Find the marker gene set for each cluster

With an input SingleCellExperiment object and specifying the clustering labels, this function iteratively call the differential expression analysis on each cluster against all the others.

findMarkerDiffExp(
  inSCE,
  useAssay = "logcounts",
  method = c("wilcox", "MAST", "DESeq2", "Limma", "ANOVA"),
  cluster = "cluster",
  covariates = NULL,
  log2fcThreshold = NULL,
  fdrThreshold = 0.05,
  minClustExprPerc = NULL,
  maxCtrlExprPerc = NULL,
  minMeanExpr = NULL
)

Arguments

inSCE

SingleCellExperiment inherited object.

useAssay

character. A string specifying which assay to use for the MAST calculations. Default "logcounts".

method

A single character for specific differential expression analysis method. Choose from 'wilcox', 'MAST', 'DESeq2', 'Limma', and 'ANOVA'. Default "wilcox".

cluster

One single character to specify a column in colData(inSCE) for the clustering label. Alternatively, a vector or a factor is also acceptable. Default "cluster".

covariates

A character vector of additional covariates to use when building the model. All covariates must exist in names(colData(inSCE)). Not applicable when method is "MAST" method. Default NULL.

log2fcThreshold

Only out put DEGs with the absolute values of log2FC larger than this value. Default NULL

fdrThreshold

Only out put DEGs with FDR value smaller than this value. Default NULL

minClustExprPerc

A numeric scalar. The minimum cutoff of the percentage of cells in the cluster of interests that expressed the marker gene. From 0 to 1. Default NULL.

maxCtrlExprPerc

A numeric scalar. The maximum cutoff of the percentage of cells out of the cluster (control group) that expressed the marker gene. From 0 to 1. Default NULL.

minMeanExpr

A numeric scalar. The minimum cutoff of the mean expression value of the marker in the cluster of interests. Default NULL.

Value

The input SingleCellExperiment object with metadata(inSCE)$findMarker updated with a data.table of the up- regulated DEGs for each cluster.

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- findMarkerDiffExp(mouseBrainSubsetSCE,
                                         useAssay = "logcounts",
                                         cluster = "level1class")
#> Wed Jun 29 07:52:38 2022 ... Identifying markers for cluster 'microglia', using DE method 'wilcox'
#> Wed Jun 29 07:52:39 2022 ... Identifying markers for cluster 'oligodendrocytes', using DE method 'wilcox'
#> Wed Jun 29 07:52:40 2022 ... Organizing findMarker result