)
}
\arguments{
\item{object}{Seurat object}
\item{features}{Vector of features to plot. Features can come from:
\itemize{
\item An \code{Assay} feature (e.g. a gene name - "MS4A1")
\item A column name from meta.data (e.g. mitochondrial percentage - "percent.mito")
\item A column name from a \code{DimReduc} object corresponding to the cell embedding values
(e.g. the PC 1 scores - "PC_1")
}}
\item{cells}{Vector of cells to plot (default is all cells)}
\item{poly.data}{Name of the polygon dataframe in the misc slot}
\item{ncol}{Number of columns to split the plot into}
\item{min.cutoff}{Vector of minimum and maximum cutoff values for each feature,
may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')}
\item{max.cutoff}{Vector of minimum and maximum cutoff values for each feature,
may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')}
\item{common.scale}{...}
\item{flip.coords}{Flip x and y coordinates}
}
\value{
Returns a ggplot object
}
\description{
Plot cells as polygons, rather than single points. Color cells by any value accessible by \code{\link{FetchData}}.
}
Seurat/man/CreateAssayObject.Rd 0000644 0001762 0000144 00000002164 13617632030 016116 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{CreateAssayObject}
\alias{CreateAssayObject}
\title{Create an Assay object}
\usage{
CreateAssayObject(counts, data, min.cells = 0, min.features = 0)
}
\arguments{
\item{counts}{Unnormalized data such as raw counts or TPMs}
\item{data}{Prenormalized data; if provided, do not pass \code{counts}}
\item{min.cells}{Include features detected in at least this many cells. Will
subset the counts matrix as well. To reintroduce excluded features, create a
new object with a lower cutoff.}
\item{min.features}{Include cells where at least this many features are
detected.}
}
\description{
Create an Assay object from a feature (e.g. gene) expression matrix. The
expected format of the input matrix is features x cells.
}
\details{
Non-unique cell or feature names are not allowed. Please make unique before
calling this function.
}
\examples{
pbmc_raw <- read.table(
file = system.file('extdata', 'pbmc_raw.txt', package = 'Seurat'),
as.is = TRUE
)
pbmc_rna <- CreateAssayObject(counts = pbmc_raw)
pbmc_rna
}
Seurat/man/merge.Seurat.Rd 0000644 0001762 0000144 00000003600 13617632030 015120 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{merge.Assay}
\alias{merge.Assay}
\alias{merge.Seurat}
\alias{merge}
\alias{MergeSeurat}
\alias{AddSamples}
\title{Merge Seurat Objects}
\usage{
\method{merge}{Assay}(x = NULL, y = NULL, add.cell.ids = NULL, merge.data = TRUE, ...)
\method{merge}{Seurat}(
x = NULL,
y = NULL,
add.cell.ids = NULL,
merge.data = TRUE,
project = "SeuratProject",
...
)
}
\arguments{
\item{x}{Object}
\item{y}{Object (or a list of multiple objects)}
\item{add.cell.ids}{A character vector of length(x = c(x, y)). Appends the
corresponding values to the start of each objects' cell names.}
\item{merge.data}{Merge the data slots instead of just merging the counts
(which requires renormalization). This is recommended if the same normalization
approach was applied to all objects.}
\item{...}{Arguments passed to other methods}
\item{project}{Sets the project name for the Seurat object.}
}
\value{
Merged object
}
\description{
Merge two or more objects.
}
\details{
When merging Seurat objects, the merge procedure will merge the Assay level
counts and potentially the data slots (depending on the merge.data parameter).
It will also merge the cell-level meta data that was stored with each object
and preserve the cell identities that were active in the objects pre-merge.
The merge will not preserve reductions, graphs, logged commands, or feature-level metadata
that were present in the original objects. If add.cell.ids isn't specified
and any cell names are duplicated, cell names will be appended with _X, where
X is the numeric index of the object in c(x, y).
}
\examples{
# merge two objects
merge(x = pbmc_small, y = pbmc_small)
# to merge more than two objects, pass one to x and a list of objects to y
merge(x = pbmc_small, y = c(pbmc_small, pbmc_small))
}
Seurat/man/RegroupIdents.Rd 0000644 0001762 0000144 00000001114 13617632030 015347 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{RegroupIdents}
\alias{RegroupIdents}
\title{Regroup idents based on meta.data info}
\usage{
RegroupIdents(object, metadata)
}
\arguments{
\item{object}{Seurat object}
\item{metadata}{Name of metadata column}
}
\value{
A Seurat object with the active idents regrouped
}
\description{
For cells in each ident, set a new identity based on the most common value
of a specified metadata column.
}
\examples{
pbmc_small <- RegroupIdents(pbmc_small, metadata = "groups")
}
Seurat/man/Key.Rd 0000644 0001762 0000144 00000002150 13617632030 013306 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Key}
\alias{Key}
\alias{Key<-}
\alias{Key.Assay}
\alias{Key.DimReduc}
\alias{Key.Seurat}
\alias{Key<-.Assay}
\alias{Key<-.DimReduc}
\title{Get a key}
\usage{
Key(object, ...)
Key(object, ...) <- value
\method{Key}{Assay}(object, ...)
\method{Key}{DimReduc}(object, ...)
\method{Key}{Seurat}(object, ...)
\method{Key}{Assay}(object, ...) <- value
\method{Key}{DimReduc}(object, ...) <- value
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{value}{Key value}
}
\description{
Get a key
Set a key
}
\examples{
# Get an Assay key
Key(object = pbmc_small[["RNA"]])
# Get a DimReduc key
Key(object = pbmc_small[["pca"]])
# Show all keys associated with a Seurat object
Key(object = pbmc_small)
# Set the key for an Assay
Key(object = pbmc_small[["RNA"]]) <- "newkey_"
Key(object = pbmc_small[["RNA"]])
# Set the key for DimReduc
Key(object = pbmc_small[["pca"]]) <- "newkey2_"
Key(object = pbmc_small[["pca"]])
}
Seurat/man/VizDimLoadings.Rd 0000644 0001762 0000144 00000002311 13617632030 015440 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{VizDimLoadings}
\alias{VizDimLoadings}
\title{Visualize Dimensional Reduction genes}
\usage{
VizDimLoadings(
object,
dims = 1:5,
nfeatures = 30,
col = "blue",
reduction = "pca",
projected = FALSE,
balanced = FALSE,
ncol = NULL,
combine = TRUE
)
}
\arguments{
\item{object}{Seurat object}
\item{dims}{Number of dimensions to display}
\item{nfeatures}{Number of genes to display}
\item{col}{Color of points to use}
\item{reduction}{Reduction technique to visualize results for}
\item{projected}{Use reduction values for full dataset (i.e. projected
dimensional reduction values)}
\item{balanced}{Return an equal number of genes with + and - scores. If
FALSE (default), returns the top genes ranked by the scores absolute values}
\item{ncol}{Number of columns to display}
\item{combine}{Combine plots into a single gg object; note that if TRUE;
themeing will not work when plotting multiple features}
}
\value{
A ggplot object
}
\description{
Visualize top genes associated with reduction components
}
\examples{
VizDimLoadings(object = pbmc_small)
}
Seurat/man/AugmentPlot.Rd 0000644 0001762 0000144 00000001502 13617632030 015015 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{AugmentPlot}
\alias{AugmentPlot}
\title{Augments ggplot2-based plot with a PNG image.}
\usage{
AugmentPlot(plot, width = 10, height = 10, dpi = 100)
}
\arguments{
\item{plot}{A ggplot object}
\item{width, height}{Width and height of PNG version of plot}
\item{dpi}{Plot resolution}
}
\value{
A ggplot object
}
\description{
Creates "vector-friendly" plots. Does this by saving a copy of the plot as a PNG file,
then adding the PNG image with \code{\link[ggplot2]{annotation_raster}} to a blank plot
of the same dimensions as \code{plot}. Please note: original legends and axes will be lost
during augmentation.
}
\examples{
\dontrun{
plot <- DimPlot(object = pbmc_small)
AugmentPlot(plot = plot)
}
}
Seurat/man/RowMergeSparseMatrices.Rd 0000644 0001762 0000144 00000001223 13617632030 017153 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{RowMergeSparseMatrices}
\alias{RowMergeSparseMatrices}
\title{Merge two matrices by rowname}
\usage{
RowMergeSparseMatrices(mat1, mat2)
}
\arguments{
\item{mat1}{First matrix}
\item{mat2}{Second matrix}
}
\value{
A merged matrix
Returns a sparse matrix
}
\description{
This function is for use on sparse matrices and
should not be run on a Seurat object.
}
\details{
Shared matrix rows (with the same row name) will be merged,
and unshared rows (with different names) will be filled
with zeros in the matrix not containing the row.
}
Seurat/man/ScaleData.Rd 0000644 0001762 0000144 00000007337 13617632030 014413 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/preprocessing.R
\name{ScaleData}
\alias{ScaleData}
\alias{ScaleData.default}
\alias{ScaleData.Assay}
\alias{ScaleData.Seurat}
\title{Scale and center the data.}
\usage{
ScaleData(object, ...)
\method{ScaleData}{default}(
object,
features = NULL,
vars.to.regress = NULL,
latent.data = NULL,
split.by = NULL,
model.use = "linear",
use.umi = FALSE,
do.scale = TRUE,
do.center = TRUE,
scale.max = 10,
block.size = 1000,
min.cells.to.block = 3000,
verbose = TRUE,
...
)
\method{ScaleData}{Assay}(
object,
features = NULL,
vars.to.regress = NULL,
latent.data = NULL,
split.by = NULL,
model.use = "linear",
use.umi = FALSE,
do.scale = TRUE,
do.center = TRUE,
scale.max = 10,
block.size = 1000,
min.cells.to.block = 3000,
verbose = TRUE,
...
)
\method{ScaleData}{Seurat}(
object,
features = NULL,
assay = NULL,
vars.to.regress = NULL,
split.by = NULL,
model.use = "linear",
use.umi = FALSE,
do.scale = TRUE,
do.center = TRUE,
scale.max = 10,
block.size = 1000,
min.cells.to.block = 3000,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{features}{Vector of features names to scale/center. Default is variable features.}
\item{vars.to.regress}{Variables to regress out (previously latent.vars in
RegressOut). For example, nUMI, or percent.mito.}
\item{latent.data}{Extra data to regress out, should be cells x latent data}
\item{split.by}{Name of variable in object metadata or a vector or factor defining
grouping of cells. See argument \code{f} in \code{\link[base]{split}} for more details}
\item{model.use}{Use a linear model or generalized linear model
(poisson, negative binomial) for the regression. Options are 'linear'
(default), 'poisson', and 'negbinom'}
\item{use.umi}{Regress on UMI count data. Default is FALSE for linear
modeling, but automatically set to TRUE if model.use is 'negbinom' or 'poisson'}
\item{do.scale}{Whether to scale the data.}
\item{do.center}{Whether to center the data.}
\item{scale.max}{Max value to return for scaled data. The default is 10.
Setting this can help reduce the effects of feautres that are only expressed in
a very small number of cells. If regressing out latent variables and using a
non-linear model, the default is 50.}
\item{block.size}{Default size for number of feautres to scale at in a single
computation. Increasing block.size may speed up calculations but at an
additional memory cost.}
\item{min.cells.to.block}{If object contains fewer than this number of cells,
don't block for scaling calculations.}
\item{verbose}{Displays a progress bar for scaling procedure}
\item{assay}{Name of Assay to scale}
}
\description{
Scales and centers features in the dataset. If variables are provided in vars.to.regress,
they are individually regressed against each feautre, and the resulting residuals are
then scaled and centered.
}
\details{
ScaleData now incorporates the functionality of the function formerly known
as RegressOut (which regressed out given the effects of provided variables
and then scaled the residuals). To make use of the regression functionality,
simply pass the variables you want to remove to the vars.to.regress parameter.
Setting center to TRUE will center the expression for each feautre by subtracting
the average expression for that feautre. Setting scale to TRUE will scale the
expression level for each feautre by dividing the centered feautre expression
levels by their standard deviations if center is TRUE and by their root mean
square otherwise.
}
Seurat/man/h5ad.Rd 0000644 0001762 0000144 00000007045 13617632030 013407 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{ReadH5AD}
\alias{ReadH5AD}
\alias{WriteH5AD}
\alias{ReadH5AD.character}
\alias{ReadH5AD.H5File}
\alias{WriteH5AD.Seurat}
\title{Read from and write to h5ad files}
\usage{
ReadH5AD(file, ...)
WriteH5AD(object, ...)
\method{ReadH5AD}{character}(file, assay = "RNA", layers = "data", verbose = TRUE, ...)
\method{ReadH5AD}{H5File}(file, assay = "RNA", layers = "data", verbose = TRUE, ...)
\method{WriteH5AD}{Seurat}(
object,
file,
assay = NULL,
graph = NULL,
verbose = TRUE,
overwrite = FALSE,
...
)
}
\arguments{
\item{file}{Name of h5ad file, or an H5File object for reading in}
\item{...}{arguments passed to other methods}
\item{object}{An object}
\item{assay}{Name of assay to store}
\item{layers}{Slot to store layers as; choose from 'counts' or 'data'; pass
\code{FALSE} to not pull layers; may pass one value of 'counts' or 'data' for
each layer in the H5AD file, must be in order}
\item{verbose}{Show progress updates}
\item{graph}{Name of graph to write out, defaults to \code{paste0(assay, '_snn')}}
\item{overwrite}{Overwrite existing file}
}
\value{
\code{ReadH5AD}: A Seurat object with data from the h5ad file
\code{WriteH5AD}: None, writes to disk
}
\description{
Utilize the Anndata h5ad file format for storing and sharing single-cell expression
data. Provided are tools for writing objects to h5ad files, as well as reading
h5ad files into a Seurat object
}
\details{
\code{ReadH5AD} and \code{WriteH5AD} will try to automatically fill slots based
on data type and presence. For example, objects will be filled with scaled and
normalized data if \code{adata.X} is a dense matrix and \code{raw} is present
(when reading), or if the \code{scale.data} slot is filled (when writing). The
following is a list of how objects will be filled
\describe{
\item{\code{adata.X} is dense and \code{adata.raw} is filled; \code{ScaleData} is filled}{Objects will be filled with scaled and normalized data}
\item{
\code{adata.X} is sparse and \code{adata.raw} is filled; \code{NormalizeData} has been run, \code{ScaleData} has not been run
}{
Objects will be filled with normalized and raw data
}
\item{\code{adata.X} is sparse and \code{adata.raw} is not filled; \code{NormalizeData} has not been run}{Objects will be filled with raw data only}
}
In addition, dimensional reduction information and nearest-neighbor graphs will
be searched for and added if and only if scaled data is being added.
When reading: project name is \code{basename(file)}; identity classes will be
set as the project name; all cell-level metadata from \code{adata.obs} will be
taken; feature level metadata from \code{data.var} and \code{adata.raw.var}
(if present) will be merged and stored in assay \code{meta.features}; highly
variable features will be set if \code{highly_variable} is present in feature-level
metadata; dimensional reduction objects will be given the assay name provided
to the function call; graphs will be named \code{assay_method} if method is
present, otherwise \code{assay_adata}
When writing: only one assay will be written; all dimensional reductions and
graphs associated with that assay will be stored, no other reductions or graphs
will be written; active identity classes will be stored in \code{adata.obs} as
\code{active_ident}
}
\note{
\code{WriteH5AD} is not currently functional, please use \code{\link{as.loom}} instead
}
\seealso{
\code{\link{as.loom}}
}
Seurat/man/BuildClusterTree.Rd 0000644 0001762 0000144 00000003613 13617632030 016004 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tree.R
\name{BuildClusterTree}
\alias{BuildClusterTree}
\title{Phylogenetic Analysis of Identity Classes}
\usage{
BuildClusterTree(
object,
assay = NULL,
features = NULL,
dims = NULL,
graph = NULL,
slot = "data",
reorder = FALSE,
reorder.numeric = FALSE,
verbose = TRUE
)
}
\arguments{
\item{object}{Seurat object}
\item{assay}{Assay to use for the analysis.}
\item{features}{Genes to use for the analysis. Default is the set of
variable genes (\code{VariableFeatures(object = object)})}
\item{dims}{If set, tree is calculated in PCA space; overrides \code{features}}
\item{graph}{If graph is passed, build tree based on graph connectivity between
clusters; overrides \code{dims} and \code{features}}
\item{slot}{Slot to use; will be overriden by \code{use.scale} and \code{use.counts}}
\item{reorder}{Re-order identity classes (factor ordering), according to
position on the tree. This groups similar classes together which can be
helpful, for example, when drawing violin plots.}
\item{reorder.numeric}{Re-order identity classes according to position on
the tree, assigning a numeric value ('1' is the leftmost node)}
\item{verbose}{Show progress updates}
}
\value{
A Seurat object where the cluster tree can be accessed with \code{\link{Tool}}
}
\description{
Constructs a phylogenetic tree relating the 'average' cell from each
identity class. Tree is estimated based on a distance matrix constructed in
either gene expression space or PCA space.
}
\details{
Note that the tree is calculated for an 'average' cell, so gene expression
or PC scores are averaged across all cells in an identity class before the
tree is constructed.
}
\examples{
pbmc_small
pbmc_small <- BuildClusterTree(object = pbmc_small)
Tool(object = pbmc_small, slot = 'BuildClusterTree')
}
Seurat/man/TopCells.Rd 0000644 0001762 0000144 00000001565 13617632030 014314 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{TopCells}
\alias{TopCells}
\title{Find cells with highest scores for a given dimensional reduction technique}
\usage{
TopCells(object, dim = 1, ncells = 20, balanced = FALSE, ...)
}
\arguments{
\item{object}{DimReduc object}
\item{dim}{Dimension to use}
\item{ncells}{Number of cells to return}
\item{balanced}{Return an equal number of cells with both + and - scores.}
\item{...}{Extra parameters passed to \code{\link{Embeddings}}}
}
\value{
Returns a vector of cells
}
\description{
Return a list of genes with the strongest contribution to a set of components
}
\examples{
pbmc_small
head(TopCells(object = pbmc_small[["pca"]]))
# Can specify which dimension and how many cells to return
TopCells(object = pbmc_small[["pca"]], dim = 2, ncells = 5)
}
Seurat/man/FindAllMarkers.Rd 0000644 0001762 0000144 00000013431 13617632030 015420 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/differential_expression.R
\name{FindAllMarkers}
\alias{FindAllMarkers}
\alias{FindAllMarkersNode}
\title{Gene expression markers for all identity classes}
\usage{
FindAllMarkers(
object,
assay = NULL,
features = NULL,
logfc.threshold = 0.25,
test.use = "wilcox",
slot = "data",
min.pct = 0.1,
min.diff.pct = -Inf,
node = NULL,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
return.thresh = 0.01,
...
