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Use ggplot2 for plotEISA(), add identity line and set aspect ratio to 1
1 parent 99cab33 commit 1966bae

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Lines changed: 81 additions & 88 deletions

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‎DESCRIPTION‎

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Original file line numberDiff line numberDiff line change
@@ -41,7 +41,6 @@ Suggests:
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withr
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VignetteBuilder: knitr
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Imports:
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graphics,
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stats,
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GenomicRanges,
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S4Vectors,
@@ -51,7 +50,9 @@ Imports:
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methods,
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SummarizedExperiment,
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BiocGenerics,
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utils
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utils,
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ggplot2,
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rlang
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URL: https://github.com/fmicompbio/eisaR
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BugReports: https://github.com/fmicompbio/eisaR/issues
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Config/roxygen2/version: 8.0.0

‎NAMESPACE‎

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Original file line numberDiff line numberDiff line change
@@ -26,11 +26,19 @@ importFrom(S4Vectors,split)
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importFrom(SummarizedExperiment,SummarizedExperiment)
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importFrom(SummarizedExperiment,assay)
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importFrom(SummarizedExperiment,assayNames)
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importFrom(graphics,legend)
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importFrom(graphics,plot)
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importFrom(graphics,points)
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importFrom(ggplot2,aes)
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importFrom(ggplot2,coord_fixed)
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importFrom(ggplot2,element_blank)
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importFrom(ggplot2,geom_abline)
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importFrom(ggplot2,geom_point)
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importFrom(ggplot2,ggplot)
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importFrom(ggplot2,labs)
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importFrom(ggplot2,scale_color_manual)
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importFrom(ggplot2,theme)
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importFrom(ggplot2,theme_bw)
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importFrom(limma,nonEstimable)
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importFrom(methods,is)
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importFrom(rlang,.data)
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importFrom(stats,model.matrix)
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importFrom(stats,setNames)
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importFrom(utils,write.table)

