R/plotDEAnalysis.R
In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
Defines functions
plotMASTThresholdGenes
plotDEGVolcano
plotDEGHeatmap
getDEGTopTable
plotDEGRegression
plotDEGViolin
.checkDiffExpResultExists
Documented in
getDEGTopTable
plotDEGHeatmap
plotDEGRegression
plotDEGViolin
plotDEGVolcano
plotMASTThresholdGenes
#' Check if the specified MAST result in SingleCellExperiment object is
#' complete. But does not garantee the biological correctness.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object. a
#' differential expression analysis function has to be run in advance.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Default \code{NULL}.
#' @return Stop point if found
#' @noRd
.checkDiffExpResultExists <- function(inSCE, useResult, labelBy = NULL){
if(!inherits(inSCE, 'SingleCellExperiment')){
stop('Given object is not a valid SingleCellExperiment object.')
}
if(!'diffExp' %in% names(S4Vectors::metadata(inSCE))){
stop('"diffExp" not in metadata, please run runMAST() first.')
}
if(!useResult %in% names(S4Vectors::metadata(inSCE)$diffExp)){
p <- paste0('"', useResult, '"', ' not in metadata(inSCE)$diffExp. ')
stop(p,
'Please check.')
}
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
if(!all(c('groupNames', 'select', 'result', 'useAssay') %in% names(result))){
p <- paste0('"', useResult, '"', ' result is not complete. ')
stop(p,
'You might need to rerun it.')
}
if(!is.null(labelBy)){
if(!labelBy %in% names(SummarizedExperiment::rowData(inSCE))){
stop("labelBy: '", labelBy, "' not found.")
}
}
}
#' Generate violin plot to show the expression of top DEGs
#' @details Any of the differential expression analysis method from SCTK should
#' be performed prior to using this function
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param threshP logical. Whether to plot threshold values from adaptive
#' thresholding, instead of using the assay used by \code{runMAST()}. Default
#' \code{FALSE}.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Default \code{NULL}.
#' @param nrow Integer. Number of rows in the plot grid. Default \code{6}.
#' @param ncol Integer. Number of columns in the plot grid. Default \code{6}.
#' @param defaultTheme Logical scalar. Whether to use default SCTK theme in
#' ggplot. Default \code{TRUE}.
#' @param isLogged Logical scalar. Whether the assay used for the analysis is
#' logged. If not, will do a \code{log(assay + 1)} transformation. Default
#' \code{TRUE}.
#' @param check_sanity Logical scalar. Whether to perform MAST's sanity check
#' to see if the counts are logged. Default \code{TRUE}
#' @return A ggplot object of violin plot
#' @export
#' @examples
#' data("sceBatches")
#' logcounts(sceBatches) <- log1p(counts(sceBatches))
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGViolin(sce.w, "w.aVSb")
plotDEGViolin <- function(inSCE, useResult, threshP = FALSE, labelBy = NULL,
nrow = 6, ncol = 6, defaultTheme = TRUE,
isLogged = TRUE, check_sanity = TRUE){
#TODO: DO we split the up/down regulation too?
# Check
.checkDiffExpResultExists(inSCE, useResult, labelBy)
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
deg <- result$result
useAssay <- result$useAssay
useReducedDim <- result$useReducedDim
deg <- deg[order(deg$FDR),]
geneToPlot <- deg[seq_len(min(nrow(deg), nrow*ncol)), "Gene"]
groupName1 <- result$groupNames[1]
ix1 <- result$select$ix1
cells1 <- colnames(inSCE)[ix1]
groupName2 <- result$groupNames[2]
ix2 <- result$select$ix2
cells2 <- colnames(inSCE)[ix2]
if (!is.null(useAssay)) {
if(!is.null(labelBy)){
replGeneName <- SummarizedExperiment::rowData(inSCE[geneToPlot,])[[labelBy]]
} else {
replGeneName <- geneToPlot
}
expres <- expData(inSCE[geneToPlot, c(cells1, cells2)],
useAssay)
useMat <- useAssay
} else {
if(!is.null(labelBy)){
warning("Analysis performed on reducedDim. Cannot use rowData for ",
"labelBy. Ignored.")
}
replGeneName <- geneToPlot
expres <- t(expData(inSCE[, c(cells1, cells2)], useReducedDim))[geneToPlot,]
useMat <- useReducedDim
}
if(!is.matrix(expres)){
expres <- as.matrix(expres)
}
rownames(expres) <- replGeneName
# Format
cdat <- data.frame(wellKey = colnames(expres),
condition = factor(c(rep(groupName1, length(cells1)),
rep(groupName2, length(cells2))),
levels = result$groupNames),
ngeneson = rep("", (length(cells1) + length(cells2))),
stringsAsFactors = FALSE)
if (!isTRUE(isLogged)) {
expres <- log(expres + 1)
}
sca <- suppressMessages(MAST::FromMatrix(expres, cdat,
check_sanity = check_sanity))
if(threshP){
#TODO: if nrow*ncol < `min_per_bin`` below, there would be an error.
invisible(utils::capture.output(thres <-
MAST::thresholdSCRNACountMatrix(expres, nbins = 20,
min_per_bin = 30)))
SummarizedExperiment::assay(sca) <- thres$counts_threshold
}
flatDat <- methods::as(sca, "data.table")
flatDat$primerid <- factor(flatDat$primerid, levels = replGeneName)
names(flatDat)[5] <- useMat
# Plot
violinplot <- ggplot2::ggplot(flatDat,
ggplot2::aes_string(x = 'condition',
y = useMat,
color = 'condition')) +
ggplot2::geom_jitter() +
ggplot2::facet_wrap(~primerid, scale = "free_y",
ncol = ncol) +
ggplot2::geom_violin() +
ggplot2::ggtitle(paste0("Violin Plot for ", useResult))
if(isTRUE(defaultTheme)){
violinplot <- .ggSCTKTheme(violinplot)
}
return(violinplot)
}
#' Create linear regression plot to show the expression the of top DEGs
#' @details Any of the differential expression analysis method from SCTK should
#' be performed prior to using this function
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param threshP logical. Whether to plot threshold values from adaptive
#' thresholding, instead of using the assay used by when performing DE analysis.
#' Default \code{FALSE}.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Default \code{NULL}.
#' @param nrow Integer. Number of rows in the plot grid. Default \code{6}.
#' @param ncol Integer. Number of columns in the plot grid. Default \code{6}.
#' @param defaultTheme Logical scalar. Whether to use default SCTK theme in
#' ggplot. Default \code{TRUE}.
#' @param isLogged Logical scalar. Whether the assay used for the analysis is
#' logged. If not, will do a \code{log(assay + 1)} transformation. Default
#' \code{TRUE}.
#' @param check_sanity Logical scalar. Whether to perform MAST's sanity check
#' to see if the counts are logged. Default \code{TRUE}
#' @return A ggplot object of linear regression
#' @export
#' @examples
#' data("sceBatches")
#' logcounts(sceBatches) <- log1p(counts(sceBatches))
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGRegression(sce.w, "w.aVSb")
plotDEGRegression <- function(inSCE, useResult, threshP = FALSE, labelBy = NULL,
nrow = 6, ncol = 6, defaultTheme = TRUE,
isLogged = TRUE, check_sanity = TRUE){
#TODO: DO we split the up/down regulation too?
# Check
.checkDiffExpResultExists(inSCE, useResult, labelBy)
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
deg <- result$result
useReducedDim <- result$useReducedDim
useAssay <- result$useAssay
geneToPlot <- deg[seq_len(min(nrow(deg), nrow*ncol)), "Gene"]
groupName1 <- result$groupNames[1]
ix1 <- result$select$ix1
cells1 <- colnames(inSCE)[ix1]
groupName2 <- result$groupNames[2]
ix2 <- result$select$ix2
cells2 <- colnames(inSCE)[ix2]
if (!is.null(useAssay)) {
if(!is.null(labelBy)){
replGeneName <- SummarizedExperiment::rowData(inSCE[geneToPlot,])[[labelBy]]
} else {
replGeneName <- geneToPlot
}
expres <- expData(inSCE[geneToPlot, c(cells1, cells2)],
useAssay)
useMat <- useAssay
} else {
if (!is.null(labelBy)) {
warning("Analysis performed on reducedDim. Cannot use rowData for ",
"labelBy. Ignored.")