)
}
\arguments{
\item{object}{An object}
\item{assay}{Assay to use in differential expression testing}
\item{features}{Genes to test. Default is to use all genes}
\item{logfc.threshold}{Limit testing to genes which show, on average, at least
X-fold difference (log-scale) between the two groups of cells. Default is 0.25
Increasing logfc.threshold speeds up the function, but can miss weaker signals.}
\item{test.use}{Denotes which test to use. Available options are:
\itemize{
\item{"wilcox"} : Identifies differentially expressed genes between two
groups of cells using a Wilcoxon Rank Sum test (default)
\item{"bimod"} : Likelihood-ratio test for single cell gene expression,
(McDavid et al., Bioinformatics, 2013)
\item{"roc"} : Identifies 'markers' of gene expression using ROC analysis.
For each gene, evaluates (using AUC) a classifier built on that gene alone,
to classify between two groups of cells. An AUC value of 1 means that
expression values for this gene alone can perfectly classify the two
groupings (i.e. Each of the cells in cells.1 exhibit a higher level than
each of the cells in cells.2). An AUC value of 0 also means there is perfect
classification, but in the other direction. A value of 0.5 implies that
the gene has no predictive power to classify the two groups. Returns a
'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially
expressed genes.
\item{"t"} : Identify differentially expressed genes between two groups of
cells using the Student's t-test.
\item{"negbinom"} : Identifies differentially expressed genes between two
groups of cells using a negative binomial generalized linear model.
Use only for UMI-based datasets
\item{"poisson"} : Identifies differentially expressed genes between two
groups of cells using a poisson generalized linear model.
Use only for UMI-based datasets
\item{"LR"} : Uses a logistic regression framework to determine differentially
expressed genes. Constructs a logistic regression model predicting group
membership based on each feature individually and compares this to a null
model with a likelihood ratio test.
\item{"MAST"} : Identifies differentially expressed genes between two groups
of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST
package to run the DE testing.
\item{"DESeq2"} : Identifies differentially expressed genes between two groups
of cells based on a model using DESeq2 which uses a negative binomial
distribution (Love et al, Genome Biology, 2014).This test does not support
pre-filtering of genes based on average difference (or percent detection rate)
between cell groups. However, genes may be pre-filtered based on their
minimum detection rate (min.pct) across both cell groups. To use this method,
please install DESeq2, using the instructions at
https://bioconductor.org/packages/release/bioc/html/DESeq2.html
}}
\item{slot}{Slot to pull data from; note that if \code{test.use} is "negbinom", "poisson", or "DESeq2",
\code{slot} will be set to "counts"}
\item{min.pct}{only test genes that are detected in a minimum fraction of
min.pct cells in either of the two populations. Meant to speed up the function
by not testing genes that are very infrequently expressed. Default is 0.1}
\item{min.diff.pct}{only test genes that show a minimum difference in the
fraction of detection between the two groups. Set to -Inf by default}
\item{node}{A node to find markers for and all its children; requires
\code{\link{BuildClusterTree}} to have been run previously; replaces \code{FindAllMarkersNode}}
\item{verbose}{Print a progress bar once expression testing begins}
\item{only.pos}{Only return positive markers (FALSE by default)}
\item{max.cells.per.ident}{Down sample each identity class to a max number.
Default is no downsampling. Not activated by default (set to Inf)}
\item{random.seed}{Random seed for downsampling}
\item{latent.vars}{Variables to test, used only when \code{test.use} is one of
'LR', 'negbinom', 'poisson', or 'MAST'}
\item{min.cells.feature}{Minimum number of cells expressing the feature in at least one
of the two groups, currently only used for poisson and negative binomial tests}
\item{min.cells.group}{Minimum number of cells in one of the groups}
\item{pseudocount.use}{Pseudocount to add to averaged expression values when
calculating logFC. 1 by default.}
\item{return.thresh}{Only return markers that have a p-value < return.thresh, or a power > return.thresh (if the test is ROC)}
\item{...}{Arguments passed to other methods and to specific DE methods}
}
\value{
Matrix containing a ranked list of putative markers, and associated
statistics (p-values, ROC score, etc.)
}
\description{
Finds markers (differentially expressed genes) for each of the identity classes in a dataset
}
\examples{
# Find markers for all clusters
all.markers <- FindAllMarkers(object = pbmc_small)
head(x = all.markers)
\dontrun{
# Pass a value to node as a replacement for FindAllMarkersNode
pbmc_small <- BuildClusterTree(object = pbmc_small)
all.markers <- FindAllMarkers(object = pbmc_small, node = 4)
head(x = all.markers)
}
}
Seurat/man/as.CellDataSet.Rd 0000644 0001762 0000144 00000001165 13617632030 015312 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{as.CellDataSet}
\alias{as.CellDataSet}
\alias{as.CellDataSet.Seurat}
\title{Convert objects to CellDataSet objects}
\usage{
as.CellDataSet(x, ...)
\method{as.CellDataSet}{Seurat}(x, assay = NULL, reduction = NULL, ...)
}
\arguments{
\item{x}{An object to convert to class \code{CellDataSet}}
\item{...}{Arguments passed to other methods}
\item{assay}{Assay to convert}
\item{reduction}{Name of DimReduc to set to main reducedDim in cds}
}
\description{
Convert objects to CellDataSet objects
}
Seurat/man/FetchData.Rd 0000644 0001762 0000144 00000001472 13617632030 014407 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{FetchData}
\alias{FetchData}
\title{Access cellular data}
\usage{
FetchData(object, vars, cells = NULL, slot = "data")
}
\arguments{
\item{object}{Seurat object}
\item{vars}{List of all variables to fetch, use keyword 'ident' to pull identity classes}
\item{cells}{Cells to collect data for (default is all cells)}
\item{slot}{Slot to pull feature data for}
}
\value{
A data frame with cells as rows and cellular data as columns
}
\description{
Retreives data (feature expression, PCA scores, metrics, etc.) for a set
of cells in a Seurat object
}
\examples{
pc1 <- FetchData(object = pbmc_small, vars = 'PC_1')
head(x = pc1)
head(x = FetchData(object = pbmc_small, vars = c('groups', 'ident')))
}
Seurat/man/FindVariableFeatures.Rd 0000644 0001762 0000144 00000011113 13617632030 016602 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/preprocessing.R
\name{FindVariableFeatures}
\alias{FindVariableFeatures}
\alias{FindVariableGenes}
\alias{FindVariableFeatures.default}
\alias{FindVariableFeatures.Assay}
\alias{FindVariableFeatures.Seurat}
\title{Find variable features}
\usage{
FindVariableFeatures(object, ...)
\method{FindVariableFeatures}{default}(
object,
selection.method = "vst",
loess.span = 0.3,
clip.max = "auto",
mean.function = FastExpMean,
dispersion.function = FastLogVMR,
num.bin = 20,
binning.method = "equal_width",
verbose = TRUE,
...
)
\method{FindVariableFeatures}{Assay}(
object,
selection.method = "vst",
loess.span = 0.3,
clip.max = "auto",
mean.function = FastExpMean,
dispersion.function = FastLogVMR,
num.bin = 20,
binning.method = "equal_width",
nfeatures = 2000,
mean.cutoff = c(0.1, 8),
dispersion.cutoff = c(1, Inf),
verbose = TRUE,
...
)
\method{FindVariableFeatures}{Seurat}(
object,
assay = NULL,
selection.method = "vst",
loess.span = 0.3,
clip.max = "auto",
mean.function = FastExpMean,
dispersion.function = FastLogVMR,
num.bin = 20,
binning.method = "equal_width",
nfeatures = 2000,
mean.cutoff = c(0.1, 8),
dispersion.cutoff = c(1, Inf),
verbose = TRUE,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{selection.method}{How to choose top variable features. Choose one of :
\itemize{
\item{vst:}{ First, fits a line to the relationship of log(variance) and
log(mean) using local polynomial regression (loess). Then standardizes the
feature values using the observed mean and expected variance (given by the
fitted line). Feature variance is then calculated on the standardized values
after clipping to a maximum (see clip.max parameter).}
\item{mean.var.plot (mvp):}{ First, uses a function to calculate average
expression (mean.function) and dispersion (dispersion.function) for each
feature. Next, divides features into num.bin (deafult 20) bins based on
their average expression, and calculates z-scores for dispersion within
each bin. The purpose of this is to identify variable features while
controlling for the strong relationship between variability and average
expression.}
\item{dispersion (disp):}{ selects the genes with the highest dispersion values}
}}
\item{loess.span}{(vst method) Loess span parameter used when fitting the
variance-mean relationship}
\item{clip.max}{(vst method) After standardization values larger than
clip.max will be set to clip.max; default is 'auto' which sets this value to
the square root of the number of cells}
\item{mean.function}{Function to compute x-axis value (average expression).
Default is to take the mean of the detected (i.e. non-zero) values}
\item{dispersion.function}{Function to compute y-axis value (dispersion).
Default is to take the standard deviation of all values}
\item{num.bin}{Total number of bins to use in the scaled analysis (default
is 20)}
\item{binning.method}{Specifies how the bins should be computed. Available
methods are:
\itemize{
\item{equal_width:}{ each bin is of equal width along the x-axis [default]}
\item{equal_frequency:}{ each bin contains an equal number of features (can
increase statistical power to detect overdispersed features at high
expression values, at the cost of reduced resolution along the x-axis)}
}}
\item{verbose}{show progress bar for calculations}
\item{nfeatures}{Number of features to select as top variable features;
only used when \code{selection.method} is set to \code{'dispersion'} or
\code{'vst'}}
\item{mean.cutoff}{A two-length numeric vector with low- and high-cutoffs for
feature means}
\item{dispersion.cutoff}{A two-length numeric vector with low- and high-cutoffs for
feature dispersions}
\item{assay}{Assay to use}
}
\description{
Identifies features that are outliers on a 'mean variability plot'.
}
\details{
For the mean.var.plot method:
Exact parameter settings may vary empirically from dataset to dataset, and
based on visual inspection of the plot. Setting the y.cutoff parameter to 2
identifies features that are more than two standard deviations away from the
average dispersion within a bin. The default X-axis function is the mean
expression level, and for Y-axis it is the log(Variance/mean). All mean/variance
calculations are not performed in log-space, but the results are reported in
log-space - see relevant functions for exact details.
}
Seurat/man/L2CCA.Rd 0000644 0001762 0000144 00000000521 13617632030 013342 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dimensional_reduction.R
\name{L2CCA}
\alias{L2CCA}
\title{L2-Normalize CCA}
\usage{
L2CCA(object, ...)
}
\arguments{
\item{object}{Seurat object}
\item{\dots}{Additional parameters to L2Dim.}
}
\description{
Perform l2 normalization on CCs
}
Seurat/man/CaseMatch.Rd 0000644 0001762 0000144 00000001113 13617632030 014404 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{CaseMatch}
\alias{CaseMatch}
\title{Match the case of character vectors}
\usage{
CaseMatch(search, match)
}
\arguments{
\item{search}{A vector of search terms}
\item{match}{A vector of characters whose case should be matched}
}
\value{
Values from search present in match with the case of match
}
\description{
Match the case of character vectors
}
\examples{
cd_genes <- c('Cd79b', 'Cd19', 'Cd200')
CaseMatch(search = cd_genes, match = rownames(x = pbmc_small))
}
Seurat/man/CreateDimReducObject.Rd 0000644 0001762 0000144 00000002647 13617632030 016540 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{CreateDimReducObject}
\alias{CreateDimReducObject}
\alias{SetDimReduction}
\title{Create a DimReduc object}
\usage{
CreateDimReducObject(
embeddings = new(Class = "matrix"),
loadings = new(Class = "matrix"),
projected = new(Class = "matrix"),
assay = NULL,
stdev = numeric(),
key = NULL,
global = FALSE,
jackstraw = NULL,
misc = list()
)
}
\arguments{
\item{embeddings}{A matrix with the cell embeddings}
\item{loadings}{A matrix with the feature loadings}
\item{projected}{A matrix with the projected feature loadings}
\item{assay}{Assay used to calculate this dimensional reduction}
\item{stdev}{Standard deviation (if applicable) for the dimensional reduction}
\item{key}{A character string to facilitate looking up features from a
specific DimReduc}
\item{global}{Specify this as a global reduction (useful for visualizations)}
\item{jackstraw}{Results from the JackStraw function}
\item{misc}{list for the user to store any additional information associated
with the dimensional reduction}
}
\description{
Create a DimReduc object
}
\examples{
data <- GetAssayData(pbmc_small[["RNA"]], slot = "scale.data")
pcs <- prcomp(x = data)
pca.dr <- CreateDimReducObject(
embeddings = pcs$rotation,
loadings = pcs$x,
stdev = pcs$sdev,
key = "PC",
assay = "RNA"
)
}
Seurat/man/as.sparse.Rd 0000644 0001762 0000144 00000003234 13617632030 014461 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R, R/utilities.R
\name{as.sparse}
\alias{as.sparse}
\alias{as.sparse.data.frame}
\alias{as.sparse.H5Group}
\alias{as.sparse.Matrix}
\alias{as.sparse.matrix}
\alias{as.data.frame.Matrix}
\title{Convert between data frames and sparse matrices}
\usage{
as.sparse(x, ...)
\method{as.sparse}{data.frame}(x, ...)
\method{as.sparse}{H5Group}(x, ...)
\method{as.sparse}{Matrix}(x, ...)
\method{as.sparse}{matrix}(x, ...)
\method{as.data.frame}{Matrix}(
x,
row.names = NULL,
optional = FALSE,
...,
stringsAsFactors = default.stringsAsFactors()
)
}
\arguments{
\item{x}{An object}
\item{...}{Arguments passed to other methods}
\item{row.names}{\code{NULL} or a character vector giving the row
names for the data frame. Missing values are not allowed.}
\item{optional}{logical. If \code{TRUE}, setting row names and
converting column names (to syntactic names: see
\code{\link[base]{make.names}}) is optional. Note that all of \R's
\pkg{base} package \code{as.data.frame()} methods use
\code{optional} only for column names treatment, basically with the
meaning of \code{\link[base]{data.frame}(*, check.names = !optional)}.
See also the \code{make.names} argument of the \code{matrix} method.}
\item{stringsAsFactors}{logical: should the character vector be converted
to a factor?}
}
\value{
\code{as.sparse}: A sparse representation of the input data
\code{as.data.frame.Matrix}: A data frame representation of the S4 Matrix
}
\description{
Convert between data frames and sparse matrices
}
Seurat/man/Command.Rd 0000644 0001762 0000144 00000001304 13617632030 014134 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Command}
\alias{Command}
\alias{Command.Seurat}
\title{Get SeuratCommands}
\usage{
Command(object, ...)
\method{Command}{Seurat}(object, command = NULL, value = NULL, ...)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{command}{Name of the command to pull, pass \code{NULL} to get the names of all commands run}
\item{value}{Name of the parameter to pull the value for}
}
\value{
Either a SeuratCommand object or the requested paramter value
}
\description{
Pull information on previously run commands in the Seurat object.
}
Seurat/man/MinMax.Rd 0000644 0001762 0000144 00000001271 13617632030 013752 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{MinMax}
\alias{MinMax}
\title{Apply a ceiling and floor to all values in a matrix}
\usage{
MinMax(data, min, max)
}
\arguments{
\item{data}{Matrix or data frame}
\item{min}{all values below this min value will be replaced with min}
\item{max}{all values above this max value will be replaced with max}
}
\value{
Returns matrix after performing these floor and ceil operations
}
\description{
Apply a ceiling and floor to all values in a matrix
}
\examples{
mat <- matrix(data = rbinom(n = 25, size = 20, prob = 0.2 ), nrow = 5)
mat
MinMax(data = mat, min = 4, max = 5)
}
Seurat/man/MULTIseqDemux.Rd 0000644 0001762 0000144 00000002465 13617632030 015175 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{MULTIseqDemux}
\alias{MULTIseqDemux}
\title{Demultiplex samples based on classification method from MULTI-seq (McGinnis et al., bioRxiv 2018)}
\usage{
MULTIseqDemux(
object,
assay = "HTO",
quantile = 0.7,
autoThresh = FALSE,
maxiter = 5,
qrange = seq(from = 0.1, to = 0.9, by = 0.05),
verbose = TRUE
)
}
\arguments{
\item{object}{Seurat object. Assumes that the specified assay data has been added}
\item{assay}{Name of the multiplexing assay (HTO by default)}
\item{quantile}{The quantile to use for classification}
\item{autoThresh}{Whether to perform automated threshold finding to define the best quantile. Default is FALSE}
\item{maxiter}{Maximum number of iterations if autoThresh = TRUE. Default is 5}
\item{qrange}{A range of possible quantile values to try if autoThresh = TRUE}
\item{verbose}{Prints the output}
}
\value{
A Seurat object with demultiplexing results stored at \code{object$MULTI_ID}
}
\description{
Identify singlets, doublets and negative cells from multiplexing experiments. Annotate singlets by tags.
}
\examples{
\dontrun{
object <- MULTIseqDemux(object)
}
}
\references{
\url{https://www.biorxiv.org/content/early/2018/08/08/387241}
}
Seurat/man/CollapseEmbeddingOutliers.Rd 0000644 0001762 0000144 00000002306 13617632030 017651 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{CollapseEmbeddingOutliers}
\alias{CollapseEmbeddingOutliers}
\title{Move outliers towards center on dimension reduction plot}
\usage{
CollapseEmbeddingOutliers(
object,
reduction = "umap",
dims = 1:2,
group.by = "ident",
outlier.sd = 2,
reduction.key = "UMAP_"
)
}
\arguments{
\item{object}{Seurat object}
\item{reduction}{Name of DimReduc to adjust}
\item{dims}{Dimensions to visualize}
\item{group.by}{Group (color) cells in different ways (for example, orig.ident)}
\item{outlier.sd}{Controls the outlier distance}
\item{reduction.key}{Key for DimReduc that is returned}
}
\value{
Returns a DimReduc object with the modified embeddings
}
\description{
Move outliers towards center on dimension reduction plot
}
\examples{
\dontrun{
pbmc_small <- FindClusters(pbmc_small, resolution = 1.1)
pbmc_small <- RunUMAP(pbmc_small, dims = 1:5)
DimPlot(pbmc_small, reduction = "umap")
pbmc_small[["umap_new"]] <- CollapseEmbeddingOutliers(pbmc_small,
reduction = "umap", reduction.key = 'umap_', outlier.sd = 0.5)
DimPlot(pbmc_small, reduction = "umap_new")
}
}
Seurat/man/L2Dim.Rd 0000644 0001762 0000144 00000001131 13617632030 013463 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dimensional_reduction.R
\name{L2Dim}
\alias{L2Dim}
\title{L2-normalization}
\usage{
L2Dim(object, reduction, new.dr = NULL, new.key = NULL)
}
\arguments{
\item{object}{Seurat object}
\item{reduction}{Dimensional reduction to normalize}
\item{new.dr}{name of new dimensional reduction to store
(default is olddr.l2)}
\item{new.key}{name of key for new dimensional reduction}
}
\value{
Returns a \code{\link{Seurat}} object
}
\description{
Perform l2 normalization on given dimensional reduction
}
Seurat/man/Embeddings.Rd 0000644 0001762 0000144 00000001470 13617632030 014623 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{Embeddings}
\alias{Embeddings}
\alias{Embeddings.DimReduc}
\alias{Embeddings.Seurat}
\title{Get cell embeddings}
\usage{
Embeddings(object, ...)
