.searchBCDefaultInfo <- function(inSCE, corrMat, origAssay, matType) {
if (is.null(origAssay)) {
if ("counts" %in% expDataNames(inSCE)) {
origAssay <- "counts"
} else {
origAssay <- expDataNames(inSCE)[1]
}
warning("using '", origAssay, "' for comparison.")
}
if (is.null(matType)) {
if (corrMat %in% SummarizedExperiment::assayNames(inSCE)) {
matType <- "assay"
} else if (corrMat %in% SingleCellExperiment::altExpNames(inSCE)) {
matType <- "altExp"
} else if (corrMat %in% SingleCellExperiment::reducedDimNames(inSCE)) {
matType <- "reducedDim"
} else {
stop("Corrected Matrix name '", corrMat, "' not found in inSCE")
}
}
return(c(origAssay, matType))
}
.checkBCMeta <- function(inSCE, corrMat, origAssay, origLogged, method, matType,
batch, condition) {
if (!is.null(matType)) {
if (!matType %in% c("assay", "altExp", "reducedDim")) {
stop("Wrong matrix type '", matType, "'. Choose from 'assay', 'altExp', ",
"'reducedDim'.")
}
}
if (!"batchCorr" %in% names(S4Vectors::metadata(inSCE))) {
warning("Batch correction result from SCTK not found.")
s <- .searchBCDefaultInfo(inSCE, corrMat, origAssay, matType)
origAssay <- ifelse(is.null(origAssay), s[1], origAssay)
method <- ifelse(is.null(method), "Unidentified Method", method)
matType <- ifelse(is.null(matType), s[2], matType)
} else {
if (!corrMat %in% names(S4Vectors::metadata(inSCE)$batchCorr)) {
warning("'", corrMat, "' not identified as a Batch correction result ",
"from SCTK")
s <- .searchBCDefaultInfo(inSCE, corrMat, origAssay, matType)
origAssay <- ifelse(is.null(origAssay), s[1], origAssay)
method <- ifelse(is.null(method), "Unidentified Method", method)
matType <- ifelse(is.null(matType), s[2], matType)
} else {
bcInfo <- S4Vectors::metadata(inSCE)$batchCorr[[corrMat]]
origAssay <- ifelse(is.null(origAssay), bcInfo$useAssay, origAssay)
origLogged <- ifelse(is.null(origLogged), bcInfo$origLogged, origLogged)
method <- ifelse(is.null(method), bcInfo$method, method)
if (!is.null(matType) && matType != bcInfo$matType) {
warning("User specified matType different from SCTK identified ",
"matType. Force using user specification.")
}
matType <- ifelse(is.null(matType), bcInfo$matType, matType)
if (is.null(batch)) batch <- bcInfo$batch
if (is.null(condition)) condition <- bcInfo$condition
}
}
return(list(origAssay = origAssay,
origLogged = origLogged,
method = method,
matType = matType,
batch = batch,
condition = condition))
}
#' Plot comparison of batch corrected result against original assay
#' @details Four plots will be combined. Two of them are violin/box-plots for
#' percent variance explained by the batch variation, and optionally the
#' covariate, for original and corrected. The other two are UMAPs of the
#' original assay and the correction result matrix. If SCTK batch correction
#' methods are performed in advance, this function will automatically detect
#' necessary input. Otherwise, users can also customize the input. Future
#' improvement might include solution to reduce redundant UMAP calculation.
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param corrMat A single character indicating the name of the corrected matrix.
#' @param batch A single character. The name of batch annotation column in
#' \code{colData(inSCE)}.
#' @param condition A single character. The name of an additional covariate
#' annotation column in \code{colData(inSCE)}.
#' @param origAssay A single character indicating what the original assay used
#' for batch correction is.
#' @param origLogged Logical scalar indicating whether \code{origAssay} is
#' log-normalized.
#' @param method A single character indicating the name of the batch correction
#' method. Only used for the titles of plots.
#' @param matType A single character indicating the type of the batch correction
#' result matrix, choose from \code{"assay"}, \code{"altExp"},
#' \code{"reducedDim"}.
#' @return An object of class \code{"gtable"}, combining four \code{ggplot}s.