}
replGeneName <- geneToPlot
expres <- t(expData(inSCE[,c(cells1, cells2)], useReducedDim))[geneToPlot,]
useMat <- useReducedDim
}
if(!is.matrix(expres)){
expres <- as.matrix(expres)
}
rownames(expres) <- replGeneName
# Format
cdat <- data.frame(wellKey = colnames(expres),
condition = factor(c(rep(groupName1, length(cells1)),
rep(groupName2, length(cells2))),
levels = result$groupNames),
ngeneson = rep("", (length(cells1) + length(cells2))),
stringsAsFactors = FALSE)
if (!isTRUE(isLogged)) {
expres <- log(expres + 1)
}
sca <- suppressMessages(MAST::FromMatrix(expres, cData = cdat,
check_sanity = check_sanity))
cdr2 <- colSums(SummarizedExperiment::assay(sca) > 0)
SummarizedExperiment::colData(sca)$cngeneson <- scale(cdr2)
if(length(unique(SummarizedExperiment::colData(sca)$cngeneson)) < 2){
stop("Standardized cellular detection rate not various, unable to plot.")
}
if(threshP){
#TODO: if nrow*ncol < `min_per_bin`` below, there would be an error.
invisible(utils::capture.output(thres <-
MAST::thresholdSCRNACountMatrix(expres,
nbins = 20,
min_per_bin = 30)))
SummarizedExperiment::assay(sca) <- thres$counts_threshold
}
flatDat <- methods::as(sca, "data.table")
flatDat$primerid <- factor(flatDat$primerid, levels = replGeneName)
names(flatDat)[6] <- useMat
# Calculate
resData <- NULL
for (i in unique(flatDat$primerid)){
resdf <- flatDat[flatDat$primerid == i, ]
resdf$lmPred <- stats::lm(
stats::as.formula(paste0(useMat, "~cngeneson+", 'condition')),
data = flatDat[flatDat$primerid == i, ])$fitted
if (is.null(resData)){
resData <- resdf
} else {
resData <- rbind(resData, resdf)
}
}
# Plot
ggbase <- ggplot2::ggplot(resData, ggplot2::aes_string(
x = 'condition',
y = useMat,
color = 'condition')) +
ggplot2::geom_jitter() +
ggplot2::facet_wrap(~primerid, scale = "free_y",
ncol = ncol)
regressionplot <- ggbase +
ggplot2::aes_string(x = "cngeneson") +
ggplot2::geom_line(ggplot2::aes_string(y = "lmPred"),
lty = 1) +
ggplot2::xlab("Standardized Cellular Detection Rate") +
ggplot2::ggtitle(paste0("Linear Model Plot for ",
useResult))
if(isTRUE(defaultTheme)){
regressionplot <- .ggSCTKTheme(regressionplot)
}
return(regressionplot)
}
#' Get Top Table of a DEG analysis
#' @description Users have to run \code{runDEAnalysis()} first, any of the
#' wrapped functions of this generic function. Users can set further filters on
#' the result. A \code{data.frame} object, with variables of \code{Gene},
#' \code{Log2_FC}, \code{Pvalue}, and \code{FDR}, will be returned.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object, with of the
#' singleCellTK DEG method performed in advance.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Leave \code{NULL} for \code{rownames}.
#' Default \code{metadata(inSCE)$featureDisplay} (see
#' \code{\link{setSCTKDisplayRow}}).
#' @param onlyPos logical. Whether to only fetch DEG with positive log2_FC
#' value. Default \code{FALSE}.
#' @param log2fcThreshold numeric. Only fetch DEGs with the absolute values of
#' log2FC larger than this value. Default \code{0.25}.
#' @param fdrThreshold numeric. Only fetch DEGs with FDR value smaller than this
#' value. Default \code{0.05}.
#' @param minGroup1MeanExp numeric. Only fetch DEGs with mean expression in
#' group1 greater then this value. Default \code{NULL}.
#' @param maxGroup2MeanExp numeric. Only fetch DEGs with mean expression in
#' group2 less then this value. Default \code{NULL}.
#' @param minGroup1ExprPerc numeric. Only fetch DEGs expressed in greater then
#' this fraction of cells in group1. Default \code{NULL}.
#' @param maxGroup2ExprPerc numeric. Only fetch DEGs expressed in less then this
#' fraction of cells in group2. Default \code{NULL}.
#' @return A \code{data.frame} object of the top DEGs, with variables of
#' \code{Gene}, \code{Log2_FC}, \code{Pvalue}, and \code{FDR}.
#' @export
#' @examples
#' data("sceBatches")
#' sceBatches <- scaterlogNormCounts(sceBatches, "logcounts")
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' getDEGTopTable(sce.w, "w.aVSb")
getDEGTopTable <- function(inSCE, useResult,
labelBy = S4Vectors::metadata(inSCE)$featureDisplay,
onlyPos = FALSE, log2fcThreshold = 0.25,
fdrThreshold = 0.05, minGroup1MeanExp = NULL,
maxGroup2MeanExp = NULL, minGroup1ExprPerc = NULL,
maxGroup2ExprPerc = NULL){
# Check
.checkDiffExpResultExists(inSCE, useResult, labelBy)
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]$result
if (!is.null(labelBy)) {
genes <- result$Gene
geneIdx <- featureIndex(genes, inSCE)
result$Gene <- SummarizedExperiment::rowData(inSCE)[[labelBy]][geneIdx]
}
# Filter
result <- .filterDETable(result, onlyPos, log2fcThreshold, fdrThreshold,
minGroup1MeanExp, maxGroup2MeanExp,
minGroup1ExprPerc, maxGroup2ExprPerc)
return(result)
}
#' Heatmap visualization of DEG result
#'
#' @details A differential expression analysis function has to be run in advance
#' so that information is stored in the metadata of the input SCE object. This
#' function wraps \code{\link{plotSCEHeatmap}}.
#' A feature annotation basing on the log2FC level called \code{"regulation"}
#' will be automatically added. A cell annotation basing on the condition
#' selection while running the analysis called \code{"condition"}, and the
#' annotations used from \code{colData(inSCE)} while setting the condition and
#' covariates will also be added.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param doLog Logical scalar. Whether to do \code{log(assay + 1)}
#' transformation on the assay used for the analysis. Default \code{FALSE}.
#' @param onlyPos logical. Whether to only plot DEG with positive log2_FC
#' value. Default \code{FALSE}.
#' @param log2fcThreshold numeric. Only plot DEGs with the absolute values of
#' log2FC larger than this value. Default \code{0.25}.
#' @param fdrThreshold numeric. Only plot DEGs with FDR value smaller than this
#' value. Default \code{0.05}.
#' @param minGroup1MeanExp numeric. Only plot DEGs with mean expression in
#' group1 greater then this value. Default \code{NULL}.
#' @param maxGroup2MeanExp numeric. Only plot DEGs with mean expression in
#' group2 less then this value. Default \code{NULL}.
#' @param minGroup1ExprPerc numeric. Only plot DEGs expressed in greater then
#' this fraction of cells in group1. Default \code{NULL}.
#' @param maxGroup2ExprPerc numeric. Only plot DEGs expressed in less then this
#' fraction of cells in group2. Default \code{NULL}.
#' @param useAssay character. A string specifying an assay of expression value
#' to plot. By default the assay used for \code{runMAST()} will be used.
#' Default \code{NULL}.
#' @param featureAnnotations \code{data.frame}, with \code{rownames} containing
#' all the features going to be plotted. Character columns should be factors.
#' Default \code{NULL}.