\method{Embeddings}{DimReduc}(object, ...)
\method{Embeddings}{Seurat}(object, reduction = "pca", ...)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{reduction}{Name of reduction to pull cell embeddings for}
}
\description{
Get cell embeddings
}
\examples{
# Get the embeddings directly from a DimReduc object
Embeddings(object = pbmc_small[["pca"]])[1:5, 1:5]
# Get the embeddings from a specific DimReduc in a Seurat object
Embeddings(object = pbmc_small, reduction = "pca")[1:5, 1:5]
}
Seurat/man/IntegrateData.Rd 0000644 0001762 0000144 00000005131 13617632030 015274 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{IntegrateData}
\alias{IntegrateData}
\title{Integrate data}
\usage{
IntegrateData(
anchorset,
new.assay.name = "integrated",
normalization.method = c("LogNormalize", "SCT"),
features = NULL,
features.to.integrate = NULL,
dims = 1:30,
k.weight = 100,
weight.reduction = NULL,
sd.weight = 1,
sample.tree = NULL,
preserve.order = FALSE,
do.cpp = TRUE,
eps = 0,
verbose = TRUE
)
}
\arguments{
\item{anchorset}{Results from FindIntegrationAnchors}
\item{new.assay.name}{Name for the new assay containing the integrated data}
\item{normalization.method}{Name of normalization method used: LogNormalize
or SCT}
\item{features}{Vector of features to use when computing the PCA to determine the weights. Only set
if you want a different set from those used in the anchor finding process}
\item{features.to.integrate}{Vector of features to integrate. By default, will use the features
used in anchor finding.}
\item{dims}{Number of PCs to use in the weighting procedure}
\item{k.weight}{Number of neighbors to consider when weighting}
\item{weight.reduction}{Dimension reduction to use when calculating anchor weights.
This can be either:
\itemize{
\item{A string, specifying the name of a dimension reduction present in all objects to be integrated}
\item{A vector of strings, specifying the name of a dimension reduction to use for each object to be integrated}
\item{A vector of Dimreduc objects, specifying the object to use for each object in the integration}
\item{NULL, in which case a new PCA will be calculated and used to calculate anchor weights}
}
Note that, if specified, the requested dimension reduction will only be used for calculating anchor weights in the
first merge between reference and query, as the merged object will subsequently contain more cells than was in
query, and weights will need to be calculated for all cells in the object.}
\item{sd.weight}{Controls the bandwidth of the Gaussian kernel for weighting}
\item{sample.tree}{Specify the order of integration. If NULL, will compute automatically.}
\item{preserve.order}{Do not reorder objects based on size for each pairwise integration.}
\item{do.cpp}{Run cpp code where applicable}
\item{eps}{Error bound on the neighbor finding algorithm (from \code{\link{RANN}})}
\item{verbose}{Print progress bars and output}
}
\value{
Returns a Seurat object with a new integrated Assay
}
\description{
Perform dataset integration using a pre-computed anchorset
}
Seurat/man/CellCycleScoring.Rd 0000644 0001762 0000144 00000002275 13617632030 015752 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{CellCycleScoring}
\alias{CellCycleScoring}
\title{Score cell cycle phases}
\usage{
CellCycleScoring(object, s.features, g2m.features, set.ident = FALSE, ...)
}
\arguments{
\item{object}{A Seurat object}
\item{s.features}{A vector of features associated with S phase}
\item{g2m.features}{A vector of features associated with G2M phase}
\item{set.ident}{If true, sets identity to phase assignments}
\item{...}{Arguments to be passed to \code{\link{AddModuleScore}}
Stashes old identities in 'old.ident'}
}
\value{
A Seurat object with the following columns added to object meta data: S.Score, G2M.Score, and Phase
}
\description{
Score cell cycle phases
}
\examples{
\dontrun{
# pbmc_small doesn't have any cell-cycle genes
# To run CellCycleScoring, please use a dataset with cell-cycle genes
# An example is available at http://satijalab.org/seurat/cell_cycle_vignette.html
pbmc_small <- CellCycleScoring(
object = pbmc_small,
g2m.features = cc.genes$g2m.genes,
s.features = cc.genes$s.genes
)
head(x = pbmc_small@meta.data)
}
}
\seealso{
\code{AddModuleScore}
}
Seurat/man/FindTransferAnchors.Rd 0000644 0001762 0000144 00000005015 13617632030 016464 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{FindTransferAnchors}
\alias{FindTransferAnchors}
\title{Find transfer anchors}
\usage{
FindTransferAnchors(
reference,
query,
normalization.method = c("LogNormalize", "SCT"),
reference.assay = NULL,
query.assay = NULL,
reduction = "pcaproject",
project.query = FALSE,
features = NULL,
npcs = 30,
l2.norm = TRUE,
dims = 1:30,
k.anchor = 5,
k.filter = 200,
k.score = 30,
max.features = 200,
nn.method = "rann",
eps = 0,
approx.pca = TRUE,
verbose = TRUE
)
}
\arguments{
\item{reference}{Seurat object to use as the reference}
\item{query}{Seurat object to use as the query}
\item{normalization.method}{Name of normalization method used: LogNormalize or SCT}
\item{reference.assay}{Assay to use from reference}
\item{query.assay}{Assay to use from query}
\item{reduction}{Dimensional reduction to perform when finding anchors. Options are:
\itemize{
\item{pcaproject: Project the PCA from the reference onto the query. We recommend using PCA
when reference and query datasets are from scRNA-seq}
\item{cca: Run a CCA on the reference and query }
}}
\item{project.query}{Project the PCA from the query dataset onto the reference. Use only in rare
cases where the query dataset has a much larger cell number, but the reference dataset has a
unique assay for transfer.}
\item{features}{Features to use for dimensional reduction}
\item{npcs}{Number of PCs to compute on reference. If null, then use an existing PCA structure in
the reference object}
\item{l2.norm}{Perform L2 normalization on the cell embeddings after dimensional reduction}
\item{dims}{Which dimensions to use from the reduction to specify the neighbor search space}
\item{k.anchor}{How many neighbors (k) to use when picking anchors}
\item{k.filter}{How many neighbors (k) to use when filtering anchors}
\item{k.score}{How many neighbors (k) to use when scoring anchors}
\item{max.features}{The maximum number of features to use when specifying the neighborhood search
space in the anchor filtering}
\item{nn.method}{Method for nearest neighbor finding. Options include: rann,
annoy}
\item{eps}{Error bound on the neighbor finding algorithm (from RANN)}
\item{approx.pca}{Use truncated singular value decomposition to approximate PCA}
\item{verbose}{Print progress bars and output}
}
\value{
Returns an AnchorSet object
}
\description{
Finds the transfer anchors
}
Seurat/man/DimPlot.Rd 0000644 0001762 0000144 00000007534 13617632030 014141 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R, R/convenience.R
\name{DimPlot}
\alias{DimPlot}
\alias{TSNEPlot}
\alias{PCAPlot}
\alias{ICAPlot}
\alias{UMAPPlot}
\title{Dimensional reduction plot}
\usage{
DimPlot(
object,
dims = c(1, 2),
cells = NULL,
cols = NULL,
pt.size = NULL,
reduction = NULL,
group.by = NULL,
split.by = NULL,
shape.by = NULL,
order = NULL,
label = FALSE,
label.size = 4,
repel = FALSE,
cells.highlight = NULL,
cols.highlight = "#DE2D26",
sizes.highlight = 1,
na.value = "grey50",
combine = TRUE,
ncol = NULL,
...
)
PCAPlot(object, ...)
TSNEPlot(object, ...)
UMAPPlot(object, ...)
}
\arguments{
\item{object}{Seurat object}
\item{dims}{Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions}
\item{cells}{Vector of cells to plot (default is all cells)}
\item{cols}{Vector of colors, each color corresponds to an identity class. This may also be a single character
or numeric value corresponding to a palette as specified by \code{\link[RColorBrewer]{brewer.pal.info}}.
By default, ggplot2 assigns colors. We also include a number of palettes from the pals package.
See \code{\link{DiscretePalette}} for details.}
\item{pt.size}{Adjust point size for plotting}
\item{reduction}{Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca}
\item{group.by}{Name of one or more metadata columns to group (color) cells by
(for example, orig.ident); pass 'ident' to group by identity class}
\item{split.by}{Name of a metadata column to split plot by;
see \code{\link{FetchData}} for more details}
\item{shape.by}{If NULL, all points are circles (default). You can specify any
cell attribute (that can be pulled with FetchData) allowing for both
different colors and different shapes on cells}
\item{order}{Specify the order of plotting for the idents. This can be
useful for crowded plots if points of interest are being buried. Provide
either a full list of valid idents or a subset to be plotted last (on top)}
\item{label}{Whether to label the clusters}
\item{label.size}{Sets size of labels}
\item{repel}{Repel labels}
\item{cells.highlight}{A list of character or numeric vectors of cells to
highlight. If only one group of cells desired, can simply
pass a vector instead of a list. If set, colors selected cells to the color(s)
in \code{cols.highlight} and other cells black (white if dark.theme = TRUE);
will also resize to the size(s) passed to \code{sizes.highlight}}
\item{cols.highlight}{A vector of colors to highlight the cells as; will
repeat to the length groups in cells.highlight}
\item{sizes.highlight}{Size of highlighted cells; will repeat to the length
groups in cells.highlight}
\item{na.value}{Color value for NA points when using custom scale}
\item{combine}{Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features}
\item{ncol}{Number of columns for display when combining plots}
\item{...}{Extra parameters passed on to \code{\link{CombinePlots}}}
}
\value{
A ggplot object
}
\description{
Graphs the output of a dimensional reduction technique on a 2D scatter plot where each point is a
cell and it's positioned based on the cell embeddings determined by the reduction technique. By
default, cells are colored by their identity class (can be changed with the group.by parameter).
}
\note{
For the old \code{do.hover} and \code{do.identify} functionality, please see
\code{HoverLocator} and \code{CellSelector}, respectively.
}
\examples{
DimPlot(object = pbmc_small)
DimPlot(object = pbmc_small, split.by = 'ident')
}
\seealso{
\code{\link{FeaturePlot}} \code{\link{HoverLocator}}
\code{\link{CellSelector}} \code{link{FetchData}}
}
Seurat/man/JackStrawPlot.Rd 0000644 0001762 0000144 00000002500 13617632030 015305 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{JackStrawPlot}
\alias{JackStrawPlot}
\title{JackStraw Plot}
\usage{
JackStrawPlot(object, dims = 1:5, reduction = "pca", xmax = 0.1, ymax = 0.3)
}
\arguments{
\item{object}{Seurat object}
\item{dims}{Dims to plot}
\item{reduction}{reduction to pull jackstraw info from}
\item{xmax}{X-axis maximum on each QQ plot.}
\item{ymax}{Y-axis maximum on each QQ plot.}
}
\value{
A ggplot object
}
\description{
Plots the results of the JackStraw analysis for PCA significance. For each
PC, plots a QQ-plot comparing the distribution of p-values for all genes
across each PC, compared with a uniform distribution. Also determines a
p-value for the overall significance of each PC (see Details).
}
\details{
Significant PCs should show a p-value distribution (black curve) that is
strongly skewed to the left compared to the null distribution (dashed line)
The p-value for each PC is based on a proportion test comparing the number
of genes with a p-value below a particular threshold (score.thresh), compared with the
proportion of genes expected under a uniform distribution of p-values.
}
\examples{
JackStrawPlot(object = pbmc_small)
}
\seealso{
\code{\link{ScoreJackStraw}}
}
\author{
Omri Wurtzel
}
Seurat/man/VlnPlot.Rd 0000644 0001762 0000144 00000004233 13617632030 014160 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{VlnPlot}
\alias{VlnPlot}
\title{Single cell violin plot}
\usage{
VlnPlot(
object,
features,
cols = NULL,
pt.size = 1,
idents = NULL,
sort = FALSE,
assay = NULL,
group.by = NULL,
split.by = NULL,
adjust = 1,
y.max = NULL,
same.y.lims = FALSE,
log = FALSE,
ncol = NULL,
combine = TRUE,
slot = "data",
multi.group = FALSE,
...
)
}
\arguments{
\item{object}{Seurat object}
\item{features}{Features to plot (gene expression, metrics, PC scores,
anything that can be retreived by FetchData)}
\item{cols}{Colors to use for plotting}
\item{pt.size}{Point size for geom_violin}
\item{idents}{Which classes to include in the plot (default is all)}
\item{sort}{Sort identity classes (on the x-axis) by the average
expression of the attribute being potted, can also pass 'increasing' or 'decreasing' to change sort direction}
\item{assay}{Name of assay to use, defaults to the active assay}
\item{group.by}{Group (color) cells in different ways (for example, orig.ident)}
\item{split.by}{A variable to split the violin plots by,}
\item{adjust}{Adjust parameter for geom_violin}
\item{y.max}{Maximum y axis value}
\item{same.y.lims}{Set all the y-axis limits to the same values}
\item{log}{plot the feature axis on log scale}
\item{ncol}{Number of columns if multiple plots are displayed}
\item{combine}{Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features}
\item{slot}{Use non-normalized counts data for plotting}
\item{multi.group}{plot each group of the split violin plots by multiple or single violin shapes
see \code{\link{FetchData}} for more details}
\item{...}{Extra parameters passed on to \code{\link{CombinePlots}}}
}
\value{
A ggplot object
}
\description{
Draws a violin plot of single cell data (gene expression, metrics, PC
scores, etc.)
}
\examples{
VlnPlot(object = pbmc_small, features = 'PC_1')
VlnPlot(object = pbmc_small, features = 'LYZ', split.by = 'groups')
}
\seealso{
\code{\link{FetchData}}
}
Seurat/man/Assays.Rd 0000644 0001762 0000144 00000001165 13617632030 014026 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\name{Assays}
\alias{Assays}
\title{Pull Assays or assay names}
\usage{
Assays(object, slot = NULL)
}
\arguments{
\item{object}{A Seurat object}
\item{slot}{Name of Assay to return}
}
\value{
If \code{slot} is \code{NULL}, the names of all \code{Assay} objects
in this Seurat object. Otherwise, the \code{Assay} object specified
}
\description{
Lists the names of \code{\link{Assay}} objects present in
a Seurat object. If slot is provided, pulls specified Assay object.
}
\examples{
Assays(object = pbmc_small)
}
Seurat/man/FeaturePlot.Rd 0000644 0001762 0000144 00000010770 13617632030 015017 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{FeaturePlot}
\alias{FeaturePlot}
\alias{FeatureHeatmap}
\title{Visualize 'features' on a dimensional reduction plot}
\usage{
FeaturePlot(
object,
features,
dims = c(1, 2),
cells = NULL,
cols = if (blend) { c("lightgrey", "#ff0000", "#00ff00") } else {
c("lightgrey", "blue") },
pt.size = NULL,
order = FALSE,
min.cutoff = NA,
max.cutoff = NA,
reduction = NULL,
split.by = NULL,
shape.by = NULL,
slot = "data",
blend = FALSE,
blend.threshold = 0.5,
label = FALSE,
label.size = 4,
repel = FALSE,
ncol = NULL,
combine = TRUE,
coord.fixed = FALSE,
by.col = TRUE,
sort.cell = FALSE
)
}
\arguments{
\item{object}{Seurat object}
\item{features}{Vector of features to plot. Features can come from:
\itemize{
\item An \code{Assay} feature (e.g. a gene name - "MS4A1")
\item A column name from meta.data (e.g. mitochondrial percentage - "percent.mito")
\item A column name from a \code{DimReduc} object corresponding to the cell embedding values
(e.g. the PC 1 scores - "PC_1")
}}
\item{dims}{Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions}
\item{cells}{Vector of cells to plot (default is all cells)}
\item{cols}{The two colors to form the gradient over. Provide as string vector with
the first color corresponding to low values, the second to high. Also accepts a Brewer
color scale or vector of colors. Note: this will bin the data into number of colors provided.
When blend is \code{TRUE}, takes anywhere from 1-3 colors:
\describe{
\item{1 color:}{Treated as color for double-negatives, will use default colors 2 and 3 for per-feature expression}
\item{2 colors:}{Treated as colors for per-feature expression, will use default color 1 for double-negatives}
\item{3+ colors:}{First color used for double-negatives, colors 2 and 3 used for per-feature expression, all others ignored}
}}
\item{pt.size}{Adjust point size for plotting}
\item{order}{Boolean determining whether to plot cells in order of expression. Can be useful if
cells expressing given feature are getting buried.}
\item{min.cutoff, max.cutoff}{Vector of minimum and maximum cutoff values for each feature,
may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')}
\item{reduction}{Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca}
\item{split.by}{A factor in object metadata to split the feature plot by, pass 'ident'
to split by cell identity'; similar to the old \code{FeatureHeatmap}}
\item{shape.by}{If NULL, all points are circles (default). You can specify any
cell attribute (that can be pulled with FetchData) allowing for both
different colors and different shapes on cells}
\item{slot}{Which slot to pull expression data from?}
\item{blend}{Scale and blend expression values to visualize coexpression of two features}
\item{blend.threshold}{The color cutoff from weak signal to strong signal; ranges from 0 to 1.}
\item{label}{Whether to label the clusters}
\item{label.size}{Sets size of labels}
\item{repel}{Repel labels}
\item{ncol}{Number of columns to combine multiple feature plots to, ignored if \code{split.by} is not \code{NULL}}
\item{combine}{Combine plots into a single gg object; note that if \code{TRUE}; themeing will not work when plotting multiple features}
\item{coord.fixed}{Plot cartesian coordinates with fixed aspect ratio}
\item{by.col}{If splitting by a factor, plot the splits per column with the features as rows; ignored if \code{blend = TRUE}}
\item{sort.cell}{If \code{TRUE}, the positive cells will overlap the negative cells}
}
\value{
Returns a ggplot object if only 1 feature is plotted.
If >1 features are plotted and \code{combine=TRUE}, returns a combined ggplot object using \code{cowplot::plot_grid}.
If >1 features are plotted and \code{combine=FALSE}, returns a list of ggplot objects.
}
\description{
Colors single cells on a dimensional reduction plot according to a 'feature'
(i.e. gene expression, PC scores, number of genes detected, etc.)
}
\note{
For the old \code{do.hover} and \code{do.identify} functionality, please see
\code{HoverLocator} and \code{CellSelector}, respectively.
