@@ -19,6 +19,14 @@

 #' larger than this value. Default \code{NULL}

 #' @param fdrThreshold Only out put DEGs with FDR value smaller than this

 #' value. Default \code{1}

+#' @param minClustExprPerc A numeric scalar. The minimum cutoff of the

+#' percentage of cells in the cluster of interests that expressed the marker

+#' gene. Default \code{0.7}.

+#' @param maxCtrlExprPerc A numeric scalar. The maximum cutoff of the

+#' percentage of cells out of the cluster (control group) that expressed the

+#' marker gene. Default \code{0.4}.

+#' @param minMeanExpr A numeric scalar. The minimum cutoff of the mean

+#' expression value of the marker in the cluster of interests. Default \code{1}.

 #' @return The input \linkS4class{SingleCellExperiment} object with

 #' \code{metadata(inSCE)$findMarker} updated with a data.table of the up-

 #' regulated DEGs for each cluster.

@@ -27,7 +35,9 @@

 findMarkerDiffExp <- function(inSCE, useAssay = 'logcounts',

                               method = c('MAST', "DESeq2", "Limma"),

                               cluster = 'cluster', covariates = NULL,

-                              log2fcThreshold = 0.25, fdrThreshold = 0.05){

+                              log2fcThreshold = 0.25, fdrThreshold = 0.05,

+                              minClustExprPerc = 0.6, maxCtrlExprPerc = 0.4,

+                              minMeanExpr = 0.5){

     # Input checks

     if(!inherits(inSCE, "SingleCellExperiment")){

         stop('"inSCE" should be a SingleCellExperiment inherited Object.')

@@ -92,6 +102,50 @@ findMarkerDiffExp <- function(inSCE, useAssay = 'logcounts',

     }

     degFull <- degFull[stats::complete.cases(degFull),]

     attr(degFull, "useAssay") <- useAssay

+    degFull <- .calcMarkerExpr(degFull, inSCE, clusterName,

+                               c(minClustExprPerc, maxCtrlExprPerc,

+                                 minMeanExpr))

     S4Vectors::metadata(inSCE)$findMarker <- degFull

     return(inSCE)

 }

+

+.calcMarkerExpr <- function(markerTable, inSCE, clusterName, params) {

+    genes <- markerTable$Gene

+    cellLabels <- SummarizedExperiment::colData(inSCE)[[clusterName]]

+    useAssay <- attr(markerTable, "useAssay")

+    cells.in.col <- c()

+    cells.out.col <- c()

+    exprs.avg <- c()

+    toRemove <- integer()

+    for (i in seq_len(nrow(markerTable))) {

+        gene <- genes[i]

+        cluster <- markerTable[[clusterName]][i]

+        if (gene %in% rownames(inSCE)) {

+            # Logical indices

+            cells.in <- cellLabels == cluster

+            cells.out <- cellLabels != cluster

+            # cells.in expressed percentage

+            cells.in.expr <- SummarizedExperiment::assay(inSCE, useAssay)[gene, cells.in]

+            cells.in.perc <- sum(cells.in.expr > 0) / length(which(cells.in))

+            # cells.out expressed percentage

+            cells.out.expr <- SummarizedExperiment::assay(inSCE, useAssay)[gene, cells.out]

+            cells.out.perc <- sum(cells.out.expr > 0) / length(which(cells.out))

+            # Average Expression in the cluster

+            cells.in.mean <- mean(cells.in.expr)

+            if (cells.in.perc >= params[1] &&

+                cells.out.perc <= params[2] &&

+                cells.in.mean >= params[3]) {

+                cells.in.col <- c(cells.in.col, cells.in.perc)

+                cells.out.col <- c(cells.out.col, cells.out.perc)

+                exprs.avg <- c(exprs.avg, cells.in.mean)

+            } else {

+                toRemove <- c(toRemove, i)

+            }

+        }

+    }

+    markerTable <- markerTable[-toRemove,]

+    markerTable$clusterExprPerc <- cells.in.col

+    markerTable$ControlExprPerc <- cells.out.col

+    markerTable$clusterAveExpr <- exprs.avg

+    return(markerTable)

+}

@@ -457,8 +457,8 @@ plotMASTThresholdGenes <- function(inSCE, useAssay="logcounts", doPlot = TRUE,

     data_log = isLogged)))

   # plotting

   plotNRow <- ceiling(length(thres$valleys) / 4)

-  thres.grob <- as.grob(function(){

-    par(mfrow = c(plotNRow, 4), mar = c(3, 3, 2, 1),

+  thres.grob <- ggplotify::as.grob(function(){

+    graphics::par(mfrow = c(plotNRow, 4), mar = c(3, 3, 2, 1),

         mgp = c(2, 0.7, 0), tck = -0.01, new = TRUE)

     plot(thres)

   })

@@ -16,6 +16,14 @@

 #' larger than this value. Default \code{1}

 #' @param fdrThreshold Only use DEGs with FDR value smaller than this value.

