Nothing
R/CorrelationMethods.R
In CAGEr: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining
Defines functions
.plotCorrelation2
corVector
.applyThreshold
#' @include CAGEr.R
#' @name plotCorrelation
#'
#' @title Pairwise scatter plots and correlations of CAGE signal
#'
#' @description Calculates the pairwise correlation between samples and creates
#' a plot matrix showing the correlation coeficients in the upper triangle, the
#' sample names in the diagonal, and the catter plots in the lower triangle.
#'
#' @param object A \code{\link{CAGEr}} object or (only for
#' \code{plotCorrelation2}) a \code{\link{SummarizedExperiment}} or an
#' expression table as a \code{\link{DataFrame}}, \code{\link{data.frame}} or
#' \code{\link{matrix}} object.
#'
#' @param what The clustering level to be used for plotting and calculating
#' correlations. Can be either \code{"CTSS"} to use individual TSSs or
#' \code{"consensusClusters"} to use consensus clusters, \emph{i.e.} entire
#' promoters. Ignored for anything else than \code{CAGEr} objects.
#'
#' @param values Use either \code{"raw"} (default) or \code{"normalized"} CAGE
#' signal. Ignored for plain expression tables.
#'
#' @param samples Character vector indicating which samples to use. Can be
#' either \code{"all"} to select all samples in a \code{CAGEr} object, or a
#' subset of valid sample labels as returned by the
#' \code{\link{sampleLabels}} function.
#'
#' @param method A character string indicating which correlation coefficient
#' should be computed. Passed to \code{cor} function. Can be one of
#' \code{"pearson"}, \code{"spearman"}, or \code{"kendall"}.
#'
#' @param tagCountThreshold Only TSSs with tag count \code{>= tagCountThreshold}
#' in either one (\code{applyThresholdBoth = FALSE}) or both samples
#' (\code{applyThresholdBoth = TRUE}) are plotted and used to calculate
#' correlation.
#'
#' @param applyThresholdBoth See \code{tagCountThreshold} above.
#'
#' @param plotSize Size of the individual comparison plot in pixels - the
#' total size of the resulting png will be \code{length(samples) * plotSize}
#' in both dimensions. Ignored in \code{plotCorrelation2}.
#'
#' @details In the scatter plots, a pseudo-count equal to half the lowest score
#' is added to the null values so that they can appear despite logarithmic scale.
#'
#' \code{SummarizedExperiment} objects are expected to contain raw tag counts
#' in a \dQuote{counts} assay and the normalized expression scores in a
#' \dQuote{normalized} assay.
#'
#' Avoid using large \code{matrix} objects as they are coerced to
#' \code{DataFrame} class without special care for efficiency.
#'
#' @return Displays the plot and returns a \code{matrix} of pairwise
#' correlations between selected samples. The scatterplots of
#' \code{plotCorrelation} are colored according to the density of points, and
#' in \code{plotCorrelation2} they are just black and white, which is much
#' faster to plot. Note that while the scatterplots are on a logarithmic scale
#' with pseudocount added to the zero values, the correlation coefficients are
#' calculated on untransformed (but thresholded) data.
#'
#' @author Vanja Haberle
#' @author Charles Plessy
#'
#' @family CAGEr plot functions
#'
#' @importFrom graphics axis
#' @importFrom S4Vectors decode
#'
#' @examples
#'
#' plotCorrelation2(exampleCAGEexp, what = "consensusClusters", value = "normalized")
#'
#' @export
setGeneric( "plotCorrelation"
, function( object, what = c("CTSS", "consensusClusters")
, values = c("raw", "normalized")
, samples = "all", method = "pearson"
, tagCountThreshold = 1, applyThresholdBoth = FALSE, plotSize=800)
standardGeneric("plotCorrelation"))
#' @rdname plotCorrelation
setMethod( "plotCorrelation", "CAGEr"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth, plotSize) {
what <- match.arg(what)
values <- match.arg(values)
if (what == "CTSS" & values == "raw")
tag.count <- CTSStagCountDF(object)
if (what == "CTSS" & values == "normalized")
tag.count <- CTSSnormalizedTpmDF(object)
if (what == "consensusClusters" & values == "raw")
stop("Raw consensus clusters not supported yet.")
if (what == "consensusClusters" & values == "normalized")
tag.count <- consensusClustersTpm(object)
if(all(samples %in% sampleLabels(object))){
tag.count <- tag.count[,samples]
nr.samples <- length(samples)
}else if(samples == "all"){
samples <- sampleLabels(object)
nr.samples <- length(samples)
}else{
stop("'samples' parameter must be either \"all\" or a character vector of valid sample labels!")