}
\examples{
FeaturePlot(object = pbmc_small, features = 'PC_1')
}
\seealso{
\code{\link{DimPlot}} \code{\link{HoverLocator}}
\code{\link{CellSelector}}
}
Seurat/man/RunALRA.Rd 0000644 0001762 0000144 00000007205 13617632030 013770 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/preprocessing.R
\name{RunALRA}
\alias{RunALRA}
\alias{RunALRA.default}
\alias{RunALRA.Seurat}
\title{Run Adaptively-thresholded Low Rank Approximation (ALRA)}
\usage{
RunALRA(object, ...)
\method{RunALRA}{default}(
object,
k = NULL,
q = 10,
quantile.prob = 0.001,
use.mkl = FALSE,
mkl.seed = -1,
...
)
\method{RunALRA}{Seurat}(
object,
k = NULL,
q = 10,
quantile.prob = 0.001,
use.mkl = FALSE,
mkl.seed = -1,
assay = NULL,
slot = "data",
setDefaultAssay = TRUE,
genes.use = NULL,
K = NULL,
thresh = 6,
noise.start = NULL,
q.k = 2,
k.only = FALSE,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods}
\item{k}{The rank of the rank-k approximation. Set to NULL for automated choice of k.}
\item{q}{The number of additional power iterations in randomized SVD when
computing rank k approximation. By default, q=10.}
\item{quantile.prob}{The quantile probability to use when calculating threshold.
By default, quantile.prob = 0.001.}
\item{use.mkl}{Use the Intel MKL based implementation of SVD. Needs to be
installed from https://github.com/KlugerLab/rpca-mkl. \strong{Note}: this requires
the \href{https://github.com/satijalab/seurat-wrappers}{SeuratWrappers} implementation
of \code{RunALRA}}
\item{mkl.seed}{Only relevant if \code{use.mkl = TRUE}. Set the seed for the random
generator for the Intel MKL implementation of SVD. Any number <0 will
use the current timestamp. If \code{use.mkl = FALSE}, set the seed using
\code{\link{set.seed}()} function as usual.}
\item{assay}{Assay to use}
\item{slot}{slot to use}
\item{setDefaultAssay}{If TRUE, will set imputed results as default Assay}
\item{genes.use}{genes to impute}
\item{K}{Number of singular values to compute when choosing k. Must be less
than the smallest dimension of the matrix. Default 100 or smallest dimension.}
\item{thresh}{The threshold for ''significance'' when choosing k. Default 1e-10.}
\item{noise.start}{Index for which all smaller singular values are considered noise.
Default K - 20.}
\item{q.k}{Number of additional power iterations when choosing k. Default 2.}
\item{k.only}{If TRUE, only computes optimal k WITHOUT performing ALRA}
}
\description{
Runs ALRA, a method for imputation of dropped out values in scRNA-seq data.
Computes the k-rank approximation to A_norm and adjusts it according to the
error distribution learned from the negative values. Described in
Linderman, G. C., Zhao, J., Kluger, Y. (2018). "Zero-preserving imputation
of scRNA-seq data using low rank approximation." (bioRxiv:138677)
}
\note{
RunALRA and associated functions are being moved to SeuratWrappers;
for more information on SeuratWrappers, please see \url{https://github.com/satijalab/seurat-wrappers}
}
\examples{
pbmc_small
# Example 1: Simple usage, with automatic choice of k.
pbmc_small_alra <- RunALRA(object = pbmc_small)
\dontrun{
# Example 2: Visualize choice of k, then run ALRA
# First, choose K
pbmc_small_alra <- RunALRA(pbmc_small, k.only=TRUE)
# Plot the spectrum, spacings, and p-values which are used to choose k
ggouts <- ALRAChooseKPlot(pbmc_small_alra)
do.call(gridExtra::grid.arrange, c(ggouts, nrow=1))
# Run ALRA with the chosen k
pbmc_small_alra <- RunALRA(pbmc_small_alra)
}
}
\references{
Linderman, G. C., Zhao, J., Kluger, Y. (2018). "Zero-preserving imputation
of scRNA-seq data using low rank approximation." (bioRxiv:138677)
}
\seealso{
\code{\link{ALRAChooseKPlot}}
}
\author{
Jun Zhao, George Linderman
}
Seurat/man/RunPCA.Rd 0000644 0001762 0000144 00000005207 13617632030 013654 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{RunPCA}
\alias{RunPCA}
\alias{RunPCA.default}
\alias{RunPCA.Assay}
\alias{RunPCA.Seurat}
\title{Run Principal Component Analysis}
\usage{
RunPCA(object, ...)
\method{RunPCA}{default}(
object,
assay = NULL,
npcs = 50,
rev.pca = FALSE,
weight.by.var = TRUE,
verbose = TRUE,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.key = "PC_",
seed.use = 42,
approx = TRUE,
...
)
\method{RunPCA}{Assay}(
object,
assay = NULL,
features = NULL,
npcs = 50,
rev.pca = FALSE,
weight.by.var = TRUE,
verbose = TRUE,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.key = "PC_",
seed.use = 42,
...
)
\method{RunPCA}{Seurat}(
object,
assay = NULL,
features = NULL,
npcs = 50,
rev.pca = FALSE,
weight.by.var = TRUE,
verbose = TRUE,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.name = "pca",
reduction.key = "PC_",
seed.use = 42,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods and IRLBA}
\item{assay}{Name of Assay PCA is being run on}
\item{npcs}{Total Number of PCs to compute and store (50 by default)}
\item{rev.pca}{By default computes the PCA on the cell x gene matrix. Setting
to true will compute it on gene x cell matrix.}
\item{weight.by.var}{Weight the cell embeddings by the variance of each PC
(weights the gene loadings if rev.pca is TRUE)}
\item{verbose}{Print the top genes associated with high/low loadings for
the PCs}
\item{ndims.print}{PCs to print genes for}
\item{nfeatures.print}{Number of genes to print for each PC}
\item{reduction.key}{dimensional reduction key, specifies the string before
the number for the dimension names. PC by default}
\item{seed.use}{Set a random seed. By default, sets the seed to 42. Setting
NULL will not set a seed.}
\item{approx}{Use truncated singular value decomposition to approximate PCA}
\item{features}{Features to compute PCA on. If features=NULL, PCA will be run
using the variable features for the Assay. Note that the features must be present
in the scaled data. Any requested features that are not scaled or have 0 variance
will be dropped, and the PCA will be run using the remaining features.}
\item{reduction.name}{dimensional reduction name, pca by default}
}
\value{
Returns Seurat object with the PCA calculation stored in the reductions slot
}
\description{
Run a PCA dimensionality reduction. For details about stored PCA calculation
parameters, see \code{PrintPCAParams}.
}
Seurat/man/ColorDimSplit.Rd 0000644 0001762 0000144 00000007005 13617632030 015306 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{ColorDimSplit}
\alias{ColorDimSplit}
\title{Color dimensional reduction plot by tree split}
\usage{
ColorDimSplit(
object,
node,
left.color = "red",
right.color = "blue",
other.color = "grey50",
...
)
}
\arguments{
\item{object}{Seurat object}
\item{node}{Node in cluster tree on which to base the split}
\item{left.color}{Color for the left side of the split}
\item{right.color}{Color for the right side of the split}
\item{other.color}{Color for all other cells}
\item{...}{
Arguments passed on to \code{\link[=DimPlot]{DimPlot}}
\describe{
\item{\code{dims}}{Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions}
\item{\code{cells}}{Vector of cells to plot (default is all cells)}
\item{\code{cols}}{Vector of colors, each color corresponds to an identity class. This may also be a single character
or numeric value corresponding to a palette as specified by \code{\link[RColorBrewer]{brewer.pal.info}}.
By default, ggplot2 assigns colors. We also include a number of palettes from the pals package.
See \code{\link{DiscretePalette}} for details.}
\item{\code{pt.size}}{Adjust point size for plotting}
\item{\code{reduction}}{Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca}
\item{\code{group.by}}{Name of one or more metadata columns to group (color) cells by
(for example, orig.ident); pass 'ident' to group by identity class}
\item{\code{split.by}}{Name of a metadata column to split plot by;
see \code{\link{FetchData}} for more details}
\item{\code{shape.by}}{If NULL, all points are circles (default). You can specify any
cell attribute (that can be pulled with FetchData) allowing for both
different colors and different shapes on cells}
\item{\code{order}}{Specify the order of plotting for the idents. This can be
useful for crowded plots if points of interest are being buried. Provide
either a full list of valid idents or a subset to be plotted last (on top)}
\item{\code{label}}{Whether to label the clusters}
\item{\code{label.size}}{Sets size of labels}
\item{\code{repel}}{Repel labels}
\item{\code{cells.highlight}}{A list of character or numeric vectors of cells to
highlight. If only one group of cells desired, can simply
pass a vector instead of a list. If set, colors selected cells to the color(s)
in \code{cols.highlight} and other cells black (white if dark.theme = TRUE);
will also resize to the size(s) passed to \code{sizes.highlight}}
\item{\code{cols.highlight}}{A vector of colors to highlight the cells as; will
repeat to the length groups in cells.highlight}
\item{\code{sizes.highlight}}{Size of highlighted cells; will repeat to the length
groups in cells.highlight}
\item{\code{na.value}}{Color value for NA points when using custom scale}
\item{\code{combine}}{Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features}
\item{\code{ncol}}{Number of columns for display when combining plots}
}}
}
\value{
Returns a DimPlot
}
\description{
Returns a DimPlot colored based on whether the cells fall in clusters
to the left or to the right of a node split in the cluster tree.
}
\examples{
pbmc_small
pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE)
PlotClusterTree(pbmc_small)
ColorDimSplit(pbmc_small, node = 5)
}
Seurat/man/HTODemux.Rd 0000644 0001762 0000144 00000004120 13617632030 014212 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{HTODemux}
\alias{HTODemux}
\title{Demultiplex samples based on data from cell 'hashing'}
\usage{
HTODemux(
object,
assay = "HTO",
positive.quantile = 0.99,
init = NULL,
nstarts = 100,
kfunc = "clara",
nsamples = 100,
seed = 42,
verbose = TRUE
)
}
\arguments{
\item{object}{Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized.}
\item{assay}{Name of the Hashtag assay (HTO by default)}
\item{positive.quantile}{The quantile of inferred 'negative' distribution for each hashtag - over which the cell is considered 'positive'. Default is 0.99}
\item{init}{Initial number of clusters for hashtags. Default is the # of hashtag oligo names + 1 (to account for negatives)}
\item{nstarts}{nstarts value for k-means clustering (for kfunc = "kmeans"). 100 by default}
\item{kfunc}{Clustering function for initial hashtag grouping. Default is "clara" for fast k-medoids clustering on large applications, also support "kmeans" for kmeans clustering}
\item{nsamples}{Number of samples to be drawn from the dataset used for clustering, for kfunc = "clara"}
\item{seed}{Sets the random seed. If NULL, seed is not set}
\item{verbose}{Prints the output}
}
\value{
The Seurat object with the following demultiplexed information stored in the meta data:
\describe{
\item{hash.maxID}{Name of hashtag with the highest signal}
\item{hash.secondID}{Name of hashtag with the second highest signal}
\item{hash.margin}{The difference between signals for hash.maxID and hash.secondID}
\item{classification}{Classification result, with doublets/multiplets named by the top two highest hashtags}
\item{classification.global}{Global classification result (singlet, doublet or negative)}
\item{hash.ID}{Classification result where doublet IDs are collapsed}
}
}
\description{
Assign sample-of-origin for each cell, annotate doublets.
}
\examples{
\dontrun{
object <- HTODemux(object)
}
}
\seealso{
\code{\link{HTOHeatmap}}
}
Seurat/man/as.Seurat.Rd 0000644 0001762 0000144 00000010703 13617632030 014426 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/objects.R
\name{as.Seurat}
\alias{as.Seurat}
\alias{as.Seurat.CellDataSet}
\alias{as.Seurat.loom}
\alias{as.Seurat.SingleCellExperiment}
\title{Convert objects to Seurat objects}
\usage{
as.Seurat(x, ...)
\method{as.Seurat}{CellDataSet}(x, slot = "counts", assay = "RNA", verbose = TRUE, ...)
\method{as.Seurat}{loom}(
x,
cells = "CellID",
features = "Gene",
normalized = NULL,
scaled = NULL,
assay = NULL,
verbose = TRUE,
...
)
\method{as.Seurat}{SingleCellExperiment}(
x,
counts = "counts",
data = "logcounts",
assay = "RNA",
project = "SingleCellExperiment",
...
)
}
\arguments{
\item{x}{An object to convert to class \code{Seurat}}
\item{...}{Arguments passed to other methods}
\item{slot}{Slot to store expression data as}
\item{assay}{Name to store expression matrices as}
\item{verbose}{Display progress updates}
\item{cells}{The name of the dataset within \code{col_attrs} containing cell names}
\item{features}{The name of the dataset within \code{row_attrs} containing feature names}
\item{normalized}{The name of the dataset within \code{layers} containing the
normalized expression matrix; pass \code{/matrix} (with preceeding forward slash) to store
\code{/matrix} as normalized data}
\item{scaled}{The name of the dataset within \code{layers} containing the scaled expression matrix}
\item{counts}{name of the SingleCellExperiment assay to store as \code{counts};
set to \code{NULL} if only normalized data are present}
\item{data}{name of the SingleCellExperiment assay to slot as \code{data}.
Set to NULL if only counts are present}
\item{project}{Project name for new Seurat object}
}
\description{
Convert objects to Seurat objects
}
\details{
The \code{loom} method for \code{as.Seurat} will try to automatically fill in a Seurat object based on data presence.
For example, if no normalized data is present, then scaled data, dimensional reduction informan, and neighbor graphs
will not be pulled as these depend on normalized data. The following is a list of how the Seurat object will be constructed
\itemize{
\item If no assay information is provided, will default to an assay name in a root-level HDF5 attribute called \code{assay};
if no attribute is present, will default to "RNA"
\item Cell-level metadata will consist of all one-dimensional datasets in \code{col_attrs} \strong{except} datasets named "ClusterID", "ClusterName",
and whatever is passed to \code{cells}
\item Identity classes will be set if either \code{col_attrs/ClusterID} or \code{col_attrs/ClusterName} are present; if both are present, then
the values in \code{col_attrs/ClusterID} will set the order (numeric value of a factor) for values in \code{col_attrs/ClusterName}
(charater value of a factor)
\item Feature-level metadata will consist of all one-dimensional datasets in \code{row_attrs} \strong{except} datasets named "Selected" and whatever
is passed to \code{features}; any feature-level metadata named "variance_standardized", "variance_expected", or "dispersion_scaled" will have
underscores "_" replaced with a period "."
\item Variable features will be set if \code{row_attrs/Selected} exists and it is a numeric type
\item If a dataset is passed to \code{normalized}, stored as a sparse matrix in \code{data};
if no dataset provided, \code{scaled} will be set to \code{NULL}
\item If a dataset is passed to \code{scaled}, stored as a dense matrix in \code{scale.data}; all rows entirely consisting of \code{NA}s
will be removed
\item If a dataset is passed to \code{scaled}, dimensional reduction information will assembled from cell embedding information
stored in \code{col_attrs}; cell embeddings will be pulled from two-dimensional datasets ending with "_cell_embeddings"; priority will
be given to cell embeddings that have the name of \code{assay} in their name; feature loadings will be added from two-dimensional
datasets in \code{row_attrs} that start with the name of the dimensional reduction and end with either "feature_loadings" or
"feature_loadings_projected" (priority given to the latter)
\item If a dataset is passed to \code{scaled}, neighbor graphs will be pulled from \code{col_graphs}, provided the name starts
with the value of \code{assay}
}
}
\examples{
\dontrun{
lfile <- as.loom(x = pbmc_small)
pbmc <- as.Seurat(x = lfile)
}
}
Seurat/man/CreateGeneActivityMatrix.Rd 0000644 0001762 0000144 00000002115 13617632030 017463 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocessing.R
\name{CreateGeneActivityMatrix}
\alias{CreateGeneActivityMatrix}
\title{Convert a peak matrix to a gene activity matrix}
\usage{
CreateGeneActivityMatrix(
peak.matrix,
annotation.file,
seq.levels = c(1:22, "X", "Y"),
include.body = TRUE,
upstream = 2000,
downstream = 0,
verbose = TRUE
)
}
\arguments{
\item{peak.matrix}{Matrix of peak counts}
\item{annotation.file}{Path to GTF annotation file}
\item{seq.levels}{Which seqlevels to keep (corresponds to chromosomes usually)}
\item{include.body}{Include the gene body?}
\item{upstream}{Number of bases upstream to consider}
\item{downstream}{Number of bases downstream to consider}
\item{verbose}{Print progress/messages}
}
\description{
This function will take in a peak matrix and an annotation file (gtf) and collapse the peak
matrix to a gene activity matrix. It makes the simplifying assumption that all counts in the gene
body plus X kb up and or downstream should be attributed to that gene.
}
Seurat/DESCRIPTION 0000644 0001762 0000144 00000007256 13620617476 013251 0 ustar ligges users Package: Seurat
Version: 3.1.3
Date: 2020-02-07
Title: Tools for Single Cell Genomics
Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , and Butler A and Satija R (2017) for more details.