#' @param cellAnnotations \code{data.frame}, with \code{rownames} containing
#' all the cells going to be plotted. Character columns should be factors.
#' Default \code{NULL}.
#' @param featureAnnotationColor A named list. Customized color settings for
#' feature labeling. Should match the entries in the \code{featureAnnotations}
#' or \code{rowDataName}. For each entry, there should be a list/vector of
#' colors named with categories. Default \code{NULL}.
#' @param cellAnnotationColor A named list. Customized color settings for
#' cell labeling. Should match the entries in the \code{cellAnnotations} or
#' \code{colDataName}. For each entry, there should be a list/vector of colors
#' named with categories. Default \code{NULL}.
#' @param rowDataName character. The column name(s) in \code{rowData} that need
#' to be added to the annotation. Default \code{NULL}.
#' @param colDataName character. The column name(s) in \code{colData} that need
#' to be added to the annotation. Default \code{NULL}.
#' @param rowSplitBy character. Do semi-heatmap based on the grouping of
#' this(these) annotation(s). Should exist in either \code{rowDataName} or
#' \code{names(featureAnnotations)}. Default \code{"regulation"}.
#' @param colSplitBy character. Do semi-heatmap based on the grouping of
#' this(these) annotation(s). Should exist in either \code{colDataName} or
#' \code{names(cellAnnotations)}. Default \code{"condition"}.
#' @param rowLabel \code{FALSE} for not displaying; a variable in \code{rowData}
#' to display feature identifiers stored there; if have run
#' \code{\link{setSCTKDisplayRow}}, display the specified feature name;
#' \code{TRUE} for the \code{rownames} of \code{inSCE}; \code{NULL} for
#' auto-display \code{rownames} when the number of filtered feature is less
#' than 60. Default looks for \code{\link{setSCTKDisplayRow}} information.
#' @param title character. Main title of the heatmap. Default
#' \code{"DE Analysis: <useResult>"}.
#' @param ... Other arguments passed to \code{\link{plotSCEHeatmap}}
#' @examples
#' data("sceBatches")
#' logcounts(sceBatches) <- log1p(counts(sceBatches))
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGHeatmap(sce.w, "w.aVSb")
#' @return A \code{\link[ggplot2]{ggplot}} object
#' @export
#' @author Yichen Wang
plotDEGHeatmap <- function(inSCE, useResult, onlyPos = FALSE,
log2fcThreshold = 0.25, fdrThreshold = 0.05,
minGroup1MeanExp = NULL, maxGroup2MeanExp = NULL,
minGroup1ExprPerc = NULL, maxGroup2ExprPerc = NULL,
useAssay = NULL, doLog = FALSE,
featureAnnotations = NULL,
cellAnnotations = NULL,
featureAnnotationColor = NULL,
cellAnnotationColor = NULL,
rowDataName = NULL, colDataName = NULL,
colSplitBy = 'condition', rowSplitBy = 'regulation',
rowLabel = S4Vectors::metadata(inSCE)$featureDisplay,
title = paste0("DE Analysis: ", useResult), ...){
# Check
.checkDiffExpResultExists(inSCE, useResult)
extraArgs <- list(...)
warnArgs <- c('featureIndex', 'cellIndex')
if(any(warnArgs %in% names(extraArgs))){
warning('"', paste(warnArgs[warnArgs %in% names(extraArgs)],
collapse = ', '), '" are not allowed at this point.')
extraArgs[c('cellIndex', 'featureIndex')] <- NULL
}
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
if (!is.null(result$useReducedDim)) {
# Analysis performed with reducedDim, cannot set useAssay in plotDEGHeatmap
if (!is.null(useAssay)) {
warning("Analysis performed on reducedDim, cannot set `useAssay`, ",
"ignored.")
}
useAssay <- NULL
useReducedDim <- result$useReducedDim
} else {
if(is.null(useAssay)){
useAssay <- result$useAssay
}
useReducedDim <- NULL
}
ix1 <- result$select$ix1
ix2 <- result$select$ix2
deg.filtered <- getDEGTopTable(inSCE, useResult = useResult, labelBy = NULL,
onlyPos = onlyPos,
log2fcThreshold = log2fcThreshold,
fdrThreshold = fdrThreshold,
minGroup1MeanExp, maxGroup2MeanExp,
minGroup1ExprPerc, maxGroup2ExprPerc)
if(dim(deg.filtered)[1] <= 1){
stop('Too few genes that pass filtration, unable to plot')
}
# Not directly using deg.filtered$Gene because rownames might be deduplicated
# to avoid error when performing DEG.
if (!is.null(useReducedDim)) {
mat <- t(expData(inSCE, useReducedDim))
assayList <- list(mat)
names(assayList) <- useReducedDim
tmpSCE <- SingleCellExperiment::SingleCellExperiment(assays = assayList)
SummarizedExperiment::colData(tmpSCE) <- SummarizedExperiment::colData(inSCE)
assayName <- useReducedDim
} else {
tmpSCE <- inSCE
assayName <- useAssay
}
gene.ix <- rownames(tmpSCE) %in% deg.filtered$Gene
cell.ix <- which(ix1 | ix2)
allGenes <- rownames(tmpSCE)[gene.ix]
allCells <- colnames(tmpSCE)[cell.ix]
# Annotation organization
## Cells
group <- rep(NA, ncol(tmpSCE))
group[ix1] <- result$groupNames[1]
group[ix2] <- result$groupNames[2]
group <- factor(group[cell.ix], levels = result$groupNames)
if(!is.null(cellAnnotations)){
if(!all(allCells %in% rownames(cellAnnotations))){
stop('Not all cells involved in comparison found in given ',
'`cellAnnotations`. ')
}
cellAnnotations <- cellAnnotations[allCells, , drop = FALSE]
cellAnnotations <- data.frame(cellAnnotations, condition = group)
} else {
cellAnnotations <- data.frame(condition = group,
row.names = allCells)
}
kCol <- celda::distinctColors(2)
names(kCol) <- result$groupNames
if(!is.null(cellAnnotationColor)){
if(!"condition" %in% names(cellAnnotationColor)){
cellAnnotationColor <- c(list(condition = kCol),
cellAnnotationColor)
}
} else {
cellAnnotationColor <- list(condition = kCol)
}
if(!is.null(colDataName)){
if (length(which(colDataName %in% result$annotation)) > 0) {
colDataName <- colDataName[-which(colDataName %in% result$annotation)]
}
colDataName <- c(colDataName, result$annotation)
} else {
colDataName <- result$annotation
}
## Genes
regulation <- vector()
genes.up <- deg.filtered[deg.filtered$Log2_FC > 0, "Gene"]
genes.down <- deg.filtered[deg.filtered$Log2_FC < 0, "Gene"]
regulation[rownames(tmpSCE) %in% genes.up] <- 'up'
regulation[rownames(tmpSCE) %in% genes.down] <- 'down'
regulation <- factor(regulation[gene.ix], levels = c('up', 'down'))
if(!is.null(featureAnnotations)){
if(!all(allGenes %in% rownames(featureAnnotations))){
stop('Not all genes involved in comparison found in given ',
'`featureAnnotations`. ')
}
featureAnnotations <- featureAnnotations[allGenes, , drop = FALSE]
featureAnnotations <- data.frame(featureAnnotations,
regulation = regulation)
} else {
featureAnnotations <- data.frame(regulation = regulation,
row.names = allGenes)
}
lCol <- celda::distinctColors(2)
names(lCol) <- c('up', 'down')
if(!is.null(featureAnnotationColor)){
if(!"regulation" %in% names(featureAnnotationColor)){
featureAnnotationColor <- c(list(regulation = lCol),
featureAnnotationColor)
}
} else {
featureAnnotationColor <- list(regulation = lCol)
}
if (is.null(rowLabel) & nrow(deg.filtered) < 60) {
rowLabel <- TRUE
}
# Plot
hm <- plotSCEHeatmap(inSCE = tmpSCE, useAssay = assayName, doLog = doLog,
featureIndex = gene.ix, cellIndex = cell.ix,
featureAnnotations = featureAnnotations,
cellAnnotations = cellAnnotations,
rowDataName = rowDataName,
colDataName = colDataName,
featureAnnotationColor = featureAnnotationColor,
cellAnnotationColor = cellAnnotationColor,
rowSplitBy = rowSplitBy, colSplitBy = colSplitBy,
title = title, rowLabel = rowLabel, ...)
return(hm)
}
#' Generate volcano plot for DEGs
#' @details Any of the differential expression analysis method from SCTK should
#' be performed prior to using this function to generate volcano plots.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param labelTopN Integer, label this number of top DEGs that pass the
#' filters. \code{FALSE} for not labeling. Default \code{10}.