#' @examples
#' data("sceBatches")
#' logcounts(sceBatches) <- log1p(counts(sceBatches))
#' sceBatches <- runLimmaBC(sceBatches)
#' plotBatchCorrCompare(sceBatches, "LIMMA", condition = "cell_type")
#' @export
#' @author Yichen Wang
plotBatchCorrCompare <- function(inSCE, corrMat, batch = NULL, condition = NULL,
origAssay = NULL, origLogged = NULL,
method = NULL, matType = NULL) {
if(!inherits(inSCE, "SingleCellExperiment")){
stop("\"inSCE\" should be a SingleCellExperiment Object.")
}
m <- .checkBCMeta(inSCE, corrMat, origAssay, origLogged, method, matType,
batch, condition)
origAssay <- m$origAssay
origLogged <- m$origLogged
method <- m$method
matType <- m$matType
batch <- m$batch
condition <- m$condition
if (isFALSE(origLogged)) {
inSCE <- scaterlogNormCounts(inSCE, origAssay, origAssay)
}
# Batch Variance Plot for origAssay
bv.before <- plotBatchVariance(inSCE, useAssay = origAssay, useReddim = NULL,
useAltExp = NULL, batch = batch,
condition = condition,
title = "Batch Variance before correction") +
ggplot2::theme(text=ggplot2::element_text(size=10))
inSCE <- runUMAP(inSCE, useAssay = origAssay, useReducedDim = NULL,
reducedDimName = "umap.before")
umap.before <- plotSCEDimReduceColData(inSCE, batch, "umap.before",
shape = condition, axisLabelSize = 9,
axisSize = 8, dotSize = 1,
titleSize = 12, labelClusters = FALSE,
legendSize = 10, legendTitle = "batch",
legendTitleSize = 10,
title = "UMAP before correction")
if (matType == "assay") {
if (isFALSE(origLogged)) {
inSCE <- scaterlogNormCounts(inSCE, corrMat, corrMat)
}
# Batch Variance Plot for CorrMat
bv.after <- plotBatchVariance(inSCE, useAssay = corrMat, batch = batch,
condition = condition,
title = paste0("Batch Variance corrected with ",
method)) +
ggplot2::theme(text=ggplot2::element_text(size=10))
if (method == "ComBatSeq") {
inSCE <- runUMAP(inSCE, useAssay = corrMat, useReducedDim = NULL,
logNorm = TRUE, reducedDimName = "umap.after")
} else {
inSCE <- runUMAP(inSCE, useAssay = corrMat, useReducedDim = NULL,
logNorm = FALSE, reducedDimName = "umap.after")
}
} else if (matType == "altExp") {
# Doing log, because only Seurat returns altExp,
# and the assay inside is not logged
ae <- SingleCellExperiment::altExp(inSCE, corrMat)
ae <- scaterlogNormCounts(ae, corrMat, corrMat)
SingleCellExperiment::altExp(inSCE, corrMat) <- ae
bv.after <- plotBatchVariance(inSCE, useAltExp = corrMat, batch = batch,
condition = condition,
title = paste0("Batch Variance corrected with ",
method)) +
ggplot2::theme(text=ggplot2::element_text(size=10))
inSCE <- runQuickUMAP(inSCE, useAssay = corrMat, useAltExp = corrMat,
reducedDimName = "umap.after")
} else if (matType == "reducedDim") {
bv.after <- plotBatchVariance(inSCE, useReddim = corrMat, batch = batch,
condition = condition,
title = paste0("Batch Variance corrected with ",
method)) +
ggplot2::theme(text=ggplot2::element_text(size=10))
if (method == "BBKNN") {
SingleCellExperiment::reducedDim(inSCE, "umap.after") <-
SingleCellExperiment::reducedDim(inSCE, corrMat)
} else {
inSCE <- runUMAP(inSCE, useReducedDim = corrMat,
reducedDimName = "umap.after")
}
} else {
stop("Cannot identify result matrix type")
}
umap.after <- plotSCEDimReduceColData(inSCE, batch, "umap.after", dim1 = 1,
dim2 = 2,
shape = condition, axisLabelSize = 9,
axisSize = 8, dotSize = 1,
titleSize = 12, labelClusters = FALSE,
legendSize = 10, legendTitle = "batch",
legendTitleSize = 10,
title = "UMAP after correction") +
ggplot2::theme(text=ggplot2::element_text(size=8))
return(gridExtra::grid.arrange(bv.before, bv.after,
umap.before, umap.after, nrow = 2))
}
#' Plot the percent of the variation that is explained by batch and condition
#' in the data
#'
#' Visualize the percent variation in the data that is explained by batch and
#' condition, individually, and that explained by combining both annotations.