Authors@R: c(
person(given = 'Rahul', family = 'Satija', email = 'rsatija@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0001-9448-8833')),
person(given = 'Andrew', family = 'Butler', email = 'abutler@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0003-3608-0463')),
person(given = 'Paul', family = 'Hoffman', email = 'nygcSatijalab@nygenome.org', role = c('aut', 'cre'), comment = c(ORCID = '0000-0002-7693-8957')),
person(given = 'Tim', family = 'Stuart', email = 'tstuart@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0002-3044-0897')),
person(given = 'Jeff', family = 'Farrell', email = 'jfarrell@g.harvard.edu', role = 'ctb'),
person(given = 'Shiwei', family = 'Zheng', email = 'szheng@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6682-6743')),
person(given = 'Christoph', family = 'Hafemeister', email = 'chafemeister@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6365-8254')),
person(given = 'Patrick', family = 'Roelli', email = 'proelli@nygenome.org', role = 'ctb'),
person(given = "Yuhan", family = "Hao", email = 'yhao@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0002-1810-0822'))
)
URL: http://www.satijalab.org/seurat,
https://github.com/satijalab/seurat
BugReports: https://github.com/satijalab/seurat/issues
Additional_repositories: https://mojaveazure.github.io/loomR
Depends: R (>= 3.4.0), methods,
Imports: ape, cluster, cowplot, fitdistrplus, future, future.apply,
ggplot2 (>= 3.0.0), ggrepel, ggridges, graphics, grDevices,
grid, httr, ica, igraph, irlba, KernSmooth, leiden (>= 0.3.1),
lmtest, MASS, Matrix (>= 1.2-14), metap, pbapply, plotly, png,
RANN, RColorBrewer, Rcpp, RcppAnnoy, reticulate, rlang, ROCR,
rsvd, Rtsne, scales, sctransform (>= 0.2.0), stats, tools,
tsne, utils, uwot (>= 0.1.5)
LinkingTo: Rcpp (>= 0.11.0), RcppEigen, RcppProgress
License: GPL-3 | file LICENSE
LazyData: true
Collate: 'RcppExports.R' 'generics.R' 'clustering.R' 'visualization.R'
'convenience.R' 'data.R' 'differential_expression.R'
'dimensional_reduction.R' 'integration.R' 'objects.R'
'preprocessing.R' 'tree.R' 'utilities.R' 'zzz.R'
RoxygenNote: 7.0.2
Encoding: UTF-8
biocViews:
Suggests: loomR, SDMTools, testthat, hdf5r, S4Vectors,
SummarizedExperiment, SingleCellExperiment, MAST, DESeq2,
BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges,
rtracklayer, monocle, Biobase, VGAM
NeedsCompilation: yes
Packaged: 2020-02-10 02:12:30 UTC; mojav
Author: Rahul Satija [aut] (),
Andrew Butler [aut] (),
Paul Hoffman [aut, cre] (),
Tim Stuart [aut] (),
Jeff Farrell [ctb],
Shiwei Zheng [ctb] (),
Christoph Hafemeister [ctb] (),
Patrick Roelli [ctb],
Yuhan Hao [ctb] ()
Maintainer: Paul Hoffman
Repository: CRAN
Date/Publication: 2020-02-11 21:40:14 UTC
Seurat/tests/ 0000755 0001762 0000144 00000000000 13527073365 012672 5 ustar ligges users Seurat/tests/testthat/ 0000755 0001762 0000144 00000000000 13620617476 014533 5 ustar ligges users Seurat/tests/testthat/test_differential_expression.R 0000644 0001762 0000144 00000032610 13620135602 022614 0 ustar ligges users # Tests for functions in differential_expression.R
suppressWarnings(RNGversion(vstr = "3.5.3"))
set.seed(seed = 42)
# Tests for FindMarkers default parameters
# --------------------------------------------------------------------------------
context("FindMarkers")
markers.0 <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, verbose = FALSE))
markers.01 <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, verbose = FALSE))
test_that("Default settings work as expected", {
expect_error(FindMarkers(object = pbmc_small))
expect_error(FindMarkers(object = pbmc_small, ident.1 = "test"))
expect_error(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = "test"))
expect_equal(colnames(x = markers.0), c("p_val", "avg_logFC", "pct.1", "pct.2", "p_val_adj"))
expect_equal(markers.0[1, "p_val"], 9.572778e-13)
expect_equal(markers.0[1, "avg_logFC"], -4.034691, tolerance = 1e-6)
expect_equal(markers.0[1, "pct.1"], 0.083)
expect_equal(markers.0[1, "pct.2"], 0.909)
expect_equal(markers.0[1, "p_val_adj"], 2.201739e-10)
expect_equal(nrow(x = markers.0), 204)
expect_equal(rownames(markers.0)[1], "HLA-DPB1")
expect_equal(markers.01[1, "p_val"], 1.702818e-11)
expect_equal(markers.01[1, "avg_logFC"], -2.539289, tolerance = 1e-6)
expect_equal(markers.01[1, "pct.1"], 0.111)
expect_equal(markers.01[1, "pct.2"], 1.00)
expect_equal(markers.01[1, "p_val_adj"], 3.916481e-09)
expect_equal(nrow(x = markers.01), 201)
expect_equal(rownames(x = markers.01)[1], "TYMP")
})
tymp.results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, features = "TYMP", verbose = FALSE))
vargenes.results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, features = VariableFeatures(object = pbmc_small), verbose = FALSE))
test_that("features parameter behaves correctly ", {
expect_equal(nrow(x = tymp.results), 1)
expect_equal(tymp.results[1, "p_val"], 3.227445e-07)
expect_equal(tymp.results[1, "avg_logFC"], -2.093928, tolerance = 1e-6)
expect_equal(tymp.results[1, "pct.1"], 0.111)
expect_equal(tymp.results[1, "pct.2"], 0.682)
expect_equal(tymp.results[1, "p_val_adj"], 7.423123e-05)
expect_equal(rownames(x = tymp.results)[1], "TYMP")
expect_equal(nrow(x = vargenes.results), 19)
expect_equal(vargenes.results[19, "p_val"], 4.225151e-01, tolerance = 1e-6)
expect_equal(vargenes.results[19, "avg_logFC"], 1.5976958, tolerance = 1e-6)
expect_equal(vargenes.results[19, "pct.1"], 0.139)
expect_equal(vargenes.results[19, "pct.2"], 0.091)
expect_equal(vargenes.results[19, "p_val_adj"], 1.000000e+00)
expect_equal(rownames(x = vargenes.results)[19], "PARVB")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = Cells(x = pbmc_small)[1:40], ident.2 = Cells(x = pbmc_small)[41:80], verbose = FALSE))
test_that("passing cell names works", {
expect_equal(nrow(x = results), 176)
expect_equal(results[1, "p_val"], 0.0001690882)
expect_equal(results[1, "avg_logFC"], -1.790824, tolerance = 1e-6)
expect_equal(results[1, "pct.1"], 0.075)
expect_equal(results[1, "pct.2"], 0.450)
expect_equal(results[1, "p_val_adj"], 0.03889028)
expect_equal(rownames(x = results)[1], "IFI30")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, logfc.threshold = 2, verbose = FALSE))
test_that("logfc.threshold works", {
expect_equal(nrow(x = results), 112)
expect_gte(min(abs(x = results$avg_logFC)), 2)
expect_error(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, logfc.threshold = 100, verbose = FALSE))
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, min.pct = 0.5, verbose = FALSE))
test_that("min.pct works", {
expect_equal(nrow(x = results), 63)
expect_gte(min(apply(X = results, MARGIN = 1, FUN = function(x) max(x[3], x[4]))), 0.5)
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, min.diff.pct = 0.5, verbose = FALSE))
test_that("min.diff.pct works", {
expect_equal(nrow(x = results), 43)
expect_gte(min(apply(X = results, MARGIN = 1, FUN = function(x) abs(x[4] - x[3]))), 0.5)
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, only.pos = TRUE, verbose = FALSE))
test_that("only.pos works", {
expect_equal(nrow(x = results), 116)
expect_true(all(results$avg_logFC > 0))
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, max.cells.per.ident = 20, verbose = FALSE))
test_that("max.cells.per.ident works", {
expect_equal(nrow(x = results), 201)
expect_equal(results[1, "p_val"], 3.428568e-08)
expect_equal(results[1, "avg_logFC"], -2.539289, tolerance = 1e-6)
expect_equal(results[1, "pct.1"], 0.111)
expect_equal(results[1, "pct.2"], 1)
expect_equal(results[1, "p_val_adj"], 7.885706e-06)
expect_equal(rownames(x = results)[1], "TYMP")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, latent.vars= "groups", verbose = FALSE, test.use = 'LR'))
test_that("latent.vars works", {
expect_error(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, latent.vars= "fake", verbose = FALSE))
expect_warning(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, latent.vars= "groups", verbose = FALSE))
expect_equal(nrow(x = results), 201)
expect_equal(results[1, "p_val"], 2.130202e-16)
expect_equal(results[1, "avg_logFC"], -3.082150, tolerance = 1e-6)
expect_equal(results[1, "pct.1"], 0.417)
expect_equal(results[1, "pct.2"], 1)
expect_equal(results[1, "p_val_adj"], 4.899466e-14)
expect_equal(rownames(x = results)[1], "LYZ")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = "g1", ident.2 = "g2", group.by= "groups", verbose = FALSE))
t2 <- pbmc_small
Idents(object = t2) <- "groups"
results2 <- suppressWarnings(FindMarkers(object = t2, ident.1 = "g1", ident.2 = "g2", verbose = FALSE))
test_that("group.by works", {
expect_equal(nrow(x = results), 136)
expect_equal(results, results2)
expect_equal(results[1, "p_val"], 0.02870319)
expect_equal(results[1, "avg_logFC"], 0.8226720, tolerance = 1e-6)
expect_equal(results[1, "pct.1"], 0.455)
expect_equal(results[1, "pct.2"], 0.194)
expect_equal(results[1, "p_val_adj"], 1)
expect_equal(rownames(x = results)[1], "NOSIP")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = "g1", ident.2 = "g2", group.by= "groups", subset.ident = 0, verbose = FALSE))
t2 <- subset(x = pbmc_small, idents = 0)
Idents(object = t2) <- "groups"
results2 <- suppressWarnings(FindMarkers(object = t2, ident.1 = "g1", ident.2 = "g2", verbose = FALSE))
test_that("subset.ident works", {
expect_equal(nrow(x = results), 114)
expect_equal(results, results2)
expect_equal(results[1, "p_val"], 0.01293720)
expect_equal(results[1, "avg_logFC"], 1.799280, tolerance = 1e-6)
expect_equal(results[1, "pct.1"], 0.50)
expect_equal(results[1, "pct.2"], 0.125)
expect_equal(results[1, "p_val_adj"], 1)
expect_equal(rownames(x = results)[1], "TSPO")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, reduction = "pca", verbose = FALSE))
test_that("reduction works", {
expect_equal(results[1, "p_val"], 1.664954e-10)
expect_equal(results[1, "avg_diff"], -2.810453669, tolerance = 1e-6)
expect_equal(results[1, "p_val_adj"], 3.163412e-09)
expect_equal(rownames(x = results)[1], "PC_2")
})
results <- FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, test.use = "bimod", verbose = FALSE)
test_that("bimod test works", {
expect_equal(nrow(x = results), 201)
expect_equal(results[1, "p_val"], 4.751376e-17)
expect_equal(results[1, "avg_logFC"], -2.552769, tolerance = 1e-6)
expect_equal(results[1, "pct.1"], 0.306)
expect_equal(results[1, "pct.2"], 1.00)
expect_equal(results[1, "p_val_adj"], 1.092816e-14)
expect_equal(rownames(x = results)[1], "CST3")
})
results <- FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, test.use = "roc", verbose = FALSE)
test_that("roc test works", {
expect_equal(nrow(x = results), 201)
expect_equal(colnames(x = results), c("myAUC", "avg_diff", "power", "pct.1", "pct.2"))
expect_equal(results["CST3", "myAUC"], 0.018)
expect_equal(results["CST3", "avg_diff"], -2.552769, tolerance = 1e-6)
expect_equal(results["CST3", "power"], 0.964)
expect_equal(results["CST3", "pct.1"], 0.306)
expect_equal(results["CST3", "pct.2"], 1.00)
expect_equal(rownames(x = results)[1], "LYZ")
})
results <- FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, test.use = "t", verbose = FALSE)
test_that("bimod test works", {
expect_equal(nrow(x = results), 201)
expect_equal(results["CST3", "p_val"], 1.170112e-15)
expect_equal(results["CST3", "avg_logFC"], -2.552769 , tolerance = 1e-6)
expect_equal(results["CST3", "pct.1"], 0.306)
expect_equal(results["CST3", "pct.2"], 1.00)
expect_equal(results["CST3", "p_val_adj"], 2.691258e-13)
expect_equal(rownames(x = results)[1], "TYMP")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, test.use = "negbinom", verbose = FALSE))
test_that("negbinom test works", {
expect_equal(nrow(x = results), 149)
expect_equal(results["CST3", "p_val"], 1.354443e-17)
expect_equal(results["CST3", "avg_logFC"], -2.353701, tolerance = 1e-6)
expect_equal(results["CST3", "pct.1"], 0.306)
expect_equal(results["CST3", "pct.2"], 1.00)
expect_equal(results["CST3", "p_val_adj"], 3.115218e-15)
expect_equal(rownames(x = results)[1], "LYZ")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, test.use = "poisson", verbose = FALSE))
test_that("poisson test works", {
expect_equal(nrow(x = results), 149)
expect_equal(results["CST3", "p_val"], 3.792196e-78)
expect_equal(results["CST3", "avg_logFC"], -2.353701, tolerance = 1e-6)
expect_equal(results["CST3", "pct.1"], 0.306)
expect_equal(results["CST3", "pct.2"], 1.00)
expect_equal(results["CST3", "p_val_adj"], 8.722050e-76)
expect_equal(rownames(x = results)[1], "LYZ")
})
results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, test.use = "LR", verbose = FALSE))
test_that("LR test works", {
expect_equal(nrow(x = results), 201)
expect_equal(results["CST3", "p_val"], 3.990707e-16)
expect_equal(results["CST3", "avg_logFC"], -2.552769, tolerance = 1e-6)
expect_equal(results["CST3", "pct.1"], 0.306)
expect_equal(results["CST3", "pct.2"], 1.00)
expect_equal(results["CST3", "p_val_adj"], 9.178625e-14)
expect_equal(rownames(x = results)[1], "LYZ")
})
# Tests for FindConservedMarkers
# --------------------------------------------------------------------------------
context("FindConservedMarkers")
pbmc_small$groups
markers <- suppressWarnings(FindConservedMarkers(object = pbmc_small, ident.1 = 0, grouping.var = "groups", verbose = FALSE))
standard.names <- c("p_val", "avg_logFC", "pct.1", "pct.2", "p_val_adj")
test_that("FindConservedMarkers works", {
expect_equal(colnames(x = markers), c(paste0("g2_", standard.names), paste0("g1_", standard.names), "max_pval", "minimump_p_val"))
expect_equal(markers[1, "g2_p_val"], 4.983576e-05)
expect_equal(markers[1, "g2_avg_logFC"], -4.125279, tolerance = 1e-6)
# expect_equal(markers[1, "g2_pct.1"], 0.062)
expect_equal(markers[1, "g2_pct.2"], 0.75)
expect_equal(markers[1, "g2_p_val_adj"], 0.0114622238)
expect_equal(markers[1, "g1_p_val"], 3.946643e-08)
expect_equal(markers[1, "g1_avg_logFC"], -3.589384, tolerance = 1e-6)
expect_equal(markers[1, "g1_pct.1"], 0.10)
expect_equal(markers[1, "g1_pct.2"], 0.958)
expect_equal(markers[1, "g1_p_val_adj"], 9.077279e-06)
expect_equal(markers[1, "max_pval"], 4.983576e-05)
expect_equal(markers[1, "minimump_p_val"], 7.893286e-08)
expect_equal(nrow(markers), 162)
expect_equal(rownames(markers)[1], "HLA-DRB1")
expect_equal(markers[, "max_pval"], unname(obj = apply(X = markers, MARGIN = 1, FUN = function(x) max(x[c("g1_p_val", "g2_p_val")]))))
})
test_that("FindConservedMarkers errors when expected", {
expect_error(FindConservedMarkers(pbmc_small))
expect_error(FindConservedMarkers(pbmc_small, ident.1 = 0))
expect_error(FindConservedMarkers(pbmc_small, ident.1 = 0, grouping.var = "groups", meta.method = "minimump"))
})
pbmc.test <- pbmc_small
Idents(object = pbmc.test) <- "RNA_snn_res.1"
pbmc.test$id.group <- paste0(pbmc.test$RNA_snn_res.1, "_", pbmc.test$groups)
pbmc.test <- subset(x = pbmc.test, id.group == "0_g1", invert = TRUE)
markers.missing <- suppressWarnings(FindConservedMarkers(object = pbmc.test, ident.1 = 0, grouping.var = "groups", test.use = "t", verbose = FALSE))
test_that("FindConservedMarkers handles missing idents in certain groups", {
expect_warning(FindConservedMarkers(object = pbmc.test, ident.1 = 0, grouping.var = "groups", test.use = "t"))
expect_equal(colnames(x = markers.missing), paste0("g2_", standard.names))
expect_equal(markers.missing[1, "g2_p_val"], 1.672911e-13)
expect_equal(markers.missing[1, "g2_avg_logFC"], -4.527888, tolerance = 1e-6)
# expect_equal(markers.missing[1, "g2_pct.1"], 0.062)
expect_equal(markers.missing[1, "g2_pct.2"], 0.95)
expect_equal(markers.missing[1, "g2_p_val_adj"], 3.847695e-11)
expect_equal(nrow(markers.missing), 190)
expect_equal(rownames(markers.missing)[1], "HLA-DPB1")
})
Seurat/tests/testthat/test_data_manipulation.R 0000644 0001762 0000144 00000022623 13527073365 021412 0 ustar ligges users # Tests for functions in data_manipulation.cpp
# change in random number generation in R3.6, this ensures tests will pass under older and newer Rs
suppressWarnings(RNGversion(vstr = "3.5.3"))
set.seed(42)
library(Matrix)
# Tests for row merging
# --------------------------------------------------------------------------------
context("Row Merging")
m1 <- rsparsematrix(10, 10, 0.1)
m2 <- rsparsematrix(10, 10, 0.1)
m1.names <- paste0("row", sample(1:10, size = 10))
m2.names <- paste0("row", sample(1:20, size = 10))
all.names <- union(m1.names, m2.names)
rownames(m1) <- m1.names
rownames(m2) <- m2.names
m1 <- as(m1, "RsparseMatrix")
m2 <- as(m2, "RsparseMatrix")
test_that("Row merging done correctly", {
m3 <- RowMergeMatrices(mat1 = m1, mat2 = m2, mat1_rownames = m1.names, mat2_rownames = m2.names,
all_rownames = all.names)
expect_equal(m3[1, 14], -0.17)
expect_equal(m3[3, 2], -1.4)
expect_equal(m3[14, 18], -0.43)
expect_equal(length(m3), 280)
})
# Tests for log normalization
# --------------------------------------------------------------------------------
context("Log Normalization")
mat <- as(matrix(1:16, ncol = 4, nrow = 4), "sparseMatrix")
test_that("Log Normalization returns expected values", {
mat.norm.r <- log1p(sweep(mat, 2, Matrix::colSums(mat), FUN = "/") * 1e4)
mat.norm <- LogNorm(mat, 1e4, display_progress = F)
expect_equal(mat.norm[1, ], mat.norm.r[1, ])
expect_equal(mat.norm[4, 4], mat.norm.r[4, 4])
})
# Tests for scaling data
# --------------------------------------------------------------------------------
context("Fast Scale Data Functions")
mat <- matrix(seq(0.001, 0.1, 0.001), nrow = 10, ncol = 10)
# should be the equivalent of t(scale(t(mat)))
test_that("Fast implementation of row scaling returns expected values", {
expect_equal(t(scale(t(mat))[1:10, 1:10]), FastRowScale(mat, display_progress = FALSE))
expect_equal(t(scale(t(mat), center = FALSE))[1:10, 1:10],
FastRowScale(mat, center = FALSE, display_progress = FALSE))
expect_equal(t(scale(t(mat), scale = FALSE))[1:10, 1:10],
FastRowScale(mat, scale = FALSE, display_progress = FALSE))
expect_equal(t(scale(t(mat), scale = FALSE, center = F))[1:10, 1:10],
FastRowScale(mat, scale = FALSE, center = F, display_progress = FALSE))
mat.clipped <- FastRowScale(mat, scale_max = 0.2, display_progress = F)
expect_true(max(mat.clipped, na.rm = T) >= 0.2)
})
# should be the equivalent of scale(mat, TRUE, apply(mat, 2, sd))
test_that("Standardize returns expected values", {
expect_equal(Standardize(mat, display_progress = FALSE), scale(mat, TRUE, apply(mat, 2, sd)),
check.attributes = FALSE)
})
# should be the equivalent of t(scale(t(mat)))
mat <- rsparsematrix(10, 15, 0.1)