#' @param log2fcThreshold numeric. Label genes with the absolute values of
#' log2FC greater than this value as regulated. Default \code{0.25}.
#' @param fdrThreshold numeric. Label genes with FDR value less than this
#' value as regulated. Default \code{0.05}.
#' @param featureDisplay A character string to indicate a variable in
#' \code{rowData(inSCE)} for feature labeling. \code{NULL} for using
#' \code{rownames}. Default \code{metadata(inSCE)$featureDisplay} (see
#' \code{\link{setSCTKDisplayRow}})
#' @return A \code{ggplot} object of volcano plot
#' @seealso \code{\link{runDEAnalysis}}, \code{\link{plotDEGHeatmap}}
#' @export
#' @examples
#' data("sceBatches")
#' sceBatches <- scaterlogNormCounts(sceBatches, "logcounts")
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGVolcano(sce.w, "w.aVSb")
plotDEGVolcano <- function(
inSCE,
useResult,
labelTopN = 10,
log2fcThreshold = 0.25,
fdrThreshold = 0.05,
featureDisplay = S4Vectors::metadata(inSCE)$featureDisplay
) {
.checkDiffExpResultExists(inSCE, useResult, featureDisplay)
deg <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]$result
deg <- deg[order(abs(deg$Log2_FC), decreasing = TRUE),]
if (!is.null(featureDisplay)) {
geneIdx <- featureIndex(deg$Gene, inSCE)
deg$Gene <- SummarizedExperiment::rowData(inSCE)[[featureDisplay]][geneIdx]
}
rownames(deg) <- deg$Gene
groupNames <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]$groupNames
# Prepare for coloring that shows the filtering
deg$Regulation <- NA
deg$Regulation[deg$Log2_FC > 0] <- "Up"
deg$Regulation[deg$Log2_FC < 0] <- "Down"
if (!is.null(log2fcThreshold)) {
deg$Regulation[abs(deg$Log2_FC) <= log2fcThreshold] <- "No"
}
if (!is.null(fdrThreshold)) {
deg$Regulation[deg$FDR >= fdrThreshold] <- "No"
}
# Prepare for Top DEG text labeling
passIdx <- deg$Regulation != "No"
deg$label <- NA
if (!is.null(labelTopN) & !isFALSE(labelTopN)) {
labelTopN <- min(labelTopN, length(which(passIdx)))
if (labelTopN > 0) {
deg.pass <- deg[passIdx,]
label.origTable.idx <- deg$Gene %in% deg.pass$Gene[seq(labelTopN)]
deg$label[label.origTable.idx] <- deg$Gene[label.origTable.idx]
}
}
# Prepare for lines that mark the cutoffs
vlineLab <- data.frame(
X = c(-log2fcThreshold, log2fcThreshold),
text = c(paste("lower log2FC cutoff:", -log2fcThreshold),
paste("upper log2FC cutoff:", log2fcThreshold)),
h = c(1.01, -0.01)
)
hlineLab <- data.frame(
Y = c(-log10(fdrThreshold)),
text = paste("FDR cutoff:", fdrThreshold)
)
# Plot
ggplot2::ggplot() +
ggplot2::geom_point(data = deg,
ggplot2::aes_string(x = "Log2_FC", y = "-log10(FDR)",
col = "Regulation")) +
ggplot2::scale_color_manual(values = c("Down" = "#619cff",
"No" = "light grey",
"Up" = "#f8766d")) +
ggrepel::geom_text_repel(data = deg,
ggplot2::aes_string(x = "Log2_FC",
y = "-log10(FDR)",
label = "label"),
colour = "black", na.rm = TRUE) +
ggplot2::geom_vline(data = vlineLab,
ggplot2::aes_string(xintercept = "X"),
linetype = "longdash") +
ggplot2::geom_text(data = vlineLab,
ggplot2::aes_string(x = "X", y = 0, label = "text",
hjust = "h"),
size = 3, vjust = 1) +
ggplot2::geom_hline(data = hlineLab,
ggplot2::aes_string(yintercept = "Y"),
linetype = "longdash") +
ggplot2::geom_text(data = hlineLab,
ggplot2::aes_string(x = -Inf, y = "Y", label = "text"),
size = 3, vjust = -0.5, hjust = -.03) +
ggplot2::xlab("Fold Change (log2)") +
ggplot2::ylab("FDR (-Log10 q-value)") +
ggplot2::theme(panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
axis.line = ggplot2::element_line(colour = "black")) +
ggplot2::xlim(-max(abs(deg$Log2_FC)), max(abs(deg$Log2_FC))) +
ggplot2::ggtitle(paste("DEG between", groupNames[1],
"and", groupNames[2]))
}
#' MAST Identify adaptive thresholds
#'
#' Calculate and produce a list of thresholded counts (on natural scale),
#' thresholds, bins, densities estimated on each bin, and the original data from
#' \code{\link[MAST]{thresholdSCRNACountMatrix}}
#' @param inSCE SingleCellExperiment object
#' @param useAssay character, default \code{"logcounts"}
#' @param doPlot Logical scalar. Whether to directly plot in the plotting area.
#' If \code{FALSE}, will return a graphical object which can be visualized with
#' \code{grid.draw()}. Default \code{TRUE}.
#' @param isLogged Logical scalar. Whether the assay used for the analysis is
#' logged. If not, will do a \code{log(assay + 1)} transformation. Default
#' \code{TRUE}.
#' @param check_sanity Logical scalar. Whether to perform MAST's sanity check
#' to see if the counts are logged. Default \code{TRUE}
#' @return Plot the thresholding onto the plotting region if \code{plot == TRUE}
#' or a graphical object if \code{plot == FALSE}.
#' @export
#' @examples
#' data("mouseBrainSubsetSCE")
#' plotMASTThresholdGenes(mouseBrainSubsetSCE)
plotMASTThresholdGenes <- function(inSCE, useAssay="logcounts", doPlot = TRUE,
isLogged = TRUE, check_sanity = TRUE){
# data preparation
expres <- expData(inSCE, useAssay)
if(!is.matrix(expres)){
expres <- as.matrix(expres)
}
expres <- dedupRowNames(expres)
fdata <- data.frame(Gene = rownames(expres))
rownames(fdata) <- fdata$Gene
SCENew <- MAST::FromMatrix(expres, SingleCellExperiment::colData(inSCE),
fdata, check_sanity = check_sanity)
SCENew <- SCENew[which(MAST::freq(SCENew) > 0), ]
invisible(utils::capture.output(thres <- MAST::thresholdSCRNACountMatrix(
SummarizedExperiment::assay(SCENew), nbins = 20, min_per_bin = 30,
data_log = isLogged)))
# plotting
plotNRow <- ceiling(length(thres$valleys) / 4)
thres.grob <- ggplotify::as.grob(function(){
graphics::par(mfrow = c(plotNRow, 4), mar = c(3, 3, 2, 1),
mgp = c(2, 0.7, 0), tck = -0.01, new = TRUE)
plot(thres)
})
if (isTRUE(doPlot)) {
grid::grid.draw(thres.grob)
} else {
return(thres.grob)
}
}
compbiomed/singleCellTK documentation built on May 8, 2024, 6:58 p.m.
