AUCell_buildRankings: Build gene expression rankings for each cell

In aertslab/AUCell: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures)

Build gene expression rankings for each cell

Description

Builds the "rankings" for each cell: expression-based ranking for all the genes in each cell.

The genes with same expression value are shuffled. Therefore, genes with expression '0' are randomly sorted at the end of the ranking.

These "rankings" can be seen as a new representation of the original dataset. Once they are calculated, they can be saved for future analyses.

Usage

AUCell_buildRankings(
  exprMat,
  featureType = "genes",
  plotStats = TRUE,
  splitByBlocks = FALSE,
  BPPARAM = NULL,
  keepZeroesAsNA = FALSE,
  verbose = TRUE,
  nCores = NULL,
  mctype = NULL,
  ...
)

## S4 method for signature 'dgCMatrix'
AUCell_buildRankings(
  exprMat,
  featureType = "genes",
  plotStats = TRUE,
  splitByBlocks = TRUE,
  BPPARAM = NULL,
  keepZeroesAsNA = FALSE,
  verbose = TRUE,
  nCores = NULL,
  mctype = NULL
)

## S4 method for signature 'matrix'
AUCell_buildRankings(
  exprMat,
  featureType = "genes",
  plotStats = TRUE,
  splitByBlocks = FALSE,
  BPPARAM = NULL,
  keepZeroesAsNA = FALSE,
  verbose = TRUE,
  nCores = NULL,
  mctype = NULL
)

## S4 method for signature 'ExpressionSet'
AUCell_buildRankings(
  exprMat,
  featureType = "genes",
  plotStats = TRUE,
  splitByBlocks = FALSE,
  BPPARAM = NULL,
  keepZeroesAsNA = FALSE,
  verbose = TRUE,
  nCores = NULL,
  mctype = NULL
)

## S4 method for signature 'SummarizedExperiment'
AUCell_buildRankings(
  exprMat,
  featureType = "genes",
  plotStats = TRUE,
  splitByBlocks = FALSE,
  BPPARAM = NULL,
  keepZeroesAsNA = FALSE,
  verbose = TRUE,
  assayName = NULL,
  nCores = NULL,
  mctype = NULL
)

Arguments

exprMat	Expression matrix (genes as rows, cells as columns) The expression matrix can also be provided as one of the R/Bioconductor classes: dgCMatrix-class: Sparse matrix RangedSummarizedExperiment and derived classes (e.g. SingleCellExperiment ): The matrix will be obtained through assay(exprMatrix), -which will extract the first assay (usually the counts)- or the assay name given in 'assayName' ExpressionSet: The matrix will be obtained through exprs(exprMatrix)
featureType	Name for the rows (e.g. "genes"). Only for naming the rankings, not used internally.
plotStats	Should the function plot the expression boxplots/histograms? (TRUE / FALSE). These plots can also be produced with the function plotGeneCount.
splitByBlocks	Whether to split the matrix by blocks in the ranking calculation. Allows using multiple cores. FALSE by default. If using sparse matrices it is automatically set to TRUE.
BPPARAM	Set to use multiple cores. Only used if 'splitByBlocks=TRUE'
keepZeroesAsNA	Convert zeroes to NA instead of locating randomly at the end of the ranking.
verbose	Should the function show progress messages? (TRUE / FALSE)
nCores	Deprecated
mctype	Deprecated
...	Other arguments
assayName	Name of the assay containing the expression matrix (e.g. in SingleCellExperiment objects)

Details

It is important to check that most cells have at least the number of expressed/detected genes that are going to be used to calculate the AUC ('aucMaxRank' in 'calcAUC()'). The histogram provided by 'AUCell_buildRankings()' allows to quickly check this distribution. 'plotGeneCount(exprMatrix)' allows to obtain only the plot before building the rankings.

Value

Ranking of the feature within the cell (features as rows, cells as columns)

See Also

Next step in the workflow: AUCell_calcAUC.

See the package vignette for examples and more details: vignette("AUCell")

Examples

# This example is run using a fake expression matrix.
# Therefore, the output will be meaningless.

############# Fake expression matrix #############
set.seed(123)
exprMatrix <- matrix(data=sample(c(rep(0, 5000), sample(1:3, 5000, replace=TRUE))),
                     nrow=20, 
                     dimnames=list(paste("Gene", 1:20, sep=""), 
                                   paste("Cell", 1:500, sep="")))
##################################################

cells_rankings <- AUCell_buildRankings(exprMatrix, plotStats=TRUE)
cells_rankings

aertslab/AUCell documentation built on March 12, 2024, 11:40 p.m.