test_that("Fast implementation of row scaling returns expected values", {
expect_equal(t(scale(t(as.matrix(mat))))[1:10, 1:15], FastSparseRowScale(mat, display_progress = FALSE),
check.attributes = FALSE)
expect_equal(t(scale(t(as.matrix(mat)), center = FALSE))[1:10, 1:15],
FastSparseRowScale(mat, center = FALSE, display_progress = FALSE),
check.attributes = FALSE)
expect_equal(t(scale(t(as.matrix(mat)), scale = FALSE))[1:10, 1:15],
FastSparseRowScale(mat, scale = FALSE, display_progress = FALSE),
check.attributes = FALSE)
expect_equal(t(scale(t(as.matrix(mat)), scale = FALSE, center = F))[1:10, 1:15],
FastSparseRowScale(mat, scale = FALSE, center = F, display_progress = FALSE),
check.attributes = FALSE)
mat.clipped <- FastSparseRowScale(mat, scale_max = 0.2, display_progress = F)
expect_true(max(mat.clipped, na.rm = T) >= 0.2)
})
mat <- as(object = matrix(rnorm(1000), nrow = 10, ncol = 10), Class = "dgCMatrix")
test_that("Row scaling with known stats works", {
mat.rowmeans <- rowMeans(x = mat)
mat.sd <- apply(X = mat, MARGIN = 1, FUN = sd)
expect_equal(
t(scale(t(as.matrix(mat)), center = mat.rowmeans, scale = mat.sd)),
FastSparseRowScaleWithKnownStats(mat = mat, mu = mat.rowmeans, sigma = mat.sd, scale = TRUE, center = TRUE, scale_max = 10, display_progress = FALSE),
check.attributes = FALSE
)
expect_equal(
t(scale(t(as.matrix(mat)), center = FALSE, scale = mat.sd)),
FastSparseRowScaleWithKnownStats(mat = mat, mu = mat.rowmeans, sigma = mat.sd, scale = TRUE, center = FALSE, scale_max = 10, display_progress = FALSE),
check.attributes = FALSE
)
expect_equal(
t(scale(t(as.matrix(mat)), center = mat.rowmeans, scale = FALSE)),
FastSparseRowScaleWithKnownStats(mat = mat, mu = mat.rowmeans, sigma = mat.sd, scale = FALSE, center = TRUE, scale_max = 10, display_progress = FALSE),
check.attributes = FALSE
)
mat.clipped <- FastSparseRowScaleWithKnownStats(mat = mat, mu = mat.rowmeans, sigma = mat.sd, scale = FALSE, center = TRUE, scale_max = 0.2, display_progress = FALSE)
expect_true(max(mat.clipped, na.rm = T) >= 0.2)
})
# Tests for fast basic stats functions
# --------------------------------------------------------------------------------
context("Fast Basic Stats Functions")
set.seed(42)
mat <- replicate(10, rchisq(10, 4))
fcv <- FastCov(mat)
cv <- cov(mat)
test_that("Fast implementation of covariance returns expected values", {
expect_equal(fcv[1,1], 9.451051142)
expect_equal(fcv[10,10], 5.6650068)
expect_equal(fcv, cv)
})
mat2 <- replicate(10, rchisq(10, 4))
fcv <- FastCovMats(mat1 = mat, mat2 = mat2)
cv <- cov(mat, mat2)
test_that("Fast implementation of covariance returns expected values for matrices", {
expect_equal(fcv[1,1], 1.523417, tolerance = 1e-6)
expect_equal(fcv[10,10], -0.6031694, tolerance = 1e-6)
expect_equal(fcv, cv)
})
merged.mat <- FastRBind(mat, fcv)
test_that("Fast implementation of rbind returns expected values", {
expect_equal(merged.mat, rbind(mat, fcv))
expect_equal(mat[1,1], merged.mat[1,1])
expect_equal(fcv[10,10], merged.mat[20,10])
})
mat <- as(mat, "dgCMatrix")
test_that("Fast implementation of ExpMean returns expected values",{
expect_equal(ExpMean(mat[1,]), FastExpMean(mat, display_progress = F)[1])
expect_equal(ExpMean(mat[5,]), FastExpMean(mat, display_progress = F)[5])
expect_equal(ExpMean(mat[10,]), FastExpMean(mat, display_progress = F)[10])
expect_equal(length(FastExpMean(mat, display_progress = F)), nrow(mat))
expect_error(FastExpMean(mat[1, ], display_progress = F))
expect_equal(FastExpMean(mat[1, ,drop = F], display_progress = F), ExpMean(mat[1,]))
expect_equal(FastExpMean(mat, display_progress = F)[1], 6.493418, tolerance = 1e-6)
expect_equal(FastExpMean(mat, display_progress = F)[5], 6.255206, tolerance = 1e-6)
expect_equal(FastExpMean(mat, display_progress = F)[10], 7.84965, tolerance = 1e-6)
})
test_that("Fast implementation of LogVMR returns expected values", {
expect_equal(LogVMR(mat[1,]), FastLogVMR(mat, display_progress = F)[1])
expect_equal(LogVMR(mat[5,]), FastLogVMR(mat, display_progress = F)[5])
expect_equal(LogVMR(mat[10,]), FastLogVMR(mat, display_progress = F)[10])
expect_equal(length(FastExpMean(mat, display_progress = F)), nrow(mat))
expect_error(FastLogVMR(mat[1, ], display_progress = F))
expect_equal(FastLogVMR(mat[1, ,drop = F], display_progress = F), LogVMR(mat[1,]))
expect_equal(FastLogVMR(mat, display_progress = F)[1], 7.615384, tolerance = 1e-6)
expect_equal(FastLogVMR(mat, display_progress = F)[5], 7.546768, tolerance = 1e-6)
expect_equal(FastLogVMR(mat, display_progress = F)[10], 10.11755, tolerance = 1e-6)
})
test_that("Row variance calculations for sparse matrices work", {
expect_equal(apply(X = mat, MARGIN = 1, FUN = var), SparseRowVar(mat = mat, display_progress = FALSE), tolerance = 1e-6)
expect_equal(apply(X = mat2, MARGIN = 1, FUN = var), SparseRowVar(mat = as(object = mat2, Class = "dgCMatrix"), display_progress = FALSE), tolerance = 1e-6)
})
# Tests for data structure manipulations
# --------------------------------------------------------------------------------
context("Data structure manipulations")
mat <- rsparsematrix(nrow = 10, ncol = 100, density = 0.1)
mat2 <- rsparsematrix(nrow = 10, ncol = 10, density = 0.1)
cols.to.replace1 <- 1:10
cols.to.replace2 <- 10:1
cols.to.replace3 <- 91:100
cols.to.replace4 <- c(10, 15, 33, 2, 6, 99, 55, 30, 25, 42)
ReplaceCols <- function(mat, cols, replace){
mat[, cols] <- replace
return(mat)
}
test_that("Replacing columns works", {
expect_equal(ReplaceColsC(mat = mat, col_idx = cols.to.replace1 - 1, replacement = mat2),
ReplaceCols(mat = mat, cols = cols.to.replace1, replace = mat2))
expect_equal(ReplaceColsC(mat = mat, col_idx = cols.to.replace2 - 1, replacement = mat2),
ReplaceCols(mat = mat, cols = cols.to.replace2, replace = mat2))
expect_equal(ReplaceColsC(mat = mat, col_idx = cols.to.replace3 - 1, replacement = mat2),
ReplaceCols(mat = mat, cols = cols.to.replace3, replace = mat2))
expect_equal(ReplaceColsC(mat = mat, col_idx = cols.to.replace4 - 1, replacement = mat2),
ReplaceCols(mat = mat, cols = cols.to.replace4, replace = mat2))
})
test_that("Cpp implementation of row variance is correct", {
expect_equal(apply(X = mat, MARGIN = 1, FUN = var), RowVar(as.matrix(mat)))
expect_equal(apply(X = merged.mat, MARGIN = 1, FUN = var), RowVar(as.matrix(merged.mat)))
})
Seurat/tests/testthat/test_utilities.R 0000644 0001762 0000144 00000001731 13602476667 017737 0 ustar ligges users set.seed(42)
pbmc.file <- system.file('extdata', 'pbmc_raw.txt', package = 'Seurat')
pbmc.test <- as(as.matrix(read.table(pbmc.file, sep = "\t", row.names = 1)), "dgCMatrix")
meta.data <- data.frame(
a = rep(as.factor(c('a', 'b', 'c')), length.out = ncol(pbmc.test)),
row.names = colnames(pbmc.test)
)
object.filtered <- CreateSeuratObject(
counts = pbmc.test,
min.cells = 10,
min.features = 30,
meta.data = meta.data
)
test_that("AverageExpression", {
object <- SetIdent(object.filtered, value = 'a')
average.expression <- AverageExpression(object, slot = 'data')$RNA
expect_equivalent(average.expression['KHDRBS1', 1:3],
c(a = 7.278237e-01, b = 1.658166e+14, c = 1.431902e-01),
tolerance = 1e-6
)
expect_equivalent(average.expression['DNAJB1', 1:3] ,
c(a = 1.374079e+00, b = 5.100840e-01, c = 5.011655e-01),
tolerance = 1e-6)
})
Seurat/tests/testthat/test_objects.R 0000644 0001762 0000144 00000030770 13527073365 017354 0 ustar ligges users # Tests for functions in objects.R
# Tests for interacting with the meta.data slot
# ------------------------------------------------------------------------------
context("Metadata")
cluster_letters <- LETTERS[Idents(object = pbmc_small)]
names(cluster_letters) <- colnames(x = pbmc_small)
cluster_letters_shuffled <- sample(x = cluster_letters)
test_that("AddMetaData adds in cell-level vector properly ", {
pbmc_small <- AddMetaData(object = pbmc_small, metadata = cluster_letters, col.name = 'letter.idents')
expect_equal(pbmc_small$letter.idents, cluster_letters)
pbmc_small <- AddMetaData(object = pbmc_small, metadata = cluster_letters_shuffled, col.name = 'letter.idents.shuffled')
expect_equal(pbmc_small$letter.idents, pbmc_small$letter.idents.shuffled)
})
cluster_letters_df <- data.frame(A = cluster_letters, B = cluster_letters_shuffled)
test_that("AddMetaData adds in data frame properly for cell-level metadata", {
pbmc_small <- AddMetaData(object = pbmc_small, metadata = cluster_letters_df)
expect_equal(pbmc_small[[c("A", "B")]], cluster_letters_df)
})
feature_letters <- sample(x = LETTERS, size = nrow(x = pbmc_small[["RNA"]]), replace = TRUE)
names(feature_letters) <- rownames(x = pbmc_small[["RNA"]])
feature_letters_shuffled <- sample(x = feature_letters)
test_that("AddMetaData adds feature level metadata", {
pbmc_small[["RNA"]] <- AddMetaData(object = pbmc_small[["RNA"]], metadata = feature_letters, col.name = 'feature_letters')
expect_equal(pbmc_small[["RNA"]][["feature_letters", drop = TRUE]], feature_letters)
pbmc_small[["RNA"]] <- AddMetaData(object = pbmc_small[["RNA"]], metadata = feature_letters_shuffled, col.name = 'feature_letters_shuffled')
expect_equal(pbmc_small[["RNA"]][["feature_letters", drop = TRUE]], pbmc_small[["RNA"]][["feature_letters_shuffled", drop = TRUE]])
})
feature_letters_df <- data.frame(A = feature_letters, B = feature_letters_shuffled)
test_that("AddMetaData adds in data frame properly for Assays", {
pbmc_small[["RNA"]] <- AddMetaData(object = pbmc_small[["RNA"]], metadata = feature_letters_df)
expect_equal(pbmc_small[["RNA"]][[c("A", "B")]], feature_letters_df)
})
test_that("AddMetaData errors", {
expect_error(AddMetaData(object = pbmc_small, metadata = cluster_letters, col.name = "RNA"))
expect_error(AddMetaData(object = pbmc_small, metadata = c(unname(cluster_letters), "A"), col.name = "letter.idents"))
expect_error(AddMetaData(object = pbmc_small, metadata = feature_letters, col.name = "letter.idents"))
expect_error(AddMetaData(object = pbmc_small[["RNA"]], metadata = cluster_letters, col.name = "letter.idents"))
})
# Tests for creating an Assay object
# ------------------------------------------------------------------------------
context("CreateAssayObject")
pbmc.raw <- GetAssayData(object = pbmc_small[["RNA"]], slot = "counts")
rna.assay <- CreateAssayObject(counts = pbmc.raw)
rna.assay2 <- CreateAssayObject(data = pbmc.raw)
test_that("CreateAssayObject works as expected", {
expect_equal(dim(x = rna.assay), c(230, 80))
expect_equal(rownames(x = rna.assay), rownames(x = pbmc.raw))
expect_equal(colnames(x = rna.assay), colnames(x = pbmc.raw))
expect_equal(GetAssayData(object = rna.assay, slot = "counts"), pbmc.raw)
expect_equal(GetAssayData(object = rna.assay, slot = "data"), pbmc.raw)
expect_equal(GetAssayData(object = rna.assay, slot = "scale.data"), new(Class = "matrix"))
expect_equal(dim(rna.assay[[]]), c(230, 0))
expect_equal(rownames(x = rna.assay[[]]), rownames(x = rna.assay))
expect_equal(VariableFeatures(object = rna.assay), vector())
expect_equal(rna.assay@misc, NULL)
expect_equal(GetAssayData(object = rna.assay2, slot = "counts"), new(Class = "matrix"))
})
rna.assay2 <- CreateAssayObject(counts = pbmc.raw, min.cells = 10, min.features = 30)
test_that("CreateAssayObject filtering works", {
expect_equal(dim(x = rna.assay2), c(163, 77))
expect_true(all(rowSums(GetAssayData(object = rna.assay2, slot = "counts")) >= 10))
expect_true(all(colSums(GetAssayData(object = rna.assay2, slot = "counts")) >= 30))
})
test_that("CreateAssayObject catches improper input", {
expect_error(CreateAssayObject())
expect_error(CreateAssayObject(counts = pbmc.raw, data = pbmc.raw))
pbmc.raw2 <- cbind(pbmc.raw[, 1:10], pbmc.raw[, 1:10])
expect_warning(CreateAssayObject(counts = pbmc.raw2))
expect_warning(CreateAssayObject(data = pbmc.raw2))
pbmc.raw2 <- rbind(pbmc.raw[1:10, ], pbmc.raw[1:10, ])
expect_warning(CreateAssayObject(counts = pbmc.raw2))
expect_warning(CreateAssayObject(data = pbmc.raw2))
pbmc.raw2 <- pbmc.raw
colnames(x = pbmc.raw2) <- c()
expect_error(CreateAssayObject(counts = pbmc.raw2))
expect_error(CreateAssayObject(data = pbmc.raw2))
pbmc.raw2 <- pbmc.raw
rownames(x = pbmc.raw2) <- c()
expect_error(CreateAssayObject(counts = pbmc.raw2))
expect_error(CreateAssayObject(data = pbmc.raw2))
pbmc.raw.mat <- as.matrix(x = pbmc.raw)
pbmc.raw.df <- as.data.frame(x = pbmc.raw.mat)
rna.assay3 <- CreateAssayObject(counts = pbmc.raw.df)
rna.assay4 <- CreateAssayObject(counts = pbmc.raw.mat)
expect_is(object = GetAssayData(object = rna.assay3, slot = "counts"), class = "dgCMatrix")
expect_is(object = GetAssayData(object = rna.assay4, slot = "counts"), class = "dgCMatrix")
pbmc.raw.underscores <- pbmc.raw
rownames(pbmc.raw.underscores) <- gsub(pattern = "-", replacement = "_", x = rownames(pbmc.raw.underscores))
expect_warning(CreateAssayObject(counts = pbmc.raw.underscores))
})
# Tests for creating an DimReduc object
# ------------------------------------------------------------------------------
context("CreateDimReducObject")
pca <- pbmc_small[["pca"]]
Key(object = pca) <- 'PC_'
test_that("CreateDimReducObject works", {
pca.dr <- CreateDimReducObject(
embeddings = Embeddings(object = pca),
loadings = Loadings(object = pca),
projected = Loadings(object = pca, projected = TRUE),
assay = "RNA"
)
expect_equal(Embeddings(object = pca.dr), Embeddings(object = pca))
expect_equal(Loadings(object = pca.dr), Loadings(object = pca))
expect_equal(Loadings(object = pca.dr, projected = TRUE), Loadings(object = pca, projected = TRUE))
expect_equal(Key(object = pca.dr), "PC_")
expect_equal(pca.dr@assay.used, "RNA")
})
test_that("CreateDimReducObject catches improper input", {
bad.embeddings <- Embeddings(object = pca)
colnames(x = bad.embeddings) <- paste0("PCA", 1:ncol(x = bad.embeddings))
expect_warning(CreateDimReducObject(embeddings = bad.embeddings, key = "PC"))
colnames(x = bad.embeddings) <- paste0("PC", 1:ncol(x = bad.embeddings), "X")
suppressWarnings(expect_error(CreateDimReducObject(embeddings = bad.embeddings, key = "PC")))
suppressWarnings(expect_error(CreateDimReducObject(embeddings = bad.embeddings)))
})
# Tests for creating a Seurat object
# ------------------------------------------------------------------------------
context("CreateSeuratObject")
colnames(x = pbmc.raw) <- paste0(colnames(x = pbmc.raw), "-", pbmc_small$groups)
metadata.test <- pbmc_small[[]][, 5:7]
rownames(x = metadata.test) <- colnames(x = pbmc.raw)
test_that("CreateSeuratObject works", {
seurat.object <- CreateSeuratObject(
counts = pbmc.raw,
project = "TESTING",
assay = "RNA.TEST",
names.field = 2,
names.delim = "-",
meta.data = metadata.test
)
expect_equal(seurat.object[[]][, 4:6], metadata.test)
expect_equal(seurat.object@project.name, "TESTING")
expect_equal(names(x = seurat.object), "RNA.TEST")
expect_equal(as.vector(x = unname(obj = Idents(object = seurat.object))), unname(pbmc_small$groups))
})
test_that("CreateSeuratObject handles bad names.field/names.delim", {
expect_warning(seurat.object <- CreateSeuratObject(
counts = pbmc.raw[1:5,1:5],
names.field = 3,
names.delim = ":",
meta.data = metadata.test
))
})
# Tests for creating a Seurat object
# ------------------------------------------------------------------------------
context("Merging")
pbmc.assay <- pbmc_small[["RNA"]]
x <- merge(x = pbmc.assay, y = pbmc.assay)
test_that("Merging Assays works properly", {
expect_equal(dim(GetAssayData(object = x, slot = "counts")), c(230, 160))
expect_equal(dim(GetAssayData(object = x, slot = "data")), c(230, 160))
expect_equal(GetAssayData(object = x, slot = "scale.data"), new(Class = "matrix"))
expect_equal(Key(object = x), "rna_")
expect_equal(VariableFeatures(object = x), vector())
expect_equal(x[[]], data.frame(row.names = rownames(x = pbmc.assay)))
})
pbmc.assay2 <- pbmc.assay
pbmc.assay2@counts <- new("dgCMatrix")
test_that("Merging Assays handles case when counts not present", {
y <- merge(x = pbmc.assay2, y = pbmc.assay)
expect_equal(unname(colSums(x = GetAssayData(object = y, slot = "counts"))[1:80]), rep.int(x = 0, times = 80))
z <- merge(x = pbmc.assay2, pbmc.assay2)
expect_equal(nnzero(x = GetAssayData(object = z, slot = "counts")), 0)
})
pbmc.assay2 <- pbmc.assay
pbmc.assay2@data <- new("dgCMatrix")
test_that("Merging Assays handles case when data not present", {
y <- merge(x = pbmc.assay2, y = pbmc.assay, merge.data = TRUE)
expect_equal(unname(colSums(x = GetAssayData(object = y, slot = "data"))[1:80]), rep.int(x = 0, times = 80))
z <- merge(x = pbmc.assay2, y = pbmc.assay2, merge.data = TRUE)
expect_equal(nnzero(x = GetAssayData(object = z, slot = "data")), 0)
})
# Tests for FetchData
# ------------------------------------------------------------------------------
context("FetchData")
# Features to test:
# able to pull cell embeddings, data, metadata
# subset of cells
test_that("Fetching a subset of cells works", {
x <- FetchData(object = pbmc_small, cells = colnames(x = pbmc_small)[1:10], vars = rownames(x = pbmc_small)[1])
expect_equal(rownames(x = x), colnames(x = pbmc_small)[1:10])
random.cells <- sample(x = colnames(x = pbmc_small), size = 10)
x <- FetchData(object = pbmc_small, cells = random.cells, vars = rownames(x = pbmc_small)[1])
expect_equal(rownames(x = x), random.cells)
x <- FetchData(object = pbmc_small, cells = 1:10, vars = rownames(x = pbmc_small)[1])
expect_equal(rownames(x = x), colnames(x = pbmc_small)[1:10])
})
suppressWarnings(pbmc_small[["RNA2"]] <- pbmc_small[["RNA"]])
Key(pbmc_small[["RNA2"]]) <- "rna2_"
test_that("Fetching keyed variables works", {
x <- FetchData(object = pbmc_small, vars = c(paste0("rna_", rownames(x = pbmc_small)[1:5]), paste0("rna2_", rownames(x = pbmc_small)[1:5])))
expect_equal(colnames(x = x), c(paste0("rna_", rownames(x = pbmc_small)[1:5]), paste0("rna2_", rownames(x = pbmc_small)[1:5])))
x <- FetchData(object = pbmc_small, vars = c(paste0("rna_", rownames(x = pbmc_small)[1:5]), paste0("PC_", 1:5)))
expect_equal(colnames(x = x), c(paste0("rna_", rownames(x = pbmc_small)[1:5]), paste0("PC_", 1:5)))
})
test_that("Fetching embeddings/loadings not present returns warning or errors", {
expect_warning(FetchData(object = pbmc_small, vars = c("PC_1", "PC_100")))
expect_error(FetchData(object = pbmc_small, vars = "PC_100"))
})
bad.gene <- GetAssayData(object = pbmc_small[["RNA"]], slot = "data")
rownames(x = bad.gene)[1] <- paste0("rna_", rownames(x = bad.gene)[1])
pbmc_small[["RNA"]]@data <- bad.gene