}
corr.m <- matrix(rep(1, (nr.samples)^2), nrow = nr.samples)
colnames(corr.m) <- samples
rownames(corr.m) <- samples
old.par <- par(mfrow = c(nr.samples, nr.samples), mai = c(0.05,0.05,0.05,0.05), omi = c(0.05*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360,0.1*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360,0,0))
on.exit(par(old.par))
for(i in c(1:nr.samples)){
for(j in c(1:nr.samples)){
if(i == j){
plot(1, 1, type = "n", bty = "n", xlim = c(0,1), ylim = c(0,1), axes = F)
text(0.5, 0.5, samples[i], cex = 0.98/max(sapply(samples, strwidth)))
box(lwd = 3)
}else {
x <- tag.count[,samples[j]]
y <- tag.count[,samples[i]]
if(applyThresholdBoth){
idx <- (x >= tagCountThreshold) & (y >= tagCountThreshold)
}else{
idx <- (x >= tagCountThreshold) | (y >= tagCountThreshold)
}
x <- x[idx]
y <- y[idx]
if (j > i) {
pairwise.cor <- cor(x = decode(x), y = decode(y), method = method) # decode() in case of Rle.
plot(1, 1, type = "n", bty = "n", xlim = c(0,1), ylim = c(0,1), axes = F)
txt <- sprintf("%.2f", pairwise.cor)
txt.abs <- sprintf("%.2f", abs(pairwise.cor))
text(0.5, 0.5, txt, cex = 1.5 + 0.5/strwidth(txt.abs) * abs(pairwise.cor))
box(lwd = 3)
corr.m[i,j] <- pairwise.cor
corr.m[j,i] <- pairwise.cor
}else{
.mySmoothScatter(x = log10(x+1), y = log10(y+1), xlim = c(0, 3), ylim = c(0,3), nrpoints = 0, nbin = c(plotSize, plotSize), bandwidth = c(3/plotSize * 5, 3/plotSize * 5), transformation = function(x) x^(1/6), axes = F)
if(i == nr.samples & j < nr.samples){
if((nr.samples <= 3) | ((nr.samples > 3) & (j%%2 == 1))){
axis(side = 1, at = seq(0,3), labels = 10^seq(0,3), cex.axis = 0.1*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360/(4*strwidth("1")))
}else{
axis(side = 1, at = seq(0,3), labels = rep("", 4))
}
}
if(j == 1 & i > 1){
if((nr.samples <= 3) | ((nr.samples > 3) & ((i - nr.samples)%%2 == 0))){
axis(side = 2, at = seq(0,3), labels = 10^seq(0,3), las = 2, cex.axis = 0.1*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360/(4*strwidth("1")))
}else{
axis(side = 2, at = seq(0,3), labels = rep("", 4))
}
}
box(lwd = 3)
}
# fit.lm <- lm(y ~ x)
# .mySmoothScatter(x = x, y = y, xlim = c(0, 200), ylim = c(0,200), nrpoints = 0, nbin = c(800, 800), bandwidth = c(3, 3), transformation = function(x) x^(1/9), axes = F)
# lines(x = c(0,10,100,1000), y = coefficients(fit.lm)[2]*c(0,10,100,1000) + coefficients(fit.lm)[1], col = "red3", lwd = 3)
}
}
}
return(corr.m)
})
#' @rdname plotCorrelation
#'
#' @param digits The number of significant digits for the data to be kept in log
#' scale. Ignored in \code{plotCorrelation}. In \code{plotCorrelation2}, the
#' number of points plotted is considerably reduced by rounding the point
#' coordinates to a small number of significant digits before removing
#' duplicates. Chose a value that makes the plot visually indistinguishable
#' with non-deduplicated data, by making tests on a subset of the data.
#'
#' @details \code{plotCorrelation2} speeds up the plotting by a) deduplicating
#' that data: no point is plot twice at the same coordinates, b) rounding the
#' data so that indistinguishable positions are plotted only once, c) using a
#' black square glyph for the points, d) caching some calculations that are
#' made repeatedly (to determine where to plot the correlation coefficients),
#' and e) preventing coercion of \code{DataFrames} to \code{data.frames}.
#'
#' @importFrom memoise memoise
#' @export
setGeneric( "plotCorrelation2"
, function( object, what = c("CTSS", "consensusClusters")
, values = c("raw", "normalized")
, samples = "all", method = "pearson"
, tagCountThreshold = 1, applyThresholdBoth = FALSE
, digits = 3)
standardGeneric("plotCorrelation2"))
#' @importFrom graphics pairs
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "CAGEset"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
what <- match.arg(what)
values <- match.arg(values)
if (what == "CTSS" & values == "raw")
expr.table <- CTSStagCountDf(object)
if (what == "CTSS" & values == "normalized")
expr.table <- CTSSnormalizedTpmDf(object)
if (what == "consensusClusters" & values == "raw")
stop("Raw consensus clusters not supported yet.")
