runFindMarker: Find the marker gene set for each cluster

In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Find the marker gene set for each cluster

Description

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. runFindMarker will be deprecated in the future.

Usage

runFindMarker(
  inSCE,
  useAssay = "logcounts",
  useReducedDim = NULL,
  method = "wilcox",
  cluster = "cluster",
  covariates = NULL,
  log2fcThreshold = NULL,
  fdrThreshold = 0.05,
  minClustExprPerc = NULL,
  maxCtrlExprPerc = NULL,
  minMeanExpr = NULL,
  detectThresh = 0
)

findMarkerDiffExp(
  inSCE,
  useAssay = "logcounts",
  useReducedDim = NULL,
  method = c("wilcox", "MAST", "DESeq2", "Limma", "ANOVA"),
  cluster = "cluster",
  covariates = NULL,
  log2fcThreshold = NULL,
  fdrThreshold = 0.05,
  minClustExprPerc = NULL,
  maxCtrlExprPerc = NULL,
  minMeanExpr = NULL,
  detectThresh = 0
)

Arguments

inSCE	SingleCellExperiment inherited object.
useAssay	character. A string specifying which assay to use for the MAST calculations. Default "logcounts".
useReducedDim	character. A string specifying which reducedDim to use for MAST calculations. Set useAssay to NULL when using. Required.
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.
detectThresh	A numeric scalar, above which a matrix value will be treated as expressed when calculating cluster/control expression percentage. Default 0.

Details

The returned marker table, in the metadata slot, consists of 8 columns: "Gene", "Log2_FC", "Pvalue", "FDR", cluster, "clusterExprPerc", "ControlExprPerc" and "clusterAveExpr".

"clusterExprPerc" is the fraction of cells, that has marker value (e.g. gene expression counts) larger than detectThresh, in the cell population of the cluster. As for each cluster, we set all cells out of this cluster as control. Similarly, "ControlExprPerc" is the fraction of cells with marker value larger than detectThresh in the control cell group.

Value

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

See Also

runDEAnalysis, getFindMarkerTopTable, plotFindMarkerHeatmap

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runFindMarker(mouseBrainSubsetSCE,
                                     useAssay = "logcounts",
                                     cluster = "level1class")

compbiomed/singleCellTK documentation built on Oct. 27, 2024, 3:26 a.m.