 #' Default \code{0.05}

+#' @param minClustExprPerc A numeric scalar. The minimum cutoff of the

+#' percentage of cells in the cluster of interests that expressed the marker

+#' gene. Default \code{0.7}.

+#' @param maxCtrlExprPerc A numeric scalar. The maximum cutoff of the

+#' percentage of cells out of the cluster (control group) that expressed the

+#' marker gene. Default \code{0.4}.

+#' @param minMeanExpr A numeric scalar. The minimum cutoff of the mean

+#' expression value of the marker in the cluster of interests. Default \code{1}.

 #' @param topN An integer. Only to plot this number of top markers for each

 #' cluster in maximum, in terms of log2FC value. Use \code{NULL} to cancel the

 #' top N subscription. Default \code{10}.

@@ -56,7 +64,8 @@

 #' @author Yichen Wang

 #' @export

 plotMarkerDiffExp <- function(inSCE, orderBy = 'size',

-    log2fcThreshold = 1, fdrThreshold = 0.05, topN = 10, decreasing = TRUE,

+    log2fcThreshold = 1, fdrThreshold = 0.05, minClustExprPerc = 0.7,

+    maxCtrlExprPerc = 0.4, minMeanExpr = 1, topN = 10, decreasing = TRUE,

     rowDataName = NULL, colDataName = NULL, featureAnnotations = NULL,

     cellAnnotations = NULL, featureAnnotationColor = NULL,

     cellAnnotationColor = NULL,

@@ -101,6 +110,15 @@ plotMarkerDiffExp <- function(inSCE, orderBy = 'size',

     if(!is.null(fdrThreshold)){

         degFull <- degFull[degFull$FDR < fdrThreshold,]

     }

+    if (!is.null(minClustExprPerc)) {

+      degFull <- degFull[degFull$clusterExprPerc >= minClustExprPerc,]

+    }

+    if (!is.null(maxCtrlExprPerc)) {

+      degFull <- degFull[degFull$ControlExprPerc <= maxCtrlExprPerc,]

+    }

+    if (!is.null(minMeanExpr)) {

+      degFull <- degFull[degFull$clusterAveExpr >= minMeanExpr,]

+    }

     # Remove duplicate by assigning the duplicated genes to the cluster where

     # their log2FC is the highest.

     # Done by keeping all unique genes and the rows  with highest Log2FC entry

@@ -14,7 +14,10 @@ findMarkerDiffExp(

   cluster = "cluster",

   covariates = NULL,

   log2fcThreshold = 0.25,

-  fdrThreshold = 0.05

+  fdrThreshold = 0.05,

+  minClustExprPerc = 0.6,

+  maxCtrlExprPerc = 0.4,

+  minMeanExpr = 0.5

 )

 }

 \arguments{

@@ -41,6 +44,17 @@ larger than this value. Default \code{NULL}}

 \item{fdrThreshold}{Only out put DEGs with FDR value smaller than this

 value. Default \code{1}}

+

+\item{minClustExprPerc}{A numeric scalar. The minimum cutoff of the

+percentage of cells in the cluster of interests that expressed the marker

+gene. Default \code{0.7}.}

+

+\item{maxCtrlExprPerc}{A numeric scalar. The maximum cutoff of the

+percentage of cells out of the cluster (control group) that expressed the

+marker gene. Default \code{0.4}.}

+

+\item{minMeanExpr}{A numeric scalar. The minimum cutoff of the mean

+expression value of the marker in the cluster of interests. Default \code{1}.}

 }

 \value{

 The input \linkS4class{SingleCellExperiment} object with

@@ -9,6 +9,9 @@ plotMarkerDiffExp(

   orderBy = "size",

   log2fcThreshold = 1,

   fdrThreshold = 0.05,

+  minClustExprPerc = 0.7,

+  maxCtrlExprPerc = 0.4,

+  minMeanExpr = 1,

   topN = 10,

   decreasing = TRUE,

   rowDataName = NULL,

@@ -36,6 +39,17 @@ larger than this value. Default \code{1}}

 \item{fdrThreshold}{Only use DEGs with FDR value smaller than this value.

 Default \code{0.05}}

+\item{minClustExprPerc}{A numeric scalar. The minimum cutoff of the

+percentage of cells in the cluster of interests that expressed the marker

+gene. Default \code{0.7}.}

+

+\item{maxCtrlExprPerc}{A numeric scalar. The maximum cutoff of the

+percentage of cells out of the cluster (control group) that expressed the

+marker gene. Default \code{0.4}.}

+

+\item{minMeanExpr}{A numeric scalar. The minimum cutoff of the mean

+expression value of the marker in the cluster of interests. Default \code{1}.}

+

 \item{topN}{An integer. Only to plot this number of top markers for each

 cluster in maximum, in terms of log2FC value. Use \code{NULL} to cancel the

 top N subscription. Default \code{10}.}