##' @title Visualization of Ranked gRNA Abundances by Replicate
##' @description This function median scales and log2 transforms the raw gRNA count data contained in an ExpressionSet,
##' and then plots the ordered expression values within each replicate. The curve colors are assigned based on a user-
##' specified column of the pData contained in the ExpressionSet. Optionally, this function can plot the location of Nontargeting control
##' guides (or any guides, really) within the distribution.
##' @param eset An ExpressionSet object containing, at minimum, count data accessible by exprs() and some phenoData.
##' @param sampleKey A sample key, supplied as a (possibly ordered) factor linking the samples to experimental
##' variables. The \code{names} attribute should exactly match those present in \code{eset}, and the control set
##' is assumed to be the first \code{level}.
##' @param annotation An annotation dataframe indicating the nontargeting controls in the geneID column.
##' @param geneSymb The \code{geneSymbol} identifier(s) in \code{annotation} that corresponds to gRNAs to be plotted on the curves.
##' If the provided value is not present in the \code{geneSymbol}, nontargeting controls will be plotted instead.
##' @param lib.size An optional vector of voom-appropriate library size adjustment factors, usually calculated with \code{\link[edgeR]{calcNormFactors}}
##' and transformed to reflect the appropriate library size. These adjustment factors are interpreted as the total library sizes for each sample,
##' and if absent will be extrapolated from the columnwise count sums of the \code{exprs} slot of the \code{eset}.
##' @return A waterfall plot as specified, on the default device.
##' @author Russell Bainer
##' @examples
##' data('es')
##' data('ann')
##'
##' #Build the sample key
##' library(Biobase)
##' sk <- ordered(relevel(as.factor(pData(es)$TREATMENT_NAME), 'ControlReference'))
##' names(sk) <- row.names(pData(es))
##'
##' ct.gRNARankByReplicate(es, sk, ann, 'Target1377')
##' @export
ct.gRNARankByReplicate <- function(eset, sampleKey, annotation = NULL, geneSymb = NULL, lib.size = NULL) {
# current.graphic.params <- par(no.readonly = TRUE) on.exit(suppressWarnings(par(current.graphic.params)))
if (!methods::is(eset, "ExpressionSet")) {
stop(deparse(substitute(eset)), "must be an ExpressionSet.")
}
if (is.null(sampleKey)) {
sampleKey <- colnames(eset)
names(sampleKey) <- sampleKey
}
sampleKey <- ct.keyCheck(sampleKey, eset)
sampleKey <- sampleKey[order(sampleKey)]
counts <- exprs(eset)
if (is.null(lib.size)) {
lib.size <- colSums(counts)
} else if (!is.numeric(lib.size) | length(lib.size) != ncol(counts)) {
stop("If specified, lib.size must be a numeric vector of the same
length as the number of samples in the eset.")
}
# Convert to CPM
e.dat <- t(log2(t(counts + 0.5)/(lib.size + 1) * 1e+06))
y <- range(e.dat)
x <- c(1, nrow(e.dat))
if (is.null(annotation) | is.null(geneSymb)) {
plottitle <- ""
} else if (geneSymb %in% annotation$geneSymbol) {
plottitle <- geneSymb
} else {
plottitle <- "Nontargeting Controls"
}
plot(x[1], y[1], xlim = x, ylim = y, xlab = "gRNA Abundance Rank", ylab = "Log2 Counts", pch = NA, main = plottitle)
colors <- colorRampPalette(c("blue", "red"), alpha = TRUE)(length(levels(sampleKey)))
colors <- gsub("FF$", "99", colors, perl = TRUE)
invisible(lapply(seq_len(ncol(e.dat)), function(x) {
lines(seq_len(nrow(e.dat)), sort(e.dat[, names(sampleKey)[x]], decreasing = TRUE), col = colors[as.numeric(sampleKey)[x]])
}))
# Add the NTC locations if requested.
if (!is.null(geneSymb)) {
if (is.null(annotation) | !("geneSymbol" %in% names(annotation))) {
stop("An annotation dataframe must be supplied if geneSymb is not NULL.")
}
annotation <- ct.prepareAnnotation(annotation, eset, throw.error = FALSE)
if (any(geneSymb %in% annotation$geneSymbol)) {
ntc <- row.names(annotation)[annotation$geneSymbol %in% geneSymb]
} else {
ntc <- row.names(annotation)[(annotation$geneSymbol %in% "NoTarget")]
}
if (length(ntc) == 0) {
stop("No suitable elements are present in the supplied annotation file.
Please specify a geneSymbol if you want to display individual guides.")
}
# make a table of the ntc locations and ranks
ntc.ranks <- (apply(e.dat, 2, rank))
ntc.ranks <- ntc.ranks[ntc, ]
ntc.ranks <- nrow(e.dat) - ntc.ranks
ntc.dat <- e.dat[ntc, ]
rimcolors <- colorRampPalette(c("black", "blue", "green", "red", "yellow", "white"))(nrow(ntc.dat))
invisible(lapply(seq_len(ncol(ntc.dat)), function(x) {
points(ntc.ranks[, names(sampleKey)[x]], ntc.dat[, names(sampleKey)[x]], bg = rimcolors, col = colors[as.numeric(sampleKey[x])], pch = 23, lwd = 4)
}))
}
legend("bottomleft", legend = levels(sampleKey), fill = colors, bty = "n")
}
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