if (what == "consensusClusters" & values == "normalized")
expr.table <- consensusClustersTpm(object)
plotCorrelation2( expr.table
, what = what
, values = values
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "CAGEexp"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
what <- match.arg(what)
se <- switch( what
, CTSS = CTSStagCountSE(object)
, consensusClusters = consensusClustersSE(object)
, genes = GeneExpSE(object)
, stop("Unsupported value for ", dQuote("what"), ": ", what))
plotCorrelation2( se
, what = what
, values = values
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "SummarizedExperiment"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
values <- match.arg(values)
if (values == "raw") {
if ("counts" %in% assayNames(object)) {
values <- "counts"
} else {
stop( "Could not find a ", dQuote("counts"), " assay for the "
, dQuote(what), " clustering level")
}
} else if (values == "normalized") {
if ("normalized" %in% assayNames(object)) {
values <- "normalized"
} else if ("normalizedTpmMatrix" %in% assayNames(object)) {
values <- "normalizedTpmMatrix"
} else {
stop( "Could not find a ", dQuote("normalized"), " assay for the "
, dQuote(what), " clustering level")
}
}
plotCorrelation2( assay(object, values)
, what = what
, values = values
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "DataFrame"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
.plotCorrelation2( object
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "data.frame"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
.plotCorrelation2( object
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "matrix"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
.plotCorrelation2( as.data.frame(object)
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
# Helper function to apply threshold pairwise
.applyThreshold <- function(df, tagCountThreshold, applyThresholdBoth) {
if (applyThresholdBoth) {
idx <- (df[[1]] >= tagCountThreshold) & (df[[2]] >= tagCountThreshold)
} else {
idx <- (df[[1]] >= tagCountThreshold) | (df[[2]] >= tagCountThreshold)
}
df[idx,]
}
# Helper function to Pre-calculate a vector of correlation coefficients
corVector <- function(expr.table, method, tagCountThreshold, applyThresholdBoth) {
corTreshold <- function(x, y, method) {
df <- data.frame(x, y)
df <- .applyThreshold(df, tagCountThreshold, applyThresholdBoth)
cor(x = df$x, y = df$y, method = method)
}
nr.samples <- ncol(expr.table)
corr.v <- numeric()
for (i in 1:(nr.samples-1)) {
for (j in (min(i+1, nr.samples)):nr.samples) {
corr.v <- append(corr.v, corTreshold(expr.table[[i]], expr.table[[j]], method))
}
}
corr.v
}
#' @importFrom grDevices dev.flush dev.hold
#' @importFrom graphics Axis mtext
# Re-implement the pairs function to prevent coercion to data.frame
pairs.DataFrame <- function (x, labels, panel = points, ..., horInd = 1:nc, verInd = 1:nc,
lower.panel = panel, upper.panel = panel, diag.panel = NULL,
text.panel = textPanel, label.pos = 0.5 + has.diag/3, line.main = 3,
cex.labels = NULL, font.labels = 1, row1attop = TRUE, gap = 1,
log = "")
{
if (doText <- missing(text.panel) || is.function(text.panel))
textPanel <- function(x = 0.5, y = 0.5, txt, cex, font) text(x,
y, txt, cex = cex, font = font)
localAxis <- function(side, x, y, xpd, bg, col = NULL, main,
oma, ...) {
xpd <- NA
if (side%%2L == 1L && xl[j])
xpd <- FALSE
if (side%%2L == 0L && yl[i])
xpd <- FALSE
if (side%%2L == 1L)
Axis(x, side = side, xpd = xpd, ...)
else Axis(y, side = side, xpd = xpd, ...)
}
localPlot <- function(..., main, oma, font.main, cex.main) plot(...)
localLowerPanel <- function(..., main, oma, font.main, cex.main) lower.panel(...)
localUpperPanel <- function(..., main, oma, font.main, cex.main) upper.panel(...)
localDiagPanel <- function(..., main, oma, font.main, cex.main) diag.panel(...)
dots <- list(...)
nmdots <- names(dots)
# if (!is.matrix(x)) {
# x <- as.data.frame(x)
# for (i in seq_along(names(x))) {
# if (is.factor(x[[i]]) || is.logical(x[[i]]))
# x[[i]] <- as.numeric(x[[i]])
# if (!is.numeric(unclass(x[[i]])))
# stop("non-numeric argument to 'pairs'")
# }
# }
# else if (!is.numeric(x))
# stop("non-numeric argument to 'pairs'")
panel <- match.fun(panel)
if ((has.lower <- !is.null(lower.panel)) && !missing(lower.panel))
lower.panel <- match.fun(lower.panel)
if ((has.upper <- !is.null(upper.panel)) && !missing(upper.panel))
upper.panel <- match.fun(upper.panel)
if ((has.diag <- !is.null(diag.panel)) && !missing(diag.panel))
diag.panel <- match.fun(diag.panel)
if (row1attop) {
tmp <- lower.panel
lower.panel <- upper.panel
upper.panel <- tmp
tmp <- has.lower
has.lower <- has.upper
has.upper <- tmp
}
nc <- ncol(x)
if (nc < 2L)
stop("only one column in the argument to 'pairs'")
if (!all(horInd >= 1L & horInd <= nc))
stop("invalid argument 'horInd'")
if (!all(verInd >= 1L & verInd <= nc))
stop("invalid argument 'verInd'")
if (doText) {
if (missing(labels)) {
labels <- colnames(x)
if (is.null(labels))
labels <- paste("var", 1L:nc)
}
else if (is.null(labels))
doText <- FALSE
}
oma <- if ("oma" %in% nmdots)
dots$oma
main <- if ("main" %in% nmdots)
dots$main
if (is.null(oma))
oma <- c(4, 4, if (!is.null(main)) 6 else 4, 4)
opar <- par(mfrow = c(length(horInd), length(verInd)), mar = rep.int(gap/2,
4), oma = oma)
on.exit(par(opar))
dev.hold()
on.exit(dev.flush(), add = TRUE)
xl <- yl <- logical(nc)
if (is.numeric(log))
xl[log] <- yl[log] <- TRUE
else {
xl[] <- grepl("x", log)
yl[] <- grepl("y", log)
}
for (i in if (row1attop)
verInd
else rev(verInd)) for (j in horInd) {
l <- paste0(ifelse(xl[j], "x", ""), ifelse(yl[i], "y",
""))
localPlot(x[, j], x[, i], xlab = "", ylab = "", axes = FALSE,
type = "n", ..., log = l)
if (i == j || (i < j && has.lower) || (i > j && has.upper)) {
box()
if (i == 1 && (!(j%%2L) || !has.upper || !has.lower))
localAxis(1L + 2L * row1attop, x[, j], x[, i],
...)
