% 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}}

 }