# Tests for WhichCells
# ------------------------------------------------------------------------------
test_that("Specifying cells works", {
test.cells <- Cells(x = pbmc_small)[1:10]
expect_equal(WhichCells(object = pbmc_small, cells = test.cells), test.cells)
expect_equal(WhichCells(object = pbmc_small, cells = test.cells, invert = TRUE), setdiff(Cells(x = pbmc_small), test.cells))
})
test_that("Specifying idents works", {
c12 <- WhichCells(object = pbmc_small, idents = c(1, 2))
expect_equal(length(x = c12), 44)
expect_equal(c12[44], "CTTGATTGATCTTC")
expect_equal(c12, WhichCells(object = pbmc_small, idents = 0, invert = TRUE))
})
test_that("downsample works", {
expect_equal(length(x = WhichCells(object = pbmc_small, downsample = 5)), 15)
expect_equal(length(x = WhichCells(object = pbmc_small, downsample = 100)), 80)
})
test_that("passing an expression works", {
lyz.pos <- WhichCells(object = pbmc_small, expression = LYZ > 1)
expect_true(all(GetAssayData(object = pbmc_small, slot = "data")["LYZ", lyz.pos] > 1))
# multiple values in expression
lyz.pos <- WhichCells(object = pbmc_small, expression = LYZ > 1 & groups == "g1")
expect_equal(length(x = lyz.pos), 30)
expect_equal(lyz.pos[30], "CTTGATTGATCTTC")
})
Seurat/tests/testthat/test_preprocessing.R 0000644 0001762 0000144 00000042244 13527073365 020605 0 ustar ligges users # Tests for functions dependent on a seurat object
set.seed(42)
pbmc.file <- system.file('extdata', 'pbmc_raw.txt', package = 'Seurat')
pbmc.test <- as(as.matrix(read.table(pbmc.file, sep = "\t", row.names = 1)), "dgCMatrix")
# Tests for object creation (via CreateSeuratObject)
# --------------------------------------------------------------------------------
context("Object creation")
fake.meta.data <- data.frame(rep(1, ncol(pbmc.test)))
rownames(fake.meta.data) <- colnames(pbmc.test)
colnames(fake.meta.data) <- "FMD"
object <- CreateSeuratObject(counts = pbmc.test,
meta.data = fake.meta.data)
test_that("object initialization actually creates seurat object", {
expect_is(object, "Seurat")
})
test_that("meta.data slot generated correctly", {
expect_equal(dim(object[[]]), c(80, 4))
expect_equal(colnames(object[[]]), c("orig.ident", "nCount_RNA", "nFeature_RNA", "FMD"))
expect_equal(rownames(object[[]]), colnames(object))
expect_equal(object[["nFeature_RNA"]][1:5, ], c(47, 52, 50, 56, 53))
expect_equal(object[["nCount_RNA"]][75:80, ], c(228, 527, 202, 157, 150, 233))
})
object.filtered <- CreateSeuratObject(
counts = pbmc.test,
min.cells = 10,
min.features = 30
)
test_that("Filtering handled properly", {
expect_equal(nrow(x = GetAssayData(object = object.filtered, slot = "counts")), 163)
expect_equal(ncol(x = GetAssayData(object = object.filtered, slot = "counts")), 77)
})
test_that("Metadata check errors correctly", {
pbmc.md <- pbmc_small[[]]
pbmc.md.norownames <- as.matrix(pbmc.md)
rownames(pbmc.md.norownames) <- NULL
expect_error(CreateSeuratObject(counts = pbmc.test, meta.data = pbmc.md.norownames),
"Row names not set in metadata. Please ensure that rownames of metadata match column names of data matrix")
})
# Tests for NormalizeData
# --------------------------------------------------------------------------------
context("NormalizeData")
test_that("NormalizeData error handling", {
expect_error(NormalizeData(object = object, assay = "FAKE"))
expect_equal(
object = GetAssayData(
object = NormalizeData(
object = object,
normalization.method = NULL,
verbose = FALSE
),
slot = "data"
),
expected = GetAssayData(object = object, slot = "counts")
)
})
object <- NormalizeData(object = object, verbose = FALSE, scale.factor = 1e6)
test_that("NormalizeData scales properly", {
expect_equal(GetAssayData(object = object, slot = "data")[2, 1], 9.567085, tolerance = 1e-6)
expect_equal(GetAssayData(object = object, slot = "data")[161, 55], 8.415309, tolerance = 1e-6)
expect_equal(Command(object = object, command = "NormalizeData.RNA", value = "scale.factor"), 1e6)
expect_equal(Command(object = object, command = "NormalizeData.RNA", value = "normalization.method"), "LogNormalize")
})
normalized.data <- LogNormalize(data = GetAssayData(object = object[["RNA"]], slot = "counts"), verbose = FALSE)
test_that("LogNormalize normalizes properly", {
expect_equal(
LogNormalize(data = GetAssayData(object = object[["RNA"]], slot = "counts"), verbose = FALSE),
LogNormalize(data = as.data.frame(as.matrix(GetAssayData(object = object[["RNA"]], slot = "counts"))), verbose = FALSE)
)
})
clr.counts <- NormalizeData(object = pbmc.test, normalization.method = "CLR", verbose = FALSE)
test_that("CLR normalization returns expected values", {
expect_equal(dim(clr.counts), c(dim(pbmc.test)))
expect_equal(clr.counts[2, 1], 0.5517828, tolerance = 1e-6)
expect_equal(clr.counts[228, 76], 0.5971381, tolerance = 1e-6)
expect_equal(clr.counts[230, 80], 0)
})
rc.counts <- NormalizeData(object = pbmc.test, normalization.method = "RC", verbose = FALSE)
test_that("Relative count normalization returns expected values", {
expect_equal(rc.counts[2, 1], 142.8571, tolerance = 1e-6)
expect_equal(rc.counts[228, 76], 18.97533, tolerance = 1e-6)
expect_equal(rc.counts[230, 80], 0)
rc.counts <- NormalizeData(object = pbmc.test, normalization.method = "RC", verbose = FALSE, scale.factor = 1e6)
expect_equal(rc.counts[2, 1], 14285.71, tolerance = 1e-6)
})
# Tests for ScaleData
# --------------------------------------------------------------------------------
context("ScaleData")
object <- ScaleData(object, verbose = FALSE)
test_that("ScaleData returns expected values when input is a sparse matrix", {
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[1, 1], -0.4148587, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[75, 25], -0.2562305, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[162, 59], -0.4363939, tolerance = 1e-6)
})
new.data <- as.matrix(GetAssayData(object = object[["RNA"]], slot = "data"))
new.data[1, ] <- rep(x = 0, times = ncol(x = new.data))
object2 <- object
object2[["RNA"]] <- SetAssayData(
object = object[["RNA"]],
slot = "data",
new.data = new.data
)
object2 <- ScaleData(object = object2, verbose = FALSE)
object <- ScaleData(object = object, verbose = FALSE)
test_that("ScaleData returns expected values when input is not sparse", {
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[75, 25], -0.2562305, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[162, 59], -0.4363939, tolerance = 1e-6)
})
test_that("ScaleData handles zero variance features properly", {
expect_equal(GetAssayData(object = object2[["RNA"]], slot = "scale.data")[1, 1], 0)
expect_equal(GetAssayData(object = object2[["RNA"]], slot = "scale.data")[1, 80], 0)
})
ng1 <- rep(x = "g1", times = round(x = ncol(x = object) / 2))
object$group <- c(ng1, rep(x = "g2", times = ncol(x = object) - length(x = ng1)))
g1 <- subset(x = object, group == "g1")
g1 <- ScaleData(object = g1, features = rownames(x = g1), verbose = FALSE)
g2 <- subset(x = object, group == "g2")
g2 <- ScaleData(object = g2, features = rownames(x = g2), verbose = FALSE)
object <- ScaleData(object = object, features = rownames(x = object), verbose = FALSE, split.by = "group")
test_that("split.by option works", {
expect_equal(GetAssayData(object = object, slot = "scale.data")[, Cells(x = g1)],
GetAssayData(object = g1, slot = "scale.data"))
expect_equal(GetAssayData(object = object, slot = "scale.data")[, Cells(x = g2)],
GetAssayData(object = g2, slot = "scale.data"))
})
g1 <- ScaleData(object = g1, features = rownames(x = g1), vars.to.regress = "nCount_RNA", verbose = FALSE)
g2 <- ScaleData(object = g2, features = rownames(x = g2), vars.to.regress = "nCount_RNA", verbose = FALSE)
object <- ScaleData(object = object, features = rownames(x = object), verbose = FALSE, split.by = "group", vars.to.regress = "nCount_RNA")
test_that("split.by option works with regression", {
expect_equal(GetAssayData(object = object, slot = "scale.data")[, Cells(x = g1)],
GetAssayData(object = g1, slot = "scale.data"))
expect_equal(GetAssayData(object = object, slot = "scale.data")[, Cells(x = g2)],
GetAssayData(object = g2, slot = "scale.data"))
})
# Tests for various regression techniques
context("Regression")
object <- ScaleData(
object = object,
vars.to.regress = "nCount_RNA",
features = rownames(x = object)[1:10],
verbose = FALSE,
model.use = "linear")
test_that("Linear regression works as expected", {
expect_equal(dim(x = GetAssayData(object = object[["RNA"]], slot = "scale.data")), c(10, 80))
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[1, 1], -0.6436435, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[5, 25], -0.09035383, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[10, 80], -0.2723782, tolerance = 1e-6)
})
object <- ScaleData(
object,
vars.to.regress = "nCount_RNA",
features = rownames(x = object)[1:10],
verbose = FALSE,
model.use = "negbinom")
test_that("Negative binomial regression works as expected", {
expect_equal(dim(x = GetAssayData(object = object[["RNA"]], slot = "scale.data")), c(10, 80))
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[1, 1], -0.5888811, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[5, 25], -0.2553394, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[10, 80], -0.1921429, tolerance = 1e-6)
})
test_that("Regression error handling checks out", {
expect_error(ScaleData(object, vars.to.regress = "nCount_RNA", model.use = "not.a.model", verbose = FALSE))
})
object <- ScaleData(
object,
vars.to.regress = "nCount_RNA",
features = rownames(x = object)[1:10],
verbose = FALSE,
model.use = "poisson")
test_that("Poisson regression works as expected", {
expect_equal(dim(x = GetAssayData(object = object[["RNA"]], slot = "scale.data")), c(10, 80))
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[1, 1], -1.011717, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[5, 25], 0.05575307, tolerance = 1e-6)
expect_equal(GetAssayData(object = object[["RNA"]], slot = "scale.data")[10, 80], -0.1662119, tolerance = 1e-6)
})
#Tests for SampleUMI
#--------------------------------------------------------------------------------
context("SampleUMI")
downsampled.umis <- SampleUMI(
data = GetAssayData(object = object, slot = "counts"),
max.umi = 100,
verbose = FALSE
)
downsampled.umis.p.cell <- SampleUMI(
data = GetAssayData(object = object, slot = "counts"),
max.umi = seq(50, 840, 10),
verbose = FALSE,
upsample = TRUE
)
test_that("SampleUMI gives reasonable downsampled/upsampled UMI counts", {
expect_true(!any(colSums(x = downsampled.umis) < 30, colSums(x = downsampled.umis) > 120))
expect_error(SampleUMI(data = GetAssayData(object = object, slot = "raw.data"), max.umi = rep(1, 5)))
expect_true(!is.unsorted(x = colSums(x = downsampled.umis.p.cell)))
expect_error(SampleUMI(
data = GetAssayData(object = object, slot = "counts"),
max.umi = seq(50, 900, 10),
verbose = FALSE,
upsample = TRUE
))
})
# Tests for FindVariableFeatures
# --------------------------------------------------------------------------------
context("FindVariableFeatures")
object <- FindVariableFeatures(object = object, selection.method = "mean.var.plot", verbose = FALSE)
test_that("mean.var.plot selection option returns expected values", {
expect_equal(VariableFeatures(object = object)[1:4], c("PTGDR", "SATB1", "ZNF330", "S100B"))
expect_equal(length(x = VariableFeatures(object = object)), 20)
expect_equal(HVFInfo(object = object[["RNA"]], selection.method = 'mvp')$mean[1:2], c(8.328927, 8.444462), tolerance = 1e6)
expect_equal(HVFInfo(object = object[["RNA"]], selection.method = 'mvp')$dispersion[1:2], c(10.552507, 10.088223), tolerance = 1e6)
expect_equal(as.numeric(HVFInfo(object = object[["RNA"]], selection.method = 'mvp')$dispersion.scaled[1:2]), c(0.1113214, -0.1113214), tolerance = 1e6)
})
object <- FindVariableFeatures(object, selection.method = "dispersion", verbose = FALSE)
test_that("dispersion selection option returns expected values", {
expect_equal(VariableFeatures(object = object)[1:4], c("PCMT1", "PPBP", "LYAR", "VDAC3"))
expect_equal(length(x = VariableFeatures(object = object)), 230)
expect_equal(HVFInfo(object = object[["RNA"]], selection.method = 'mvp')$mean[1:2], c(8.328927, 8.444462), tolerance = 1e6)
expect_equal(HVFInfo(object = object[["RNA"]], selection.method = 'mvp')$dispersion[1:2], c(10.552507, 10.088223), tolerance = 1e6)
expect_equal(as.numeric(HVFInfo(object = object[["RNA"]], selection.method = 'mvp')$dispersion.scaled[1:2]), c(0.1113214, -0.1113214), tolerance = 1e6)
expect_true(!is.unsorted(rev(HVFInfo(object = object[["RNA"]], selection.method = 'mvp')[VariableFeatures(object = object), "dispersion"])))
})
object <- FindVariableFeatures(object, selection.method = "vst", verbose = FALSE)
test_that("vst selection option returns expected values", {
expect_equal(VariableFeatures(object = object)[1:4], c("PPBP", "IGLL5", "VDAC3", "CD1C"))
expect_equal(length(x = VariableFeatures(object = object)), 230)
expect_equal(unname(object[["RNA"]][["vst.variance", drop = TRUE]][1:2]), c(1.0251582, 1.2810127), tolerance = 1e6)
expect_equal(unname(object[["RNA"]][["vst.variance.expected", drop = TRUE]][1:2]), c(1.1411616, 2.7076228), tolerance = 1e6)
expect_equal(unname(object[["RNA"]][["vst.variance.standardized", drop = TRUE]][1:2]), c(0.8983463, 0.4731134), tolerance = 1e6)
expect_true(!is.unsorted(rev(object[["RNA"]][["vst.variance.standardized", drop = TRUE]][VariableFeatures(object = object)])))
})
# Tests for internal functions
# ------------------------------------------------------------------------------
norm.fxn <- function(x) {x / mean(x)}
test_that("CustomNormalize works as expected", {
expect_equal(
CustomNormalize(data = pbmc.test, custom_function = norm.fxn, margin = 2),
apply(X = pbmc.test, MARGIN = 2, FUN = norm.fxn)
)
expect_equal(
CustomNormalize(data = as.matrix(pbmc.test), custom_function = norm.fxn, margin = 2),
apply(X = pbmc.test, MARGIN = 2, FUN = norm.fxn)
)
expect_equal(
CustomNormalize(data = as.data.frame(as.matrix(pbmc.test)), custom_function = norm.fxn, margin = 2),
apply(X = pbmc.test, MARGIN = 2, FUN = norm.fxn)
)
expect_equal(
CustomNormalize(data = pbmc.test, custom_function = norm.fxn, margin = 1),
t(apply(X = pbmc.test, MARGIN = 1, FUN = norm.fxn))
)
expect_error(CustomNormalize(data = pbmc.test, custom_function = norm.fxn, margin = 10))
})
# Tests for SCTransform
# ------------------------------------------------------------------------------
object <- suppressWarnings(SCTransform(object = object, verbose = FALSE))
test_that("SCTransform wrapper works as expected", {
expect_true("SCT" %in% names(object))
expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[1]), 13.33038640)
expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[5]), 0)
expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 55.29678, tolerance = 1e6)
expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.74404, tolerance = 1e6)
expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 123)
expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 28)
expect_equal(length(VariableFeatures(object[["SCT"]])), 220)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.detection_rate"], 0.15)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.gmean"], 0.2027364, tolerance = 1e6)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.variance"], 1.025158, tolerance = 1e6)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.residual_mean"], 0.2512783, tolerance = 1e6)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.residual_variance"], 3.551259, tolerance = 1e6)
})
object <- suppressWarnings(SCTransform(object = object, ncells = 40, verbose = FALSE))
test_that("SCTransform ncells param works", {
expect_true("SCT" %in% names(object))
expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[1]), 11.834969847)
expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[5]), 0)
expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 55.29678, tolerance = 1e6)
expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.74404, tolerance = 1e6)
expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 121)
expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 25)
expect_equal(length(VariableFeatures(object[["SCT"]])), 220)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.detection_rate"], 0.15)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.gmean"], 0.2027364, tolerance = 1e6)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.variance"], 1.025158, tolerance = 1e6)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.residual_mean"], 0.2512783, tolerance = 1e6)
expect_equal(object[["SCT"]][[]]["MS4A1", "sct.residual_variance"], 3.551259, tolerance = 1e6)
})
suppressWarnings(object[["SCT_SAVE"]] <- object[["SCT"]])
object[["SCT"]] <- SetAssayData(object = object[["SCT"]], slot = "scale.data", new.data = GetAssayData(object = object[["SCT"]], slot = "scale.data")[1:100, ])
object <- GetResidual(object = object, features = rownames(x = object), verbose = FALSE)
test_that("GetResidual works", {
expect_equal(dim(GetAssayData(object = object[["SCT"]], slot = "scale.data")), c(220, 80))
expect_equal(
GetAssayData(object = object[["SCT"]], slot = "scale.data"),
GetAssayData(object = object[["SCT_SAVE"]], slot = "scale.data")
)
expect_warning(GetResidual(object, features = "asd"))
})
Seurat/tests/testthat/test_visualization.R 0000644 0001762 0000144 00000001630 13602476667 020623 0 ustar ligges users # Tests for functions in visualization.R
set.seed(42)
# Tests for visualization utilities
# ------------------------------------------------------------------------------
pbmc_small[["tsne_new"]] <- CollapseEmbeddingOutliers(pbmc_small,
reduction = "tsne", reduction.key = 'tsne_', outlier.sd = 0.5)
test_that("CollapseEmbeddingOutliers works", {
expect_equal(Embeddings(pbmc_small[["tsne_new"]])[1, 1], -12.59713, tolerance = 1e-6)
expect_equal(colSums(x = Embeddings(object = pbmc_small[["tsne_new"]])), c(-219.9218, 182.9215), check.attributes = FALSE, tolerance = 1e-5)
})
test_that("DiscretePalette works", {
isColors <- function(x) {
all(grepl("#[0-9A-Fa-f]{6}", x))
}
expect_true(isColors(DiscretePalette(26)))
expect_true(isColors(DiscretePalette(32)))
expect_true(isColors(DiscretePalette(36)))
expect_warning(DiscretePalette(50), "Not enough colours")
})
Seurat/tests/testthat/test_modularity_optimizer.R 0000644 0001762 0000144 00000011417 13527073365 022213 0 ustar ligges users # Tests to verify the RCpp version of ModularityOptimizer produces the same