if (i == nc && (j%%2L || !has.upper || !has.lower))
localAxis(3L - 2L * row1attop, x[, j], x[, i],
...)
if (j == 1 && (!(i%%2L) || !has.upper || !has.lower))
localAxis(2L, x[, j], x[, i], ...)
if (j == nc && (i%%2L || !has.upper || !has.lower))
localAxis(4L, x[, j], x[, i], ...)
mfg <- par("mfg")
if (i == j) {
if (has.diag)
localDiagPanel(x[, i], ...)
if (doText) {
par(usr = c(0, 1, 0, 1))
if (is.null(cex.labels)) {
l.wid <- strwidth(labels, "user")
cex.labels <- max(0.8, min(2, 0.9/max(l.wid)))
}
xlp <- if (xl[i])
10^0.5
else 0.5
ylp <- if (yl[j])
10^label.pos
else label.pos
text.panel(xlp, ylp, labels[i], cex = cex.labels,
font = font.labels)
}
}
else if (i < j)
localLowerPanel(x[, j], x[, i], ...)
else localUpperPanel(x[, j], x[, i], ...)
if (any(par("mfg") != mfg))
stop("the 'panel' function made a new plot")
}
else par(new = FALSE)
}
if (!is.null(main)) {
font.main <- if ("font.main" %in% nmdots)
dots$font.main
else par("font.main")
cex.main <- if ("cex.main" %in% nmdots)
dots$cex.main
else par("cex.main")
mtext(main, 3, line.main, outer = TRUE, at = 0.5, cex = cex.main,
font = font.main)
}
invisible(NULL)
}
# The function that runs the actual work of calculating correlations and
# plotting expression values.
.plotCorrelation2 <- function( expr.table, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
# Select samples
if (all(samples %in% colnames(expr.table))) {
expr.table <- expr.table[,samples]
} else if(samples == "all"){
samples <- colnames(expr.table)
} else stop("'samples' parameter must be either \"all\" or a character vector of valid sample labels!")
nr.samples <- length(samples)
# Pre-calculate a vector of correlation coefficients
corr.v <- corVector(expr.table, method, tagCountThreshold, applyThresholdBoth)
# Add pseudocount to null values so that the plot axes are correctly set.
pseudocount <- min(sapply(expr.table, function(x) min(x[x>0]))) / 2
expr.table <- DataFrame(lapply( expr.table
, function(x) {x[x==0] <- pseudocount ; x}))
# This closure retreives correlation coefficients one after the other.
mkPanelCor <- function() {
i <- 1
function(x, y, digits=2, prefix="", cex.cor, ...) {
r <- corr.v[i]
i <<- i + 1
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep="")
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
gmean <- memoise(function(x) {
exp(mean(log(c(pseudocount, max(x)))))
})
text(gmean(x), gmean(y), txt, cex = cex.cor * sqrt(r))
}
}
panel.cor <- mkPanelCor()
# uniqueSignif returns a plain data.frame, because compression is already
# maximal in this context. See benchmarking of alternatives algorithms
# in the file "benchmarks/unique-signif.md" in the CAGEr's Git repository.
uniqueSignif <- function(x, y, digits = 0, log = c("", "xy")) {
log <- match.arg(log)
if (log == "xy") {x <- log1p(x) ; y <- log1p(y)}
u <- unique(Rle(complex( real = decode(signif(x, digits = digits))
, imaginary = decode(signif(y, digits = digits)))))
df <- data.frame(x = Re(u), y = Im(u))
if (log == "xy") df <- expm1(df)
df
}
# Thresholds are lowered of a minute amont because the rounding in the log
# scale in uniqueSignif adds minute errors to values that were already round.
pointsUnique <- function(x,y,...) {
df <- uniqueSignif(x, y, digits = digits, log = "xy")
df <- .applyThreshold(df, tagCountThreshold * 0.999, applyThresholdBoth)
df <- df[rowSums(df) > pseudocount * 1.999,] # Remove the (0,0) point.
points(df, ...)
}
pairs( expr.table
, lower.panel = pointsUnique
, upper.panel = panel.cor
, pch = "."
, cex = 4
, log = "xy"
, las = 1
, xaxp = c(1,10,1)
, yaxp = c(1,10,1)
, labels = samples)
# Return a correlation matrix
corr.m <- matrix(1, nr.samples, nr.samples)
colnames(corr.m) <- samples
rownames(corr.m) <- samples
corr.m[lower.tri(corr.m)] <- corr.v
corr.m[upper.tri(corr.m)] <- t(corr.m)[upper.tri(corr.m)]
corr.m
}
# Vanja's version of smooth scatter that allows passing range.x argument to grDevices:::.smoothScatterCalcDensity function to calculate 2D kernel smoothed density