‎R/plotEISA.R‎

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Original file line numberDiff line numberDiff line change
@@ -1,52 +1,56 @@
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#' @title Visualize the results from an exon-intron split analysis.
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#'
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#' @description \code{plotEISA} takes the return value from \code{\link{runEISA}}
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#' and generates a scatterplot of intronic versus exonic changes.
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#' and generates a scatterplot of intronic versus exonic changes.
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#'
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#' @author Michael Stadler
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#' @author Michael Stadler, Charlotte Soneson
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#'
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#' @param x \code{list} with EISA results, typically the return value from \code{\link{runEISA}}
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#' @param x \code{list} with EISA results, typically the return value from
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#' \code{\link{runEISA}}
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#' @param contrast one of \code{"ExIn"} or \code{"none"}. If \code{"ExIn"}
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#' (the default), genes that significantly differ between exonic and intronic changes
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#' are highlighted. \code{"none"} turns off gene highlighting.
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#' (the default), genes that significantly differ between exonic and
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#' intronic changes are highlighted. \code{"none"} turns off gene
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#' highlighting.
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#' @param minLfc \code{NULL} or \code{numeric(1)} with the minimal absolute log2
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#' fold change to color a gene. If \code{NULL} (the default), no fold changes
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#' are not used to select genes for highlighting.
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#' fold change to color a gene. If \code{NULL} (the default), no fold changes
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#' are not used to select genes for highlighting.
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#' @param maxFDR \code{numeric(1)} with maximal false discovery rate for gene
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#' highlighting.
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#' highlighting.
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#' @param genecolors Vector of length three specifying the colors to use for
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#' genes that are significantly up, down or unchanged.
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#' @param ... further arguments past to \code{plot()}. Parameters that will be set
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#' automatically unless given in the arguments are:\describe{
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#' \item{pch}{: plot symbol (default: \code{"."})}
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#' \item{cex}{: plot symbol expansion factor (default: \code{2})}
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#' \item{col}{: plot symbol color (default: according to \code{contrast} and \code{genecolors})}
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#' \item{xlab/ylab}{: axis labels}
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#' }
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#' genes that are significantly up, down or unchanged.
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#'
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#' @return `NULL` (invisibly)
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#' @return A \code{ggplot} object.
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#'
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#' @examples
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#' # see the help for runEISA() for a full example
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#'
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#' @importFrom graphics plot legend points
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#' @importFrom ggplot2 ggplot aes geom_abline geom_point coord_fixed
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#' @importFrom ggplot2 scale_color_manual labs theme_bw theme element_blank
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#' @importFrom rlang .data
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#'
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#' @export
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plotEISA <- function(x, contrast = c("ExIn", "none"),
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minLfc = NULL, maxFDR = 0.05,
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genecolors = c("#E41A1C", "#497AB3", "#222222"), ...) {
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genecolors = c("#E41A1C", "#497AB3", "#222222")) {
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# check arguments
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contrast <- match.arg(contrast)
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contrastName <- ifelse("contrastName" %in% names(x),
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paste0(" (", x$contrastName, ")"), "")
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sigtab <- switch(contrast, ExIn = x$tab.ExIn, none = data.frame())
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if (nrow(sigtab) == 0L && contrast != "none")
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stop("'x' does not contain the requested statistics and can only ",
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"be plotted using contrast = 'none'. Note that at least two ",
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"replicates per condition are required to run the statistical ",
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"testing.")
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if (is.null(minLfc))
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if (contrast != "none") {
41+
if (nrow(sigtab) == 0L) {
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stop("'x' does not contain the requested statistics and can only ",
43+
"be plotted using contrast = 'none'. Note that at least two ",
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"replicates per condition are required to run the statistical ",
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"testing.")
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}
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if (nrow(sigtab) != nrow(x$contrasts)) {
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stop("x$tab.ExIn and x$contrasts have different numbers of rows.")
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}
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}
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if (is.null(minLfc)) {
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minLfc <- 0.0
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}
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stopifnot(exprs = {
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is.numeric(minLfc)
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length(minLfc) == 1L
@@ -58,47 +62,32 @@ plotEISA <- function(x, contrast = c("ExIn", "none"),
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length(genecolors) == 3L
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})
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# identify gene to highlight
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if (contrast == "none") {
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sig <- rep(FALSE, nrow(x$contrasts))
64-
sigDir <- 0.0
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} else {
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sig <- abs(sigtab$logFC) >= minLfc & sigtab$FDR <= maxFDR
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sigDir <- sign(sigtab$logFC[sig])
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message("identified ", sum(sig), " genes to highlight")
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}
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# set graphical parameters (user-defined colors take precedence)
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dotsL <- list(...)
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dotsL$x <- x$contrasts[, "Din"]
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dotsL$y <- x$contrasts[, "Dex"]
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if (!"pch" %in% names(dotsL))
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dotsL$pch <- "."
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if (!"cex" %in% names(dotsL))
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dotsL$cex <- 2L
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if (!"col" %in% names(dotsL))
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dotsL$col <- ifelse(sig,
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ifelse(sigtab$logFC > 0.0,
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genecolors[1L], genecolors[2L]),
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genecolors[3L])
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if (!"xlab" %in% names(dotsL))
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dotsL$xlab <- substitute(expression(paste(Delta, "intron", cn)),
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list(cn = contrastName))
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if (!"ylab" %in% names(dotsL))
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dotsL$ylab <- substitute(expression(paste(Delta, "exon", cn)),
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list(cn = contrastName))
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91-
# Delta I vs. Delta E
92-
do.call(plot, dotsL)
65+
# identify genes to highlight
66+
plotdf <- as.data.frame(x$contrasts)
67+
plotdf$sig <- ""
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if (contrast != "none") {
94-
if (length(dotsL$col) == 1L)
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dotsL$col <- rep(dotsL$col, length(dotsL$x))
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points(dotsL$x[sig], dotsL$y[sig], pch = 20L, col = dotsL$col[sig])
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legend(x = "bottomright", bty = "n", pch = 20L, col = genecolors[c(1L, 2L)],
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legend = sprintf("%s (%d)",
99-
c("Up", "Down"),
100-
c(sum(sigDir == 1.0), sum(sigDir == -1.0))))
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plotdf$sig <- ifelse(
70+
sigtab$logFC >= minLfc & sigtab$FDR <= maxFDR,
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paste0("Up (", sum(sigtab$logFC >= minLfc & sigtab$FDR <= maxFDR), ")"),
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ifelse(sigtab$logFC <= -minLfc & sigtab$FDR <= maxFDR,
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paste0("Down (", sum(sigtab$logFC <= -minLfc & sigtab$FDR <= maxFDR), ")"),
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""))
75+
plotdf$sig <- factor(plotdf$sig, levels = rev(sort(unique(plotdf$sig))))
76+
message("identified ", sum(plotdf$sig != ""), " genes to highlight")
10177
}
102-
103-
return(invisible(NULL))
78+
79+
ggplot(plotdf, aes(x = .data$Din, y = .data$Dex)) +
80+
geom_abline(slope = 1, intercept = 0, linetype = "dashed",
81+
color = "grey") +
82+
geom_point(size = 0.1, color = genecolors[3L]) +
83+
geom_point(data = plotdf[plotdf$sig != "", ], aes(color = .data$sig)) +
84+
coord_fixed() +
85+
scale_color_manual(values = genecolors[seq_len(2)], name = "") +
86+
labs(x = substitute(Delta * "intron" ~ x, list(x = contrastName)),
87+
y = substitute(Delta * "exon" ~ x, list(x = contrastName))) +
88+
theme_bw(base_size = 14) +
89+
theme(panel.grid = element_blank(),
90+
legend.position = "inside",
91+
legend.position.inside = c(0.97, 0.03),
92+
legend.justification = c(1, 0))
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}

‎man/plotEISA.Rd‎

Lines changed: 8 additions & 15 deletions
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‎tests/testthat/test_plotEISA.R‎

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@@ -13,8 +13,10 @@ test_that("plotEISA() runs", {
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tf <- tempfile(fileext = ".pdf")
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pdf(file = tf)
1515

16-
expect_null(plotEISA(res1, col = "purple"))
17-
expect_null(plotEISA(res1, contrast = "none"))
16+
expect_true(ggplot2::is_ggplot(
17+
plotEISA(res1, genecolors = c("orange", "yellow", "purple"))))
18+
expect_true(ggplot2::is_ggplot(
19+
plotEISA(res1, contrast = "none")))
1820
expect_error(plotEISA(res2))
1921

2022
dev.off()

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