# --------------------- Uniform Clusters ----------------------
#' @name UniformClusters
#'
#' @details `GDIPlot()` directly evaluates and plots the `GDI` for a sample.
#'
#' @param objCOTAN a `COTAN` object
#' @param genes a named `list` of genes to label. Each array will have different
#' color.
#' @param condition a string corresponding to the condition/sample (it is used
#' only for the title).
#' @param statType type of statistic to be used. Default is "S": Pearson's
#' chi-squared test statistics. "G" is G-test statistics
#' @param GDIThreshold the threshold level that discriminates uniform clusters.
#' It defaults to \eqn{1.43}
#' @param GDIIn when the `GDI` data frame was already calculated, it can be put
#' here to speed up the process (default is `NULL`)
#'
#' @returns `GDIPlot()` returns a `ggplot2` object with a point got each gene,
#' where on the ordinates are the `GDI` levels and on the abscissa are the
#' average gene expression (log scaled). Also marked are the given *threshold*
#' (in red) and the \eqn{50\%} and \eqn{75\%} quantiles (in blue).
#'
#' @importFrom ggplot2 ggplot
#' @importFrom ggplot2 geom_point
#' @importFrom ggplot2 geom_tile
#' @importFrom ggplot2 geom_hline
#' @importFrom ggplot2 aes
#' @importFrom ggplot2 ggtitle
#' @importFrom ggplot2 xlab
#' @importFrom ggplot2 ylab
#' @importFrom ggplot2 scale_color_manual
#' @importFrom ggplot2 scale_fill_manual
#'
#' @importFrom ggrepel geom_text_repel
#' @importFrom ggrepel geom_label_repel
#'
#' @importFrom stats quantile
#'
#' @importFrom rlang set_names
#' @importFrom rlang is_empty
#'
#' @export
#'
#' @rdname UniformClusters
#'
GDIPlot <- function(objCOTAN, genes, condition = "",
statType = "S", GDIThreshold = 1.43,
GDIIn = NULL) {
logThis("GDI plot", logLevel = 2L)
if (!is_empty(GDIIn)) {
GDIDf <- GDIIn
} else {
gdi <- getGDI(objCOTAN)
if (!is_empty(gdi) && statType == "S") {
# complete the GDIDf
GDIDf <- data.frame()
GDIDf <- setColumnInDF(GDIDf, colToSet = gdi, colName = "GDI",
rowNames = getGenes(objCOTAN))
sumRawNorm <- log(rowSums(getNuNormData(objCOTAN)))
GDIDf <- setColumnInDF(GDIDf, colToSet = sumRawNorm,
colName = "sum.raw.norm")
} else {
GDIDf <- calculateGDI(objCOTAN, statType = statType)
}
}
GDIDf[["colors"]] <- "none"
for (n in names(genes)) {
genesSelec <- rownames(GDIDf) %in% genes[[n]]
if (any(genesSelec)) {
GDIDf[genesSelec, "colors"] <- n
} else {
logThis(paste("GDIPlot - none of the genes in group", n,
"is present: will be ignored"), logLevel = 1L)
}
}
# drop fully-expressed genes i.e. those that have GDI <= -5.0
{
genesToKeep <- (GDIDf[["GDI"]] > -5.0)
logThis(paste("Removed", sum(!genesToKeep), "low GDI genes",
"(such as the fully-expressed) in GDI plot"), logLevel = 1L)
GDIDf <- GDIDf[genesToKeep, ]
}
myColours <- set_names(getColorsVector(length(names(genes))), names(genes))
labelledGenes <- GDIDf[["colors"]] != "none"
if (isEmptyName(condition)) {
condition <- getMetadataElement(objCOTAN, datasetTags()[["cond"]])
}
title <- paste0("GDI plot - ", condition)
plot <- ggplot(subset(GDIDf, colors == "none"),
aes(x = sum.raw.norm, y = GDI)) +
geom_point(alpha = 0.3, color = "#8491B4B2", size = 2.5) +
geom_point(data = subset(GDIDf, colors != "none"),
aes(x = sum.raw.norm, y = GDI, colour = colors),
size = 2.5, alpha = 0.8) +
geom_hline(yintercept = quantile(GDIDf[["GDI"]])[[4L]],
linetype = "dashed", color = "darkblue") +
geom_hline(yintercept = quantile(GDIDf[["GDI"]])[[3L]],
linetype = "dashed", color = "darkblue") +
geom_hline(yintercept = GDIThreshold, linetype = "dotted",
color = "red", linewidth = 0.5) +
scale_color_manual("Status", values = myColours) +
scale_fill_manual( "Status", values = myColours) +
xlab("log normalized counts") +
ylab("GDI") +
geom_label_repel(data = GDIDf[labelledGenes, ],
aes(x = sum.raw.norm, y = GDI, fill = colors),
label = rownames(GDIDf)[labelledGenes],
label.size = NA, max.overlaps = 40L, alpha = 0.8,
direction = "both", na.rm = TRUE, seed = 1234L) +
ggtitle(title) +
plotTheme("GDI", textSize = 10L)
return(plot)
}
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