#' @importFrom grDevices blues9 colorRamp colorRampPalette xy.coords
#' @importFrom graphics points
#' @importFrom KernSmooth bkde2D
# Local copy of grDevices:::.smoothScatterCalcDensity,
# to avoid problems in case the original function is changed
# (since the original is private, we can not assume that changes maintain
# compatibility with existing code.)
grDevices.smoothScatterCalcDensity <- function (x, nbin, bandwidth, range.x)
{
if (length(nbin) == 1)
nbin <- c(nbin, nbin)
if (!is.numeric(nbin) || length(nbin) != 2)
stop("'nbin' must be numeric of length 1 or 2")
if (missing(bandwidth)) {
bandwidth <- diff(apply(x, 2, stats::quantile, probs = c(0.05,
0.95), na.rm = TRUE, names = FALSE))/25
bandwidth[bandwidth == 0] <- 1
}
else {
if (!is.numeric(bandwidth))
stop("'bandwidth' must be numeric")
if (any(bandwidth <= 0))
stop("'bandwidth' must be positive")
}
rv <- KernSmooth::bkde2D(x, bandwidth = bandwidth, gridsize = nbin,
range.x = range.x)
rv$bandwidth <- bandwidth
rv
}
.mySmoothScatter <- function (x, y = NULL, nbin = 128, bandwidth, colramp = colorRampPalette(c("white",
blues9)), nrpoints = 100, pch = ".", cex = 1, col = "black",
transformation = function(x) x^0.25, postPlotHook = box,
xlab = NULL, ylab = NULL, xlim, ylim, xaxs = par("xaxs"),
yaxs = par("yaxs"), ...)
{
if (!is.numeric(nrpoints) | (nrpoints < 0) | (length(nrpoints) !=
1))
stop("'nrpoints' should be numeric scalar with value >= 0.")
xlabel <- if (!missing(x))
deparse(substitute(x))
ylabel <- if (!missing(y))
deparse(substitute(y))
xy <- xy.coords(x, y, xlabel, ylabel)
xlab <- if (is.null(xlab))
xy$xlab
else xlab
ylab <- if (is.null(ylab))
xy$ylab
else ylab
x <- cbind(xy$x, xy$y)[is.finite(xy$x) & is.finite(xy$y),
, drop = FALSE]
if (!missing(xlim)) {
stopifnot(is.numeric(xlim), length(xlim) == 2, is.finite(xlim))
x <- x[min(xlim) <= x[, 1] & x[, 1] <= max(xlim), ]
}
else {
xlim <- range(x[, 1])
}
if (!missing(ylim)) {
stopifnot(is.numeric(ylim), length(ylim) == 2, is.finite(ylim))
x <- x[min(ylim) <= x[, 2] & x[, 2] <= max(ylim), ]
}
else {
ylim <- range(x[, 2])
}
map <- grDevices.smoothScatterCalcDensity(x, nbin, bandwidth, range.x = list(xlim = c(xlim[1] - 1.5*bandwidth[1], xlim[2] + 1.5*bandwidth[1]), ylim = c(ylim[1] - 1.5*bandwidth[2], ylim[2] + 1.5*bandwidth[2])))
xm <- map$x1
ym <- map$x2
dens <- map$fhat
dens[] <- transformation(dens)
image(xm, ym, z = dens, col = colramp(256), xlab = xlab,
ylab = ylab, xlim = xlim, ylim = ylim, xaxs = xaxs, yaxs = yaxs,
...)
if (!is.null(postPlotHook))
postPlotHook()
if (nrpoints > 0) {
nrpoints <- min(nrow(x), ceiling(nrpoints))
stopifnot((nx <- length(xm)) == nrow(dens), (ny <- length(ym)) ==
ncol(dens))
ixm <- 1L + as.integer((nx - 1) * (x[, 1] - xm[1])/(xm[nx] -
xm[1]))
iym <- 1L + as.integer((ny - 1) * (x[, 2] - ym[1])/(ym[ny] -
ym[1]))
sel <- order(dens[cbind(ixm, iym)])[seq_len(nrpoints)]
points(x[sel, ], pch = pch, cex = cex, col = col)
}
}
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Defines functions .plotCorrelation2 corVector .applyThreshold
#' @include CAGEr.R
#' @name plotCorrelation
#'
#' @title Pairwise scatter plots and correlations of CAGE signal
#'
#' @description Calculates the pairwise correlation between samples and creates
#' a plot matrix showing the correlation coeficients in the upper triangle, the
#' sample names in the diagonal, and the catter plots in the lower triangle.
#'
#' @param object A \code{\link{CAGEr}} object or (only for
#' \code{plotCorrelation2}) a \code{\link{SummarizedExperiment}} or an
#' expression table as a \code{\link{DataFrame}}, \code{\link{data.frame}} or
#' \code{\link{matrix}} object.
#'
#' @param what The clustering level to be used for plotting and calculating
#' correlations. Can be either \code{"CTSS"} to use individual TSSs or
#' \code{"consensusClusters"} to use consensus clusters, \emph{i.e.} entire
#' promoters. Ignored for anything else than \code{CAGEr} objects.
#'
#' @param values Use either \code{"raw"} (default) or \code{"normalized"} CAGE
#' signal. Ignored for plain expression tables.
#'
#' @param samples Character vector indicating which samples to use. Can be
#' either \code{"all"} to select all samples in a \code{CAGEr} object, or a
#' subset of valid sample labels as returned by the
#' \code{\link{sampleLabels}} function.
#'
#' @param method A character string indicating which correlation coefficient
#' should be computed. Passed to \code{cor} function. Can be one of
#' \code{"pearson"}, \code{"spearman"}, or \code{"kendall"}.
#'
#' @param tagCountThreshold Only TSSs with tag count \code{>= tagCountThreshold}
#' in either one (\code{applyThresholdBoth = FALSE}) or both samples
#' (\code{applyThresholdBoth = TRUE}) are plotted and used to calculate
#' correlation.