#' Plotting only the variation explained by batch is supported but not
#' recommended, because this can be confounded by potential condition.
#'
#' When condition and batch both are causing some variation, if the difference
#' between full variation and condition variation is close to batch variation,
#' this might imply that batches are causing some effect; if the difference is
#' much less than batch variation, then the batches are likely to be confounded
#' by the conditions.
#'
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useAssay A single character. The name of the assay that stores the
#' value to plot. For \code{useReddim} and \code{useAltExp} also. Default
#' \code{NULL}.
#' @param useReddim A single character. The name of the dimension reduced
#' matrix that stores the value to plot. Default \code{NULL}.
#' @param useAltExp A single character. The name of the alternative experiment
#' that stores an assay of the value to plot. Default \code{NULL}.
#' @param batch A single character. The name of batch annotation column in
#' \code{colData(inSCE)}. Default \code{"batch"}.
#' @param condition A single character. The name of an additional condition
#' annotation column in \code{colData(inSCE)}. Default \code{NULL}.
#' @param title A single character. The title text on the top. Default
#' \code{NULL}.
#' @return A ggplot object of a boxplot of variation explained by batch,
#' condition, and batch+condition.
#' @export
#' @examples
#' data('sceBatches', package = 'singleCellTK')
#' plotBatchVariance(sceBatches,
#' useAssay="counts",
#' batch="batch",
#' condition = "cell_type")
plotBatchVariance <- function(inSCE, useAssay = NULL, useReddim = NULL,
useAltExp = NULL, batch = 'batch',
condition = NULL, title = NULL) {
useMat <- .selectSCEMatrix(inSCE, useAssay = useAssay,
useReducedDim = useReddim, useAltExp = useAltExp,
returnMatrix = TRUE, cellAsCol = TRUE)
mat <- useMat$mat
if(is.null(batch)){
stop("Batch annotation has to be given.")
}
batchCol <- .manageCellVar(inSCE, var = batch, as.factor = TRUE)
if(!inherits(mat, 'matrix')){
mat <- as.matrix(mat)
}
nlb <- nlevels(batchCol)
if (nlb <= 1){
stop("No more than one batch found in specified annotation")
} else {
batchMod <- stats::model.matrix(~batchCol)
}
if (is.null(condition)){
condMod <- matrix(rep(1, ncol(mat)), ncol = 1)
} else {
condCol <- .manageCellVar(inSCE, var = condition, as.factor = TRUE)
nlc <- nlevels(condCol)
if (nlc <= 1){
condMod <- matrix(rep(1, ncol(mat)), ncol = 1)
} else {
condMod <- stats::model.matrix(~condCol)
}
}
mod <- cbind(condMod, batchMod[, -1])
condTest <- .batchqc_f.pvalue(mat, mod, batchMod)
batchTest <- .batchqc_f.pvalue(mat, mod, condMod)
r2Full <- condTest$r2Full
condR2 <- batchTest$r2Reduced
batchR2 <- condTest$r2Reduced
explainedVariation <- round(cbind(`Full (Condition+Batch)` = r2Full,
Condition = condR2,
Batch = batchR2), 5) * 100
colnames(explainedVariation) <- c("Full",
ifelse(is.null(condition), "No Condition", condition),
batch)
exVarM <- reshape2::melt(explainedVariation)
colnames(exVarM) <- c("Gene", "Model", "Percent.Explained.Variation")
exVarM$Model <- factor(exVarM$Model)
a <- ggplot2::ggplot(exVarM,
ggplot2::aes_string("Model",
"Percent.Explained.Variation")) +
ggplot2::geom_point(position = ggplot2::position_jitter(width = 0.2),
size = 1, alpha = 0.9) +
ggplot2::geom_violin(ggplot2::aes_string(fill = "Model"), alpha = 0.7, ) +
ggplot2::geom_boxplot(alpha = 0.4, width = 0.2) +
ggplot2::ylim(0, 100) +
ggplot2::xlab("Model") +
ggplot2::ylab("Explained Variation %") +
ggplot2::ggtitle(title) +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "none",
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank())
return(a)
