R/findMarker.R
findMarkerDiffExp.Rd
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 = 0.25,
fdrThreshold = 0.05,
minClustExprPerc = 0.6,
maxCtrlExprPerc = 0.4,
minMeanExpr = 0.5
)
SingleCellExperiment inherited object.
character. A string specifying which assay to use for the
MAST calculations. Default "logcounts"
.
A single character for specific differential expression
analysis method. Choose from 'wilcox'
, 'MAST'
, 'DESeq2'
,
'Limma'
, and 'ANOVA'
. Default "wilcox"
.
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"
.
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
.
Only out put DEGs with the absolute values of log2FC
larger than this value. Default NULL
Only out put DEGs with FDR value smaller than this
value. Default 1
A numeric scalar. The minimum cutoff of the
percentage of cells in the cluster of interests that expressed the marker
gene. Default 0.7
.
A numeric scalar. The maximum cutoff of the
percentage of cells out of the cluster (control group) that expressed the
marker gene. Default 0.4
.
A numeric scalar. The minimum cutoff of the mean
expression value of the marker in the cluster of interests. Default 1
.
The input SingleCellExperiment object with
metadata(inSCE)$findMarker
updated with a data.table of the up-
regulated DEGs for each cluster.
data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- findMarkerDiffExp(mouseBrainSubsetSCE,
useAssay = "logcounts",
cluster = "level1class")
#> Running with wilcox
#> Computing for cluster: microglia
#> Computing for cluster: oligodendrocytes