#'
#' @param applyThresholdBoth See \code{tagCountThreshold} above.
#'
#' @param plotSize Size of the individual comparison plot in pixels - the
#' total size of the resulting png will be \code{length(samples) * plotSize}
#' in both dimensions. Ignored in \code{plotCorrelation2}.
#'
#' @details In the scatter plots, a pseudo-count equal to half the lowest score
#' is added to the null values so that they can appear despite logarithmic scale.
#'
#' \code{SummarizedExperiment} objects are expected to contain raw tag counts
#' in a \dQuote{counts} assay and the normalized expression scores in a
#' \dQuote{normalized} assay.
#'
#' Avoid using large \code{matrix} objects as they are coerced to
#' \code{DataFrame} class without special care for efficiency.
#'
#' @return Displays the plot and returns a \code{matrix} of pairwise
#' correlations between selected samples. The scatterplots of
#' \code{plotCorrelation} are colored according to the density of points, and
#' in \code{plotCorrelation2} they are just black and white, which is much
#' faster to plot. Note that while the scatterplots are on a logarithmic scale
#' with pseudocount added to the zero values, the correlation coefficients are
#' calculated on untransformed (but thresholded) data.
#'
#' @author Vanja Haberle
#' @author Charles Plessy
#'
#' @family CAGEr plot functions
#'
#' @importFrom graphics axis
#' @importFrom S4Vectors decode
#'
#' @examples
#'
#' plotCorrelation2(exampleCAGEexp, what = "consensusClusters", value = "normalized")
#'
#' @export
setGeneric( "plotCorrelation"
, function( object, what = c("CTSS", "consensusClusters")
, values = c("raw", "normalized")
, samples = "all", method = "pearson"
, tagCountThreshold = 1, applyThresholdBoth = FALSE, plotSize=800)
standardGeneric("plotCorrelation"))
#' @rdname plotCorrelation
setMethod( "plotCorrelation", "CAGEr"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth, plotSize) {
what <- match.arg(what)
values <- match.arg(values)
if (what == "CTSS" & values == "raw")
tag.count <- CTSStagCountDF(object)
if (what == "CTSS" & values == "normalized")
tag.count <- CTSSnormalizedTpmDF(object)
if (what == "consensusClusters" & values == "raw")
stop("Raw consensus clusters not supported yet.")
if (what == "consensusClusters" & values == "normalized")
tag.count <- consensusClustersTpm(object)
if(all(samples %in% sampleLabels(object))){
tag.count <- tag.count[,samples]
nr.samples <- length(samples)
}else if(samples == "all"){
samples <- sampleLabels(object)
nr.samples <- length(samples)
}else{
stop("'samples' parameter must be either \"all\" or a character vector of valid sample labels!")
}
corr.m <- matrix(rep(1, (nr.samples)^2), nrow = nr.samples)
colnames(corr.m) <- samples
rownames(corr.m) <- samples
old.par <- par(mfrow = c(nr.samples, nr.samples), mai = c(0.05,0.05,0.05,0.05), omi = c(0.05*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360,0.1*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360,0,0))
on.exit(par(old.par))
for(i in c(1:nr.samples)){
for(j in c(1:nr.samples)){
if(i == j){
plot(1, 1, type = "n", bty = "n", xlim = c(0,1), ylim = c(0,1), axes = F)
text(0.5, 0.5, samples[i], cex = 0.98/max(sapply(samples, strwidth)))
box(lwd = 3)
}else {
x <- tag.count[,samples[j]]
y <- tag.count[,samples[i]]
if(applyThresholdBoth){
idx <- (x >= tagCountThreshold) & (y >= tagCountThreshold)
}else{
idx <- (x >= tagCountThreshold) | (y >= tagCountThreshold)
}
x <- x[idx]
y <- y[idx]
if (j > i) {
pairwise.cor <- cor(x = decode(x), y = decode(y), method = method) # decode() in case of Rle.
plot(1, 1, type = "n", bty = "n", xlim = c(0,1), ylim = c(0,1), axes = F)
txt <- sprintf("%.2f", pairwise.cor)
txt.abs <- sprintf("%.2f", abs(pairwise.cor))
text(0.5, 0.5, txt, cex = 1.5 + 0.5/strwidth(txt.abs) * abs(pairwise.cor))
box(lwd = 3)
corr.m[i,j] <- pairwise.cor
corr.m[j,i] <- pairwise.cor
}else{
.mySmoothScatter(x = log10(x+1), y = log10(y+1), xlim = c(0, 3), ylim = c(0,3), nrpoints = 0, nbin = c(plotSize, plotSize), bandwidth = c(3/plotSize * 5, 3/plotSize * 5), transformation = function(x) x^(1/6), axes = F)
if(i == nr.samples & j < nr.samples){
if((nr.samples <= 3) | ((nr.samples > 3) & (j%%2 == 1))){
axis(side = 1, at = seq(0,3), labels = 10^seq(0,3), cex.axis = 0.1*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360/(4*strwidth("1")))
}else{
axis(side = 1, at = seq(0,3), labels = rep("", 4))
}
}
if(j == 1 & i > 1){
if((nr.samples <= 3) | ((nr.samples > 3) & ((i - nr.samples)%%2 == 0))){
axis(side = 2, at = seq(0,3), labels = 10^seq(0,3), las = 2, cex.axis = 0.1*plotSize*(log2(nr.samples)/log2(3) + (2-1/log2(3)))/360/(4*strwidth("1")))
}else{
axis(side = 2, at = seq(0,3), labels = rep("", 4))
}
}
box(lwd = 3)
}
# fit.lm <- lm(y ~ x)
# .mySmoothScatter(x = x, y = y, xlim = c(0, 200), ylim = c(0,200), nrpoints = 0, nbin = c(800, 800), bandwidth = c(3, 3), transformation = function(x) x^(1/9), axes = F)
# lines(x = c(0,10,100,1000), y = coefficients(fit.lm)[2]*c(0,10,100,1000) + coefficients(fit.lm)[1], col = "red3", lwd = 3)
}
}
}
return(corr.m)
})
#' @rdname plotCorrelation
#'
#' @param digits The number of significant digits for the data to be kept in log
#' scale. Ignored in \code{plotCorrelation}. In \code{plotCorrelation2}, the
#' number of points plotted is considerably reduced by rounding the point
#' coordinates to a small number of significant digits before removing
#' duplicates. Chose a value that makes the plot visually indistinguishable
#' with non-deduplicated data, by making tests on a subset of the data.