R Package Documentation
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Defines functions plotMASTThresholdGenes plotDEGVolcano plotDEGHeatmap getDEGTopTable plotDEGRegression plotDEGViolin .checkDiffExpResultExists
Documented in getDEGTopTable plotDEGHeatmap plotDEGRegression plotDEGViolin plotDEGVolcano plotMASTThresholdGenes
#' Check if the specified MAST result in SingleCellExperiment object is
#' complete. But does not garantee the biological correctness.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object. a
#' differential expression analysis function has to be run in advance.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Default \code{NULL}.
#' @return Stop point if found
#' @noRd
.checkDiffExpResultExists <- function(inSCE, useResult, labelBy = NULL){
if(!inherits(inSCE, 'SingleCellExperiment')){
stop('Given object is not a valid SingleCellExperiment object.')
}
if(!'diffExp' %in% names(S4Vectors::metadata(inSCE))){
stop('"diffExp" not in metadata, please run runMAST() first.')
}
if(!useResult %in% names(S4Vectors::metadata(inSCE)$diffExp)){
p <- paste0('"', useResult, '"', ' not in metadata(inSCE)$diffExp. ')
stop(p,
'Please check.')
}
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
if(!all(c('groupNames', 'select', 'result', 'useAssay') %in% names(result))){
p <- paste0('"', useResult, '"', ' result is not complete. ')
stop(p,
'You might need to rerun it.')
}
if(!is.null(labelBy)){
if(!labelBy %in% names(SummarizedExperiment::rowData(inSCE))){
stop("labelBy: '", labelBy, "' not found.")
}
}
}
#' Generate violin plot to show the expression of top DEGs
#' @details Any of the differential expression analysis method from SCTK should
#' be performed prior to using this function
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param threshP logical. Whether to plot threshold values from adaptive
#' thresholding, instead of using the assay used by \code{runMAST()}. Default
#' \code{FALSE}.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Default \code{NULL}.
#' @param nrow Integer. Number of rows in the plot grid. Default \code{6}.
#' @param ncol Integer. Number of columns in the plot grid. Default \code{6}.
#' @param defaultTheme Logical scalar. Whether to use default SCTK theme in
#' ggplot. Default \code{TRUE}.
#' @param isLogged Logical scalar. Whether the assay used for the analysis is
#' logged. If not, will do a \code{log(assay + 1)} transformation. Default
#' \code{TRUE}.
#' @param check_sanity Logical scalar. Whether to perform MAST's sanity check
#' to see if the counts are logged. Default \code{TRUE}
#' @return A ggplot object of violin plot
#' @export
#' @examples
#' data("sceBatches")
#' logcounts(sceBatches) <- log1p(counts(sceBatches))
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGViolin(sce.w, "w.aVSb")
plotDEGViolin <- function(inSCE, useResult, threshP = FALSE, labelBy = NULL,
nrow = 6, ncol = 6, defaultTheme = TRUE,
isLogged = TRUE, check_sanity = TRUE){
#TODO: DO we split the up/down regulation too?
# Check
.checkDiffExpResultExists(inSCE, useResult, labelBy)
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
deg <- result$result
useAssay <- result$useAssay
useReducedDim <- result$useReducedDim
deg <- deg[order(deg$FDR),]
geneToPlot <- deg[seq_len(min(nrow(deg), nrow*ncol)), "Gene"]
groupName1 <- result$groupNames[1]
ix1 <- result$select$ix1
cells1 <- colnames(inSCE)[ix1]
groupName2 <- result$groupNames[2]
ix2 <- result$select$ix2
cells2 <- colnames(inSCE)[ix2]
if (!is.null(useAssay)) {
if(!is.null(labelBy)){
replGeneName <- SummarizedExperiment::rowData(inSCE[geneToPlot,])[[labelBy]]
} else {
replGeneName <- geneToPlot
}
expres <- expData(inSCE[geneToPlot, c(cells1, cells2)],
useAssay)
useMat <- useAssay
} else {
if(!is.null(labelBy)){
warning("Analysis performed on reducedDim. Cannot use rowData for ",
"labelBy. Ignored.")
}
replGeneName <- geneToPlot
expres <- t(expData(inSCE[, c(cells1, cells2)], useReducedDim))[geneToPlot,]
useMat <- useReducedDim
}
if(!is.matrix(expres)){
expres <- as.matrix(expres)
}
rownames(expres) <- replGeneName
# Format
cdat <- data.frame(wellKey = colnames(expres),
condition = factor(c(rep(groupName1, length(cells1)),
rep(groupName2, length(cells2))),
levels = result$groupNames),
ngeneson = rep("", (length(cells1) + length(cells2))),
stringsAsFactors = FALSE)
if (!isTRUE(isLogged)) {
expres <- log(expres + 1)
}
sca <- suppressMessages(MAST::FromMatrix(expres, cdat,
check_sanity = check_sanity))
if(threshP){
#TODO: if nrow*ncol < `min_per_bin`` below, there would be an error.
invisible(utils::capture.output(thres <-
MAST::thresholdSCRNACountMatrix(expres, nbins = 20,
min_per_bin = 30)))
SummarizedExperiment::assay(sca) <- thres$counts_threshold
}
flatDat <- methods::as(sca, "data.table")
flatDat$primerid <- factor(flatDat$primerid, levels = replGeneName)
names(flatDat)[5] <- useMat
# Plot
violinplot <- ggplot2::ggplot(flatDat,
ggplot2::aes_string(x = 'condition',
y = useMat,
color = 'condition')) +
ggplot2::geom_jitter() +
ggplot2::facet_wrap(~primerid, scale = "free_y",
ncol = ncol) +
ggplot2::geom_violin() +
ggplot2::ggtitle(paste0("Violin Plot for ", useResult))
if(isTRUE(defaultTheme)){
violinplot <- .ggSCTKTheme(violinplot)
}
return(violinplot)
}
#' Create linear regression plot to show the expression the of top DEGs
#' @details Any of the differential expression analysis method from SCTK should
#' be performed prior to using this function
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param threshP logical. Whether to plot threshold values from adaptive
#' thresholding, instead of using the assay used by when performing DE analysis.
#' Default \code{FALSE}.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Default \code{NULL}.
#' @param nrow Integer. Number of rows in the plot grid. Default \code{6}.
#' @param ncol Integer. Number of columns in the plot grid. Default \code{6}.
#' @param defaultTheme Logical scalar. Whether to use default SCTK theme in
#' ggplot. Default \code{TRUE}.
#' @param isLogged Logical scalar. Whether the assay used for the analysis is
#' logged. If not, will do a \code{log(assay + 1)} transformation. Default
#' \code{TRUE}.
#' @param check_sanity Logical scalar. Whether to perform MAST's sanity check
#' to see if the counts are logged. Default \code{TRUE}
#' @return A ggplot object of linear regression
#' @export
#' @examples
#' data("sceBatches")
#' logcounts(sceBatches) <- log1p(counts(sceBatches))
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGRegression(sce.w, "w.aVSb")
plotDEGRegression <- function(inSCE, useResult, threshP = FALSE, labelBy = NULL,
nrow = 6, ncol = 6, defaultTheme = TRUE,
isLogged = TRUE, check_sanity = TRUE){
#TODO: DO we split the up/down regulation too?
# Check
.checkDiffExpResultExists(inSCE, useResult, labelBy)
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
deg <- result$result
useReducedDim <- result$useReducedDim
useAssay <- result$useAssay
geneToPlot <- deg[seq_len(min(nrow(deg), nrow*ncol)), "Gene"]
groupName1 <- result$groupNames[1]
ix1 <- result$select$ix1
cells1 <- colnames(inSCE)[ix1]
groupName2 <- result$groupNames[2]
ix2 <- result$select$ix2
cells2 <- colnames(inSCE)[ix2]
if (!is.null(useAssay)) {
if(!is.null(labelBy)){
replGeneName <- SummarizedExperiment::rowData(inSCE[geneToPlot,])[[labelBy]]
} else {
replGeneName <- geneToPlot
}
expres <- expData(inSCE[geneToPlot, c(cells1, cells2)],
useAssay)
useMat <- useAssay
} else {
if (!is.null(labelBy)) {
warning("Analysis performed on reducedDim. Cannot use rowData for ",
"labelBy. Ignored.")
