#' 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 if it is given.
#'
#' @param inSCE Input SCtkExperiment object. Required
#' @param useAssay Indicate which assay to use for PCA. Default is "logcounts"
#' @param batch The column in the annotation data that corresponds to batch.
#' Required
#' @param condition The column in the annotation data that corresponds to
#' condition. Optional
#'
#' @return A boxplot of variation explained by batch, condition, and
#' batch+condition (if applicable).
#' @export
#' @examples
#' if(requireNamespace("bladderbatch", quietly = TRUE)) {
#' library(bladderbatch)
#' data(bladderdata)
#' dat <- as(as(bladderEset, "SummarizedExperiment"), "SCtkExperiment")
#' plotBatchVariance(dat, useAssay="exprs", batch="batch", condition = "cancer")
#' }
#'
plotBatchVariance <- function(inSCE, useAssay="logcounts", batch,
condition=NULL){
nlb <- nlevels(as.factor(SingleCellExperiment::colData(inSCE)[, batch]))
if (nlb <= 1){
batchMod <- matrix(rep(1, ncol(inSCE)), ncol = 1)
} else {
batchMod <- stats::model.matrix(
~as.factor(SingleCellExperiment::colData(inSCE)[, batch]))
}
if (is.null(condition)){
stop("condition required for now")
} else {
nlc <- nlevels(as.factor(
SingleCellExperiment::colData(inSCE)[, condition]))
if (nlc <= 1){
condMod <- matrix(rep(1, ncol(inSCE)), ncol = 1)
} else {
condMod <- stats::model.matrix(
~as.factor(SingleCellExperiment::colData(inSCE)[, condition]))
}
}
mod <- cbind(condMod, batchMod[, -1])
condTest <- batchqc_f.pvalue(SummarizedExperiment::assay(inSCE, useAssay),
mod, batchMod)
batchTest <- batchqc_f.pvalue(
SummarizedExperiment::assay(inSCE, useAssay), mod, condMod)
r2Full <- condTest$r2Full
condR2 <- batchTest$r2Reduced
batchR2 <- condTest$r2Reduced
explainedVariation <- round(cbind(`Full (Condition+Batch)` = r2Full,
Condition = condR2,
Batch = batchR2), 5) * 100
exVarM <- reshape2::melt(explainedVariation)
colnames(exVarM) <- c("Gene", "Model", "Percent.Explained.Variation")
exVarM$Model <- factor(exVarM$Model)
a <- ggplot2::ggplot(exVarM, ggplot2::aes_string("Model", "Percent.Explained.Variation")) +
ggplot2::geom_violin(ggplot2::aes_string(fill = "Model")) +
ggplot2::geom_boxplot(width = .1) +
ggplot2::xlab("Model") +
ggplot2::ylab("Percent Explained Variation") +
ggplot2::scale_fill_manual(values = RColorBrewer::brewer.pal(9, "Set1"),
guide = FALSE)
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))
}
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