#'
#' @details \code{plotCorrelation2} speeds up the plotting by a) deduplicating
#' that data: no point is plot twice at the same coordinates, b) rounding the
#' data so that indistinguishable positions are plotted only once, c) using a
#' black square glyph for the points, d) caching some calculations that are
#' made repeatedly (to determine where to plot the correlation coefficients),
#' and e) preventing coercion of \code{DataFrames} to \code{data.frames}.
#'
#' @importFrom memoise memoise
#' @export
setGeneric( "plotCorrelation2"
, function( object, what = c("CTSS", "consensusClusters")
, values = c("raw", "normalized")
, samples = "all", method = "pearson"
, tagCountThreshold = 1, applyThresholdBoth = FALSE
, digits = 3)
standardGeneric("plotCorrelation2"))
#' @importFrom graphics pairs
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "CAGEset"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
what <- match.arg(what)
values <- match.arg(values)
if (what == "CTSS" & values == "raw")
expr.table <- CTSStagCountDf(object)
if (what == "CTSS" & values == "normalized")
expr.table <- CTSSnormalizedTpmDf(object)
if (what == "consensusClusters" & values == "raw")
stop("Raw consensus clusters not supported yet.")
if (what == "consensusClusters" & values == "normalized")
expr.table <- consensusClustersTpm(object)
plotCorrelation2( expr.table
, what = what
, values = values
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "CAGEexp"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
what <- match.arg(what)
se <- switch( what
, CTSS = CTSStagCountSE(object)
, consensusClusters = consensusClustersSE(object)
, genes = GeneExpSE(object)
, stop("Unsupported value for ", dQuote("what"), ": ", what))
plotCorrelation2( se
, what = what
, values = values
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "SummarizedExperiment"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
values <- match.arg(values)
if (values == "raw") {
if ("counts" %in% assayNames(object)) {
values <- "counts"
} else {
stop( "Could not find a ", dQuote("counts"), " assay for the "
, dQuote(what), " clustering level")
}
} else if (values == "normalized") {
if ("normalized" %in% assayNames(object)) {
values <- "normalized"
} else if ("normalizedTpmMatrix" %in% assayNames(object)) {
values <- "normalizedTpmMatrix"
} else {
stop( "Could not find a ", dQuote("normalized"), " assay for the "
, dQuote(what), " clustering level")
}
}
plotCorrelation2( assay(object, values)
, what = what
, values = values
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "DataFrame"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
.plotCorrelation2( object
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "data.frame"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
.plotCorrelation2( object
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
#' @rdname plotCorrelation
setMethod( "plotCorrelation2", "matrix"
, function( object, what, values, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
.plotCorrelation2( as.data.frame(object)
, samples = samples
, method = method
, tagCountThreshold = tagCountThreshold
, applyThresholdBoth = applyThresholdBoth
, digits = digits)
})
# Helper function to apply threshold pairwise
.applyThreshold <- function(df, tagCountThreshold, applyThresholdBoth) {
if (applyThresholdBoth) {
idx <- (df[[1]] >= tagCountThreshold) & (df[[2]] >= tagCountThreshold)
} else {
idx <- (df[[1]] >= tagCountThreshold) | (df[[2]] >= tagCountThreshold)
}
df[idx,]
}
# Helper function to Pre-calculate a vector of correlation coefficients
corVector <- function(expr.table, method, tagCountThreshold, applyThresholdBoth) {
corTreshold <- function(x, y, method) {
df <- data.frame(x, y)
df <- .applyThreshold(df, tagCountThreshold, applyThresholdBoth)
cor(x = df$x, y = df$y, method = method)
}
nr.samples <- ncol(expr.table)
corr.v <- numeric()
for (i in 1:(nr.samples-1)) {
for (j in (min(i+1, nr.samples)):nr.samples) {
corr.v <- append(corr.v, corTreshold(expr.table[[i]], expr.table[[j]], method))
}
}
corr.v
}
#' @importFrom grDevices dev.flush dev.hold
#' @importFrom graphics Axis mtext
# Re-implement the pairs function to prevent coercion to data.frame
pairs.DataFrame <- function (x, labels, panel = points, ..., horInd = 1:nc, verInd = 1:nc,
lower.panel = panel, upper.panel = panel, diag.panel = NULL,
text.panel = textPanel, label.pos = 0.5 + has.diag/3, line.main = 3,
cex.labels = NULL, font.labels = 1, row1attop = TRUE, gap = 1,
log = "")
{
if (doText <- missing(text.panel) || is.function(text.panel))
textPanel <- function(x = 0.5, y = 0.5, txt, cex, font) text(x,
y, txt, cex = cex, font = font)
localAxis <- function(side, x, y, xpd, bg, col = NULL, main,
oma, ...) {
xpd <- NA
if (side%%2L == 1L && xl[j])
xpd <- FALSE
if (side%%2L == 0L && yl[i])
xpd <- FALSE
if (side%%2L == 1L)
Axis(x, side = side, xpd = xpd, ...)
else Axis(y, side = side, xpd = xpd, ...)