}
replGeneName <- geneToPlot
expres <- t(expData(inSCE[,c(cells1, cells2)], useReducedDim))[geneToPlot,]
useMat <- useReducedDim
}
if(!is.matrix(expres)){
expres <- as.matrix(expres)
}
rownames(expres) <- replGeneName
# Format
cdat <- data.frame(wellKey = colnames(expres),
condition = factor(c(rep(groupName1, length(cells1)),
rep(groupName2, length(cells2))),
levels = result$groupNames),
ngeneson = rep("", (length(cells1) + length(cells2))),
stringsAsFactors = FALSE)
if (!isTRUE(isLogged)) {
expres <- log(expres + 1)
}
sca <- suppressMessages(MAST::FromMatrix(expres, cData = cdat,
check_sanity = check_sanity))
cdr2 <- colSums(SummarizedExperiment::assay(sca) > 0)
SummarizedExperiment::colData(sca)$cngeneson <- scale(cdr2)
if(length(unique(SummarizedExperiment::colData(sca)$cngeneson)) < 2){
stop("Standardized cellular detection rate not various, unable to plot.")
}
if(threshP){
#TODO: if nrow*ncol < `min_per_bin`` below, there would be an error.
invisible(utils::capture.output(thres <-
MAST::thresholdSCRNACountMatrix(expres,
nbins = 20,
min_per_bin = 30)))
SummarizedExperiment::assay(sca) <- thres$counts_threshold
}
flatDat <- methods::as(sca, "data.table")
flatDat$primerid <- factor(flatDat$primerid, levels = replGeneName)
names(flatDat)[6] <- useMat
# Calculate
resData <- NULL
for (i in unique(flatDat$primerid)){
resdf <- flatDat[flatDat$primerid == i, ]
resdf$lmPred <- stats::lm(
stats::as.formula(paste0(useMat, "~cngeneson+", 'condition')),
data = flatDat[flatDat$primerid == i, ])$fitted
if (is.null(resData)){
resData <- resdf
} else {
resData <- rbind(resData, resdf)
}
}
# Plot
ggbase <- ggplot2::ggplot(resData, ggplot2::aes_string(
x = 'condition',
y = useMat,
color = 'condition')) +
ggplot2::geom_jitter() +
ggplot2::facet_wrap(~primerid, scale = "free_y",
ncol = ncol)
regressionplot <- ggbase +
ggplot2::aes_string(x = "cngeneson") +
ggplot2::geom_line(ggplot2::aes_string(y = "lmPred"),
lty = 1) +
ggplot2::xlab("Standardized Cellular Detection Rate") +
ggplot2::ggtitle(paste0("Linear Model Plot for ",
useResult))
if(isTRUE(defaultTheme)){
regressionplot <- .ggSCTKTheme(regressionplot)
}
return(regressionplot)
}
#' Get Top Table of a DEG analysis
#' @description Users have to run \code{runDEAnalysis()} first, any of the
#' wrapped functions of this generic function. Users can set further filters on
#' the result. A \code{data.frame} object, with variables of \code{Gene},
#' \code{Log2_FC}, \code{Pvalue}, and \code{FDR}, will be returned.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object, with of the
#' singleCellTK DEG method performed in advance.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param labelBy A single character for a column of \code{rowData(inSCE)} as
#' where to search for the labeling text. Leave \code{NULL} for \code{rownames}.
#' Default \code{metadata(inSCE)$featureDisplay} (see
#' \code{\link{setSCTKDisplayRow}}).
#' @param onlyPos logical. Whether to only fetch DEG with positive log2_FC
#' value. Default \code{FALSE}.
#' @param log2fcThreshold numeric. Only fetch DEGs with the absolute values of
#' log2FC larger than this value. Default \code{0.25}.
#' @param fdrThreshold numeric. Only fetch DEGs with FDR value smaller than this
#' value. Default \code{0.05}.
#' @param minGroup1MeanExp numeric. Only fetch DEGs with mean expression in
#' group1 greater then this value. Default \code{NULL}.
#' @param maxGroup2MeanExp numeric. Only fetch DEGs with mean expression in
#' group2 less then this value. Default \code{NULL}.
#' @param minGroup1ExprPerc numeric. Only fetch DEGs expressed in greater then
#' this fraction of cells in group1. Default \code{NULL}.
#' @param maxGroup2ExprPerc numeric. Only fetch DEGs expressed in less then this
#' fraction of cells in group2. Default \code{NULL}.
#' @return A \code{data.frame} object of the top DEGs, with variables of
#' \code{Gene}, \code{Log2_FC}, \code{Pvalue}, and \code{FDR}.
#' @export
#' @examples
#' data("sceBatches")
#' sceBatches <- scaterlogNormCounts(sceBatches, "logcounts")
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' getDEGTopTable(sce.w, "w.aVSb")
getDEGTopTable <- function(inSCE, useResult,
labelBy = S4Vectors::metadata(inSCE)$featureDisplay,
onlyPos = FALSE, log2fcThreshold = 0.25,
fdrThreshold = 0.05, minGroup1MeanExp = NULL,
maxGroup2MeanExp = NULL, minGroup1ExprPerc = NULL,
maxGroup2ExprPerc = NULL){
# Check
.checkDiffExpResultExists(inSCE, useResult, labelBy)
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]$result
if (!is.null(labelBy)) {
genes <- result$Gene
geneIdx <- featureIndex(genes, inSCE)
result$Gene <- SummarizedExperiment::rowData(inSCE)[[labelBy]][geneIdx]
}
# Filter
result <- .filterDETable(result, onlyPos, log2fcThreshold, fdrThreshold,
minGroup1MeanExp, maxGroup2MeanExp,
minGroup1ExprPerc, maxGroup2ExprPerc)
return(result)
}
#' Heatmap visualization of DEG result
#'
#' @details A differential expression analysis function has to be run in advance
#' so that information is stored in the metadata of the input SCE object. This
#' function wraps \code{\link{plotSCEHeatmap}}.
#' A feature annotation basing on the log2FC level called \code{"regulation"}
#' will be automatically added. A cell annotation basing on the condition
#' selection while running the analysis called \code{"condition"}, and the
#' annotations used from \code{colData(inSCE)} while setting the condition and
#' covariates will also be added.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param doLog Logical scalar. Whether to do \code{log(assay + 1)}
#' transformation on the assay used for the analysis. Default \code{FALSE}.
#' @param onlyPos logical. Whether to only plot DEG with positive log2_FC
#' value. Default \code{FALSE}.
#' @param log2fcThreshold numeric. Only plot DEGs with the absolute values of
#' log2FC larger than this value. Default \code{0.25}.
#' @param fdrThreshold numeric. Only plot DEGs with FDR value smaller than this
#' value. Default \code{0.05}.
#' @param minGroup1MeanExp numeric. Only plot DEGs with mean expression in
#' group1 greater then this value. Default \code{NULL}.
#' @param maxGroup2MeanExp numeric. Only plot DEGs with mean expression in
#' group2 less then this value. Default \code{NULL}.
#' @param minGroup1ExprPerc numeric. Only plot DEGs expressed in greater then
#' this fraction of cells in group1. Default \code{NULL}.
#' @param maxGroup2ExprPerc numeric. Only plot DEGs expressed in less then this
#' fraction of cells in group2. Default \code{NULL}.
#' @param useAssay character. A string specifying an assay of expression value
#' to plot. By default the assay used for \code{runMAST()} will be used.
#' Default \code{NULL}.
#' @param featureAnnotations \code{data.frame}, with \code{rownames} containing
#' all the features going to be plotted. Character columns should be factors.
#' Default \code{NULL}.
#' @param cellAnnotations \code{data.frame}, with \code{rownames} containing
#' all the cells going to be plotted. Character columns should be factors.