}
.batchqc_f.pvalue <- function(dat, mod, mod0) {
# F-test (full/reduced model) and returns R2 values
# (full/reduced) as well.
mod00 <- matrix(rep(1, ncol(dat)), ncol = 1)
n <- dim(dat)[2]
m <- dim(dat)[1]
df1 <- dim(mod)[2]
df0 <- dim(mod0)[2]
p <- rep(0, m)
resid <- dat - dat %*% mod %*% solve(t(mod) %*% mod) %*% t(mod)
rss1 <- rowSums(resid * resid)
rm(resid)
resid0 <- dat - dat %*% mod0 %*% solve(t(mod0) %*% mod0) %*% t(mod0)
rss0 <- rowSums(resid0 * resid0)
rm(resid0)
resid00 <- dat - dat %*% mod00 %*% solve(t(mod00) %*% mod00) %*% t(mod00)
rss00 <- rowSums(resid00 * resid00)
rm(resid00)
r2Full <- 1 - rss1 / rss00
r2Reduced <- 1 - rss0 / rss00
p <- 1
if (df1 > df0) {
fstats <- ((rss0 - rss1) / (df1 - df0)) / (rss1 / (n - df1))
p <- 1 - stats::pf(fstats, df1 = (df1 - df0), df2 = (n - df1))
}
return(list(p = p, r2Full = r2Full, r2Reduced = r2Reduced))
}
#' Plot mean feature value in each batch of a SingleCellExperiment object
#' @param inSCE \linkS4class{SingleCellExperiment} inherited object.
#' @param useAssay A single character. The name of the assay that stores the
#' value to plot. For \code{useReddim} and \code{useAltExp} also. Default
#' \code{NULL}.
#' @param useReddim A single character. The name of the dimension reduced
#' matrix that stores the value to plot. Default \code{NULL}.
#' @param useAltExp A single character. The name of the alternative experiment
#' that stores an assay of the value to plot. Default \code{NULL}.
#' @param batch A single character. The name of batch annotation column in
#' \code{colData(inSCE)}. Default \code{"batch"}.
#' @param xlab label for x-axis. Default \code{"batch"}.
#' @param ylab label for y-axis. Default \code{"Feature Mean"}.
#' @param ... Additional arguments passed to \code{.ggViolin}.
#' @examples
#' data('sceBatches', package = 'singleCellTK')
#' plotSCEBatchFeatureMean(sceBatches, useAssay = "counts")
#' @return ggplot
#' @export
plotSCEBatchFeatureMean <- function(inSCE, useAssay = NULL, useReddim = NULL,
useAltExp = NULL, batch = 'batch', xlab='batch', ylab='Feature Mean', ...){
if(!inherits(inSCE, 'SingleCellExperiment')){
stop("'inSCE' must inherit from 'SingleCellExperiment'.")
}
if(is.null(useAssay) + is.null(useReddim) + is.null(useAltExp) != 2){
stop("One and only one of `useAssay`, `useReddim`, ",
"`usAltExp` has to be specified.")
}
if(!is.null(useAssay)){
if(!useAssay %in% SummarizedExperiment::assayNames(inSCE)){
stop("'useAssay' not found in 'inSCE'.")
}
mat <- SummarizedExperiment::assay(inSCE, useAssay)
}
if(!is.null(useReddim)){
if(!useReddim %in% SingleCellExperiment::reducedDimNames(inSCE)){
stop("'useReddim not found in 'inSCE'.")
}
mat <- t(SingleCellExperiment::reducedDim(inSCE, useReddim))
}
if(!is.null(useAltExp)){
if(!useAltExp %in% SingleCellExperiment::altExpNames(inSCE)){
stop("'useAltExp not found in 'inSCE'.")
}
ae <- SingleCellExperiment::altExp(inSCE, useAltExp)
mat <- SummarizedExperiment::assay(ae)
}
if(is.null(batch)){
stop("Batch annotation has to be given.")
} else{
if(!batch %in% names(SummarizedExperiment::colData(inSCE))){
stop("'batch' not found in 'inSCE'.")
}
}
if(!inherits(mat, 'matrix')){
mat <- as.matrix(mat)
}
batchCol <- SummarizedExperiment::colData(inSCE)[[batch]]
uniqBatch <- as.vector(unique(batchCol)) #as.vector in case batchCol is factor
allMeans <- numeric()
groupBy <- character()
for(i in uniqBatch){
allMeans <- c(allMeans, DelayedArray::rowMeans(mat[,batchCol == i]))
groupBy <- c(groupBy, rep(i, nrow(mat)))
}
p <- .ggViolin(allMeans, groupBy = groupBy, xlab = xlab, ylab = ylab, ...)
p <- .ggSCTKTheme(p)
return(p)
}
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