% Generated by roxygen2: do not edit by hand % Please edit documentation in R/runFindMarker.R \name{runFindMarker} \alias{runFindMarker} \alias{findMarkerDiffExp} \title{Find the marker gene set for each cluster} \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{ \item{inSCE}{\linkS4class{SingleCellExperiment} inherited object.} \item{useAssay}{character. A string specifying which assay to use for the MAST calculations. Default \code{"logcounts"}.} \item{useReducedDim}{character. A string specifying which reducedDim to use for MAST calculations. Set \code{useAssay} to \code{NULL} when using. Required.} \item{method}{A single character for specific differential expression analysis method. Choose from \code{'wilcox'}, \code{'MAST'}, \code{'DESeq2'}, \code{'Limma'}, and \code{'ANOVA'}. Default \code{"wilcox"}.} \item{cluster}{One single character to specify a column in \code{colData(inSCE)} for the clustering label. Alternatively, a vector or a factor is also acceptable. Default \code{"cluster"}.} \item{covariates}{A character vector of additional covariates to use when building the model. All covariates must exist in \code{names(colData(inSCE))}. Not applicable when \code{method} is \code{"MAST"} method. Default \code{NULL}.} \item{log2fcThreshold}{Only out put DEGs with the absolute values of log2FC larger than this value. Default \code{NULL}} \item{fdrThreshold}{Only out put DEGs with FDR value smaller than this value. Default \code{NULL}} \item{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 \code{NULL}.} \item{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 \code{NULL}.} \item{minMeanExpr}{A numeric scalar. The minimum cutoff of the mean expression value of the marker in the cluster of interests. Default \code{NULL}.} \item{detectThresh}{A numeric scalar, above which a matrix value will be treated as expressed when calculating cluster/control expression percentage. Default \code{0}.} } \value{ The input \linkS4class{SingleCellExperiment} object with \code{metadata(inSCE)$findMarker} updated with a data.table of the up- regulated DEGs 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. \code{\link{runFindMarker}} will be deprecated in the future. } \details{ The returned marker table, in the \code{metadata} slot, consists of 8 columns: \code{"Gene"}, \code{"Log2_FC"}, \code{"Pvalue"}, \code{"FDR"}, \code{cluster}, \code{"clusterExprPerc"}, \code{"ControlExprPerc"} and \code{"clusterAveExpr"}. \code{"clusterExprPerc"} is the fraction of cells, that has marker value (e.g. gene expression counts) larger than \code{detectThresh}, in the cell population of the cluster. As for each cluster, we set all cells out of this cluster as control. Similarly, \code{"ControlExprPerc"} is the fraction of cells with marker value larger than \code{detectThresh} in the control cell group. } \examples{ data("mouseBrainSubsetSCE", package = "singleCellTK") mouseBrainSubsetSCE <- runFindMarker(mouseBrainSubsetSCE, useAssay = "logcounts", cluster = "level1class") } \seealso{ \code{\link{runDEAnalysis}}, \code{\link{getFindMarkerTopTable}}, \code{\link{plotFindMarkerHeatmap}} }