#' Default \code{NULL}.
#' @param featureAnnotationColor A named list. Customized color settings for
#' feature labeling. Should match the entries in the \code{featureAnnotations}
#' or \code{rowDataName}. For each entry, there should be a list/vector of
#' colors named with categories. Default \code{NULL}.
#' @param cellAnnotationColor A named list. Customized color settings for
#' cell labeling. Should match the entries in the \code{cellAnnotations} or
#' \code{colDataName}. For each entry, there should be a list/vector of colors
#' named with categories. Default \code{NULL}.
#' @param rowDataName character. The column name(s) in \code{rowData} that need
#' to be added to the annotation. Default \code{NULL}.
#' @param colDataName character. The column name(s) in \code{colData} that need
#' to be added to the annotation. Default \code{NULL}.
#' @param rowSplitBy character. Do semi-heatmap based on the grouping of
#' this(these) annotation(s). Should exist in either \code{rowDataName} or
#' \code{names(featureAnnotations)}. Default \code{"regulation"}.
#' @param colSplitBy character. Do semi-heatmap based on the grouping of
#' this(these) annotation(s). Should exist in either \code{colDataName} or
#' \code{names(cellAnnotations)}. Default \code{"condition"}.
#' @param rowLabel \code{FALSE} for not displaying; a variable in \code{rowData}
#' to display feature identifiers stored there; if have run
#' \code{\link{setSCTKDisplayRow}}, display the specified feature name;
#' \code{TRUE} for the \code{rownames} of \code{inSCE}; \code{NULL} for
#' auto-display \code{rownames} when the number of filtered feature is less
#' than 60. Default looks for \code{\link{setSCTKDisplayRow}} information.
#' @param title character. Main title of the heatmap. Default
#' \code{"DE Analysis: <useResult>"}.
#' @param ... Other arguments passed to \code{\link{plotSCEHeatmap}}
#' @examples
#' data("sceBatches")
#' logcounts(sceBatches) <- log1p(counts(sceBatches))
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGHeatmap(sce.w, "w.aVSb")
#' @return A \code{\link[ggplot2]{ggplot}} object
#' @export
#' @author Yichen Wang
plotDEGHeatmap <- function(inSCE, useResult, onlyPos = FALSE,
log2fcThreshold = 0.25, fdrThreshold = 0.05,
minGroup1MeanExp = NULL, maxGroup2MeanExp = NULL,
minGroup1ExprPerc = NULL, maxGroup2ExprPerc = NULL,
useAssay = NULL, doLog = FALSE,
featureAnnotations = NULL,
cellAnnotations = NULL,
featureAnnotationColor = NULL,
cellAnnotationColor = NULL,
rowDataName = NULL, colDataName = NULL,
colSplitBy = 'condition', rowSplitBy = 'regulation',
rowLabel = S4Vectors::metadata(inSCE)$featureDisplay,
title = paste0("DE Analysis: ", useResult), ...){
# Check
.checkDiffExpResultExists(inSCE, useResult)
extraArgs <- list(...)
warnArgs <- c('featureIndex', 'cellIndex')
if(any(warnArgs %in% names(extraArgs))){
warning('"', paste(warnArgs[warnArgs %in% names(extraArgs)],
collapse = ', '), '" are not allowed at this point.')
extraArgs[c('cellIndex', 'featureIndex')] <- NULL
}
# Extract
result <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]
if (!is.null(result$useReducedDim)) {
# Analysis performed with reducedDim, cannot set useAssay in plotDEGHeatmap
if (!is.null(useAssay)) {
warning("Analysis performed on reducedDim, cannot set `useAssay`, ",
"ignored.")
}
useAssay <- NULL
useReducedDim <- result$useReducedDim
} else {
if(is.null(useAssay)){
useAssay <- result$useAssay
}
useReducedDim <- NULL
}
ix1 <- result$select$ix1
ix2 <- result$select$ix2
deg.filtered <- getDEGTopTable(inSCE, useResult = useResult, labelBy = NULL,
onlyPos = onlyPos,
log2fcThreshold = log2fcThreshold,
fdrThreshold = fdrThreshold,
minGroup1MeanExp, maxGroup2MeanExp,
minGroup1ExprPerc, maxGroup2ExprPerc)
if(dim(deg.filtered)[1] <= 1){
stop('Too few genes that pass filtration, unable to plot')
}
# Not directly using deg.filtered$Gene because rownames might be deduplicated
# to avoid error when performing DEG.
if (!is.null(useReducedDim)) {
mat <- t(expData(inSCE, useReducedDim))
assayList <- list(mat)
names(assayList) <- useReducedDim
tmpSCE <- SingleCellExperiment::SingleCellExperiment(assays = assayList)
SummarizedExperiment::colData(tmpSCE) <- SummarizedExperiment::colData(inSCE)
assayName <- useReducedDim
} else {
tmpSCE <- inSCE
assayName <- useAssay
}
gene.ix <- rownames(tmpSCE) %in% deg.filtered$Gene
cell.ix <- which(ix1 | ix2)
allGenes <- rownames(tmpSCE)[gene.ix]
allCells <- colnames(tmpSCE)[cell.ix]
# Annotation organization
## Cells
group <- rep(NA, ncol(tmpSCE))
group[ix1] <- result$groupNames[1]
group[ix2] <- result$groupNames[2]
group <- factor(group[cell.ix], levels = result$groupNames)
if(!is.null(cellAnnotations)){
if(!all(allCells %in% rownames(cellAnnotations))){
stop('Not all cells involved in comparison found in given ',
'`cellAnnotations`. ')
}
cellAnnotations <- cellAnnotations[allCells, , drop = FALSE]
cellAnnotations <- data.frame(cellAnnotations, condition = group)
} else {
cellAnnotations <- data.frame(condition = group,
row.names = allCells)
}
kCol <- celda::distinctColors(2)
names(kCol) <- result$groupNames
if(!is.null(cellAnnotationColor)){
if(!"condition" %in% names(cellAnnotationColor)){
cellAnnotationColor <- c(list(condition = kCol),
cellAnnotationColor)
}
} else {
cellAnnotationColor <- list(condition = kCol)
}
if(!is.null(colDataName)){
if (length(which(colDataName %in% result$annotation)) > 0) {
colDataName <- colDataName[-which(colDataName %in% result$annotation)]
}
colDataName <- c(colDataName, result$annotation)
} else {
colDataName <- result$annotation
}
## Genes
regulation <- vector()
genes.up <- deg.filtered[deg.filtered$Log2_FC > 0, "Gene"]
genes.down <- deg.filtered[deg.filtered$Log2_FC < 0, "Gene"]
regulation[rownames(tmpSCE) %in% genes.up] <- 'up'
regulation[rownames(tmpSCE) %in% genes.down] <- 'down'
regulation <- factor(regulation[gene.ix], levels = c('up', 'down'))
if(!is.null(featureAnnotations)){
if(!all(allGenes %in% rownames(featureAnnotations))){
stop('Not all genes involved in comparison found in given ',
'`featureAnnotations`. ')
}
featureAnnotations <- featureAnnotations[allGenes, , drop = FALSE]
featureAnnotations <- data.frame(featureAnnotations,
regulation = regulation)
} else {
featureAnnotations <- data.frame(regulation = regulation,
row.names = allGenes)
}
lCol <- celda::distinctColors(2)
names(lCol) <- c('up', 'down')
if(!is.null(featureAnnotationColor)){
if(!"regulation" %in% names(featureAnnotationColor)){
featureAnnotationColor <- c(list(regulation = lCol),
featureAnnotationColor)
}
} else {
featureAnnotationColor <- list(regulation = lCol)
}
if (is.null(rowLabel) & nrow(deg.filtered) < 60) {
rowLabel <- TRUE
}
# Plot
hm <- plotSCEHeatmap(inSCE = tmpSCE, useAssay = assayName, doLog = doLog,
featureIndex = gene.ix, cellIndex = cell.ix,
featureAnnotations = featureAnnotations,
cellAnnotations = cellAnnotations,
rowDataName = rowDataName,
colDataName = colDataName,
featureAnnotationColor = featureAnnotationColor,
cellAnnotationColor = cellAnnotationColor,
rowSplitBy = rowSplitBy, colSplitBy = colSplitBy,
title = title, rowLabel = rowLabel, ...)
return(hm)
}
#' Generate volcano plot for DEGs
#' @details Any of the differential expression analysis method from SCTK should
#' be performed prior to using this function to generate volcano plots.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useResult character. A string specifying the \code{analysisName}
#' used when running a differential expression analysis function.