# results as the java version.
# Equivalent java commands are given above.
context("ModularityOptimizer")
# The "karate club" network available from the ModularityOptimizer website at:
# http://www.ludowaltman.nl/slm/
node1 <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 6, 8, 8, 8, 9, 13,
14, 14, 15, 15, 18, 18, 19, 20, 20, 22, 22, 23, 23, 23, 23, 23, 24,
24, 24, 25, 26, 26, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32)
node2 <- c(1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 17, 19, 21, 31, 2, 3, 7, 13,
17, 19, 21, 30, 3, 7, 8, 9, 13, 27, 28, 32, 7, 12, 13, 6, 10, 6, 10,
16, 16, 30, 32, 33, 33, 33, 32, 33, 32, 33, 32, 33, 33, 32, 33, 32,
33, 25, 27, 29, 32, 33, 25, 27, 31, 31, 29, 33, 33, 31, 33, 32, 33,
32, 33, 32, 33, 33)
dim_s <- max(max(node1), max(node2)) + 1
# Note we want to represent network in the lower diagonal.
connections <- sparseMatrix(i = node2 + 1, j = node1 + 1, x = 1.0)
# Result from equivalent command to
# java -jar ModularityOptimizer.jar karate_club_network.txt communities.txt 1 1.0 1 1 1 564 0
test_that("Algorithm 1", {
expected <- c(1, 1, 1, 1, 2, 2, 2, 1, 0, 1, 2, 1, 1, 1, 0, 0, 2, 1, 0, 1, 0, 1,
0, 0, 3, 3, 0, 0, 3, 0, 0, 3, 0, 0)
s <- Seurat:::RunModularityClusteringCpp(
SNN = connections,
modularityFunction = 1,
resolution = 1.0,
algorithm = 1,
nRandomStarts = 1,
nIterations = 1,
randomSeed = 564,
printOutput = 0,
""
)
expect_equal(expected, s)
})
#java -jar ModularityOptimizer.jar karate_club_network.txt communities.txt 1 1.0 2 1 1 2 0
test_that("Algorithm 2", {
expected <- c(1, 1, 1, 1, 3, 3, 3, 1, 0, 0, 3, 1, 1, 1, 0, 0, 3, 1, 0, 1, 0, 1,
0, 2, 2, 2, 0, 2, 2, 0, 0, 2, 0, 0)
s <- Seurat:::RunModularityClusteringCpp(
SNN = connections,
modularityFunction = 1,
resolution = 1.0,
algorithm = 2,
nRandomStarts = 1,
nIterations = 1,
randomSeed = 2,
printOutput = 0,
""
)
expect_equal(expected, s)
})
#java -jar ModularityOptimizer.jar karate_club_network.txt communities.txt 1 1.0 3 1 1 56464 0
test_that("Algorithm 3", {
expected <- c(1, 1, 1, 1, 3, 3, 3, 1, 0, 0, 3, 1, 1, 1, 0, 0, 3, 1, 0, 1, 0, 1,
0, 2, 2, 2, 0, 2, 2, 0, 0, 2, 0, 0)
s <- Seurat:::RunModularityClusteringCpp(
SNN = connections,
modularityFunction = 1,
resolution = 1.0,
algorithm = 3,
nRandomStarts = 1,
nIterations = 1,
randomSeed = 56464,
printOutput = 0,
"")
expect_equal(expected, s)
})
test_that("Low Resolution", {
e1 <- rep(0, 34)
# java -jar ModularityOptimizer.jar karate_club_network.txt outjava.txt 1 0.05 3 1 10 10 0
s <- Seurat:::RunModularityClusteringCpp(
SNN = connections,
modularityFunction = 1,
resolution = 0.05,
algorithm = 3,
nRandomStarts = 1,
nIterations = 10,
randomSeed = 10,
printOutput = 0,
""
)
expect_equal(s, e1)
# java -jar ModularityOptimizer.jar karate_club_network.txt outjava.txt 2 0.05 3 1 10 10 0
s2 <- Seurat:::RunModularityClusteringCpp(
SNN = connections,
modularityFunction = 2,
resolution=0.05,
algorithm = 3,
nRandomStarts = 1,
nIterations = 10,
randomSeed = 10,
printOutput = 0,
""
)
e2 = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
expect_equal(s2, e2)
})
test_that("EdgeWeights", {
# Make 1, 4, 5 and 20 a community by weighting them
c2 <- connections
c2[5, 4] <- 3.0
c2[5, 1] <- 5.0
c2[4, 1] <- 8.0
c2[20, 5] <- 8.0
c2[20, 4] <- 5.0
c2[20, 1] <- 5.0
# java -jar ModularityOptimizer.jar weighted_karate_club_network.txt outjava.txt 1 1.0 3 1 10 40 1
s2 <- Seurat:::RunModularityClusteringCpp(
SNN = c2,
modularityFunction = 1,
resolution = 1.0,
algorithm = 3,
nRandomStarts = 1,
nIterations = 10,
randomSeed = 40,
printOutput = 0,
""
)
exp <- c(2, 1, 1, 2, 2, 3, 3, 1, 0, 1, 3, 2, 2, 1, 0, 0, 3, 1, 0, 2, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
expect_equal(s2, exp)
})
# test_that("pbmc_small network", {
# observed <- as.numeric(FindClusters(
# object = pbmc_small,
# reduction.type = "pca",
# dims.use = 1:10,
# resolution = 1.1,
# save.SNN = TRUE,
# print.output = 0)@ident)
# expected = c(1,1,1,1,1,1,1,1,1,1,6,1,6,1,2,2,1,6,2,1,2,2,2,2,2,2,2,2,2,6,3,5,3,3,3,3,3,3,3,3,5,1,1,1,1,1,3,1,3,1,2,1,2,2,6,2,3,2,1,3,5,2,5,5,2,2,2,2,5,3,4,4,4,4,4,4,4,4,4,4)
# expect_equal(observed, expected)
# })
Seurat/tests/testthat/test_load_10X.R 0000644 0001762 0000144 00000002032 13527073365 017260 0 ustar ligges users context("Read10X")
# These tests were added to ensure Seurat was forwards and backwards compatible for 3.0 data
dname = "../testdata/cr3.0"
test.data <- Read10X(dname)
test.data2 <- Read10X(c(dname, dname))
test_that("Cell Ranger 3.0 Data Parsing", {
expect_is(test.data, "list")
expect_equal(ncol(test.data$`Gene Expression`), .5 * ncol(test.data2$`Gene Expression`))
expect_equal(ncol(test.data$`Antibody Capture`), .5 * ncol(test.data2$`Antibody Capture`))
expect_equal(colnames(test.data2[[1]])[6], "2_AAAGTAGCACAGTCGC")
expect_equal(test.data$`Gene Expression`[2,2], 1000)
})
# Tests of Pre-3.0 Data
test.data3 <- Read10X("../testdata/")
test_that("Read10X creates sparse matrix", {
expect_is(test.data3, "dgCMatrix")
expect_equal(colnames(test.data3)[1], "ATGCCAGAACGACT")
expect_equal(rownames(test.data3)[1], "MS4A1")
})
test_that("Read10X handles missing files properly", {
expect_error(Read10X("."))
expect_error(Read10X("./notadir/"))
expect_error(Read10X(dname, gene.column = 10))
})
Seurat/tests/testthat/test_dimensional_reduction.R 0000644 0001762 0000144 00000004220 13602476667 022276 0 ustar ligges users context("test-dimensional_reduction")
test_that("different ways of passing distance matrix", {
# Generate dummy data exp matrix
set.seed(1)
dummyexpMat <- matrix(data = sample(x = c(1:50), size = 1e4, replace = TRUE),
ncol = 100, nrow = 100)
colnames(dummyexpMat) <- paste0("cell", seq(ncol(dummyexpMat)))
row.names(dummyexpMat) <- paste0("gene", seq(nrow(dummyexpMat)))
# Create Seurat object for testing
obj <- CreateSeuratObject(counts = dummyexpMat)
# Manually make a distance object to test
distMat <- dist(t(dummyexpMat))
expect_equivalent(
suppressWarnings(expr = RunTSNE(obj, distance.matrix = distMat)),
suppressWarnings(expr = RunTSNE(obj, distance.matrix = as.matrix(distMat)))
)
expect_equivalent(
suppressWarnings(expr = RunTSNE(obj, distance.matrix = distMat)@reductions$tsne),
suppressWarnings(expr = RunTSNE(distMat, assay = "RNA"))
)
expect_equivalent(
suppressWarnings(expr = RunTSNE(obj, distance.matrix = distMat)@reductions$tsne),
suppressWarnings(expr = RunTSNE(as.matrix(distMat), assay = "RNA", is_distance = TRUE))
)
})
test_that("pca returns total variance (see #982)", {
# Generate dummy data exp matrix
set.seed(seed = 1)
dummyexpMat <- matrix(
data = sample(x = c(1:50), size = 1e4, replace = TRUE),
ncol = 100, nrow = 100
)
colnames(x = dummyexpMat) <- paste0("cell", seq(ncol(x = dummyexpMat)))
row.names(x = dummyexpMat) <- paste0("gene", seq(nrow(x = dummyexpMat)))
# Create Seurat object for testing
obj <- CreateSeuratObject(counts = dummyexpMat)
# Scale and compute PCA, using RunPCA
obj <- ScaleData(object = obj, verbose = FALSE)
pca_result <- suppressWarnings(expr = RunPCA(
object = obj,
features = rownames(x = obj),
verbose = FALSE
))
# Using stats::prcomp
scaled_data <- Seurat::GetAssayData(object = obj, slot = "scale.data")
prcomp_result <- stats::prcomp(scaled_data, center = FALSE, scale. = FALSE)
# Compare
expect_equivalent(slot(object = pca_result[["pca"]], name = "misc")$total.variance,
sum(prcomp_result$sdev^2))
})
Seurat/tests/testdata/ 0000755 0001762 0000144 00000000000 13527073365 014503 5 ustar ligges users Seurat/tests/testdata/cr3.0/ 0000755 0001762 0000144 00000000000 13527073365 015330 5 ustar ligges users Seurat/tests/testdata/cr3.0/barcodes.tsv.gz 0000644 0001762 0000144 00000000127 13527073365 020267 0 ustar ligges users ?Fw[ barcodes.tsv %
./Zx&
DCB }{Ņo]+5j{P`ܡC 0VPh3T:r|V{̤ҧ(.s>?5nY0'2K ? Seurat/tests/testdata/matrix.mtx 0000644 0001762 0000144 00000132302 13527073365 016542 0 ustar ligges users %%MatrixMarket matrix coordinate integer general
240 80 4814
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239 2 1
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Seurat/tests/testdata/barcodes.tsv 0000644 0001762 0000144 00000002640 13527073365 017025 0 ustar ligges users ATGCCAGAACGACT-1
CATGGCCTGTGCAT-1
GAACCTGATGAACC-1
TGACTGGATTCTCA-1
AGTCAGACTGCACA-1
TCTGATACACGTGT-1
TGGTATCTAAACAG-1
GCAGCTCTGTTTCT-1
GATATAACACGCAT-1
AATGTTGACAGTCA-1
AGGTCATGAGTGTC-1
AGAGATGATCTCGC-1
GGGTAACTCTAGTG-1
CATGAGACACGGGA-1
TACGCCACTCCGAA-1
CTAAACCTGTGCAT-1
GTAAGCACTCATTC-1
TTGGTACTGAATCC-1
CATCATACGGAGCA-1
TACATCACGCTAAC-1
TTACCATGAATCGC-1
ATAGGAGAAACAGA-1
GCGCACGACTTTAC-1
ACTCGCACGAAAGT-1
ATTACCTGCCTTAT-1
CCCAACTGCAATCG-1
AAATTCGAATCACG-1
CCATCCGATTCGCC-1
TCCACTCTGAGCTT-1
CATCAGGATGCACA-1
CTAAACCTCTGACA-1
GATAGAGAAGGGTG-1
CTAACGGAACCGAT-1
AGATATACCCGTAA-1
TACTCTGAATCGAC-1
GCGCATCTTGCTCC-1
GTTGACGATATCGG-1
ACAGGTACTGGTGT-1
GGCATATGCTTATC-1
CATTACACCAACTG-1
TAGGGACTGAACTC-1
GCTCCATGAGAAGT-1
TACAATGATGCTAG-1
CTTCATGACCGAAT-1
CTGCCAACAGGAGC-1
TTGCATTGAGCTAC-1
AAGCAAGAGCTTAG-1
CGGCACGAACTCAG-1
GGTGGAGATTACTC-1
GGCCGATGTACTCT-1
CGTAGCCTGTATGC-1
TGAGCTGAATGCTG-1
CCTATAACGAGACG-1
ATAAGTTGGTACGT-1
AAGCGACTTTGACG-1
ACCAGTGAATACCG-1
ATTGCACTTGCTTT-1
CTAGGTGATGGTTG-1
GCACTAGACCTTTA-1
CATGCGCTAGTCAC-1
TTGAGGACTACGCA-1
ATACCACTCTAAGC-1
CATATAGACTAAGC-1
TTTAGCTGTACTCT-1
GACATTCTCCACCT-1
ACGTGATGCCATGA-1
ATTGTAGATTCCCG-1
GATAGAGATCACGA-1
AATGCGTGGACGGA-1
GCGTAAACACGGTT-1
ATTCAGCTCATTGG-1
GGCATATGGGGAGT-1
ATCATCTGACACCA-1
GTCATACTTCGCCT-1
TTACGTACGTTCAG-1
GAGTTGTGGTAGCT-1
GACGCTCTCTCTCG-1
AGTCTTACTTCGGA-1
GGAACACTTCAGAC-1
CTTGATTGATCTTC-1
Seurat/tests/testdata/nbt_small.Rdata 0000644 0001762 0000144 00001330137 13527073365 017443 0 ustar ligges users VŹ?K[Xzea"xTQJP@ v^b5&7hnM175h$&^b)]3Ϝyg̜s]IV9=e)2zf3WV-ZիUVZwټսV-tij5
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