#' @param labelTopN Integer, label this number of top DEGs that pass the
#' filters. \code{FALSE} for not labeling. Default \code{10}.
#' @param log2fcThreshold numeric. Label genes with the absolute values of
#' log2FC greater than this value as regulated. Default \code{0.25}.
#' @param fdrThreshold numeric. Label genes with FDR value less than this
#' value as regulated. Default \code{0.05}.
#' @param featureDisplay A character string to indicate a variable in
#' \code{rowData(inSCE)} for feature labeling. \code{NULL} for using
#' \code{rownames}. Default \code{metadata(inSCE)$featureDisplay} (see
#' \code{\link{setSCTKDisplayRow}})
#' @return A \code{ggplot} object of volcano plot
#' @seealso \code{\link{runDEAnalysis}}, \code{\link{plotDEGHeatmap}}
#' @export
#' @examples
#' data("sceBatches")
#' sceBatches <- scaterlogNormCounts(sceBatches, "logcounts")
#' sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
#' sce.w <- runWilcox(sce.w, class = "cell_type",
#' classGroup1 = "alpha", classGroup2 = "beta",
#' groupName1 = "w.alpha", groupName2 = "w.beta",
#' analysisName = "w.aVSb")
#' plotDEGVolcano(sce.w, "w.aVSb")
plotDEGVolcano <- function(
inSCE,
useResult,
labelTopN = 10,
log2fcThreshold = 0.25,
fdrThreshold = 0.05,
featureDisplay = S4Vectors::metadata(inSCE)$featureDisplay
) {
.checkDiffExpResultExists(inSCE, useResult, featureDisplay)
deg <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]$result
deg <- deg[order(abs(deg$Log2_FC), decreasing = TRUE),]
if (!is.null(featureDisplay)) {
geneIdx <- featureIndex(deg$Gene, inSCE)
deg$Gene <- SummarizedExperiment::rowData(inSCE)[[featureDisplay]][geneIdx]
}
rownames(deg) <- deg$Gene
groupNames <- S4Vectors::metadata(inSCE)$diffExp[[useResult]]$groupNames
# Prepare for coloring that shows the filtering
deg$Regulation <- NA
deg$Regulation[deg$Log2_FC > 0] <- "Up"
deg$Regulation[deg$Log2_FC < 0] <- "Down"
if (!is.null(log2fcThreshold)) {
deg$Regulation[abs(deg$Log2_FC) <= log2fcThreshold] <- "No"
}
if (!is.null(fdrThreshold)) {
deg$Regulation[deg$FDR >= fdrThreshold] <- "No"
}
# Prepare for Top DEG text labeling
passIdx <- deg$Regulation != "No"
deg$label <- NA
if (!is.null(labelTopN) & !isFALSE(labelTopN)) {
labelTopN <- min(labelTopN, length(which(passIdx)))
if (labelTopN > 0) {
deg.pass <- deg[passIdx,]
label.origTable.idx <- deg$Gene %in% deg.pass$Gene[seq(labelTopN)]
deg$label[label.origTable.idx] <- deg$Gene[label.origTable.idx]
}
}
# Prepare for lines that mark the cutoffs
vlineLab <- data.frame(
X = c(-log2fcThreshold, log2fcThreshold),
text = c(paste("lower log2FC cutoff:", -log2fcThreshold),
paste("upper log2FC cutoff:", log2fcThreshold)),
h = c(1.01, -0.01)
)
hlineLab <- data.frame(
Y = c(-log10(fdrThreshold)),
text = paste("FDR cutoff:", fdrThreshold)
)
# Plot
ggplot2::ggplot() +
ggplot2::geom_point(data = deg,
ggplot2::aes_string(x = "Log2_FC", y = "-log10(FDR)",
col = "Regulation")) +
ggplot2::scale_color_manual(values = c("Down" = "#619cff",
"No" = "light grey",
"Up" = "#f8766d")) +
ggrepel::geom_text_repel(data = deg,
ggplot2::aes_string(x = "Log2_FC",
y = "-log10(FDR)",
label = "label"),
colour = "black", na.rm = TRUE) +
ggplot2::geom_vline(data = vlineLab,
ggplot2::aes_string(xintercept = "X"),
linetype = "longdash") +
ggplot2::geom_text(data = vlineLab,
ggplot2::aes_string(x = "X", y = 0, label = "text",
hjust = "h"),
size = 3, vjust = 1) +
ggplot2::geom_hline(data = hlineLab,
ggplot2::aes_string(yintercept = "Y"),
linetype = "longdash") +
ggplot2::geom_text(data = hlineLab,
ggplot2::aes_string(x = -Inf, y = "Y", label = "text"),
size = 3, vjust = -0.5, hjust = -.03) +
ggplot2::xlab("Fold Change (log2)") +
ggplot2::ylab("FDR (-Log10 q-value)") +
ggplot2::theme(panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
axis.line = ggplot2::element_line(colour = "black")) +
ggplot2::xlim(-max(abs(deg$Log2_FC)), max(abs(deg$Log2_FC))) +
ggplot2::ggtitle(paste("DEG between", groupNames[1],
"and", groupNames[2]))
}
#' MAST Identify adaptive thresholds
#'
#' Calculate and produce a list of thresholded counts (on natural scale),
#' thresholds, bins, densities estimated on each bin, and the original data from
#' \code{\link[MAST]{thresholdSCRNACountMatrix}}
#' @param inSCE SingleCellExperiment object
#' @param useAssay character, default \code{"logcounts"}
#' @param doPlot Logical scalar. Whether to directly plot in the plotting area.
#' If \code{FALSE}, will return a graphical object which can be visualized with
#' \code{grid.draw()}. Default \code{TRUE}.
#' @param isLogged Logical scalar. Whether the assay used for the analysis is
#' logged. If not, will do a \code{log(assay + 1)} transformation. Default
#' \code{TRUE}.
#' @param check_sanity Logical scalar. Whether to perform MAST's sanity check
#' to see if the counts are logged. Default \code{TRUE}
#' @return Plot the thresholding onto the plotting region if \code{plot == TRUE}
#' or a graphical object if \code{plot == FALSE}.
#' @export
#' @examples
#' data("mouseBrainSubsetSCE")
#' plotMASTThresholdGenes(mouseBrainSubsetSCE)
plotMASTThresholdGenes <- function(inSCE, useAssay="logcounts", doPlot = TRUE,
isLogged = TRUE, check_sanity = TRUE){
# data preparation
expres <- expData(inSCE, useAssay)
if(!is.matrix(expres)){
expres <- as.matrix(expres)
}
expres <- dedupRowNames(expres)
fdata <- data.frame(Gene = rownames(expres))
rownames(fdata) <- fdata$Gene
SCENew <- MAST::FromMatrix(expres, SingleCellExperiment::colData(inSCE),
fdata, check_sanity = check_sanity)
SCENew <- SCENew[which(MAST::freq(SCENew) > 0), ]
invisible(utils::capture.output(thres <- MAST::thresholdSCRNACountMatrix(
SummarizedExperiment::assay(SCENew), nbins = 20, min_per_bin = 30,
data_log = isLogged)))
# plotting
plotNRow <- ceiling(length(thres$valleys) / 4)
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)
})
if (isTRUE(doPlot)) {
grid::grid.draw(thres.grob)
} else {
return(thres.grob)
}
}
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