}
localPlot <- function(..., main, oma, font.main, cex.main) plot(...)
localLowerPanel <- function(..., main, oma, font.main, cex.main) lower.panel(...)
localUpperPanel <- function(..., main, oma, font.main, cex.main) upper.panel(...)
localDiagPanel <- function(..., main, oma, font.main, cex.main) diag.panel(...)
dots <- list(...)
nmdots <- names(dots)
# if (!is.matrix(x)) {
# x <- as.data.frame(x)
# for (i in seq_along(names(x))) {
# if (is.factor(x[[i]]) || is.logical(x[[i]]))
# x[[i]] <- as.numeric(x[[i]])
# if (!is.numeric(unclass(x[[i]])))
# stop("non-numeric argument to 'pairs'")
# }
# }
# else if (!is.numeric(x))
# stop("non-numeric argument to 'pairs'")
panel <- match.fun(panel)
if ((has.lower <- !is.null(lower.panel)) && !missing(lower.panel))
lower.panel <- match.fun(lower.panel)
if ((has.upper <- !is.null(upper.panel)) && !missing(upper.panel))
upper.panel <- match.fun(upper.panel)
if ((has.diag <- !is.null(diag.panel)) && !missing(diag.panel))
diag.panel <- match.fun(diag.panel)
if (row1attop) {
tmp <- lower.panel
lower.panel <- upper.panel
upper.panel <- tmp
tmp <- has.lower
has.lower <- has.upper
has.upper <- tmp
}
nc <- ncol(x)
if (nc < 2L)
stop("only one column in the argument to 'pairs'")
if (!all(horInd >= 1L & horInd <= nc))
stop("invalid argument 'horInd'")
if (!all(verInd >= 1L & verInd <= nc))
stop("invalid argument 'verInd'")
if (doText) {
if (missing(labels)) {
labels <- colnames(x)
if (is.null(labels))
labels <- paste("var", 1L:nc)
}
else if (is.null(labels))
doText <- FALSE
}
oma <- if ("oma" %in% nmdots)
dots$oma
main <- if ("main" %in% nmdots)
dots$main
if (is.null(oma))
oma <- c(4, 4, if (!is.null(main)) 6 else 4, 4)
opar <- par(mfrow = c(length(horInd), length(verInd)), mar = rep.int(gap/2,
4), oma = oma)
on.exit(par(opar))
dev.hold()
on.exit(dev.flush(), add = TRUE)
xl <- yl <- logical(nc)
if (is.numeric(log))
xl[log] <- yl[log] <- TRUE
else {
xl[] <- grepl("x", log)
yl[] <- grepl("y", log)
}
for (i in if (row1attop)
verInd
else rev(verInd)) for (j in horInd) {
l <- paste0(ifelse(xl[j], "x", ""), ifelse(yl[i], "y",
""))
localPlot(x[, j], x[, i], xlab = "", ylab = "", axes = FALSE,
type = "n", ..., log = l)
if (i == j || (i < j && has.lower) || (i > j && has.upper)) {
box()
if (i == 1 && (!(j%%2L) || !has.upper || !has.lower))
localAxis(1L + 2L * row1attop, x[, j], x[, i],
...)
if (i == nc && (j%%2L || !has.upper || !has.lower))
localAxis(3L - 2L * row1attop, x[, j], x[, i],
...)
if (j == 1 && (!(i%%2L) || !has.upper || !has.lower))
localAxis(2L, x[, j], x[, i], ...)
if (j == nc && (i%%2L || !has.upper || !has.lower))
localAxis(4L, x[, j], x[, i], ...)
mfg <- par("mfg")
if (i == j) {
if (has.diag)
localDiagPanel(x[, i], ...)
if (doText) {
par(usr = c(0, 1, 0, 1))
if (is.null(cex.labels)) {
l.wid <- strwidth(labels, "user")
cex.labels <- max(0.8, min(2, 0.9/max(l.wid)))
}
xlp <- if (xl[i])
10^0.5
else 0.5
ylp <- if (yl[j])
10^label.pos
else label.pos
text.panel(xlp, ylp, labels[i], cex = cex.labels,
font = font.labels)
}
}
else if (i < j)
localLowerPanel(x[, j], x[, i], ...)
else localUpperPanel(x[, j], x[, i], ...)
if (any(par("mfg") != mfg))
stop("the 'panel' function made a new plot")
}
else par(new = FALSE)
}
if (!is.null(main)) {
font.main <- if ("font.main" %in% nmdots)
dots$font.main
else par("font.main")
cex.main <- if ("cex.main" %in% nmdots)
dots$cex.main
else par("cex.main")
mtext(main, 3, line.main, outer = TRUE, at = 0.5, cex = cex.main,
font = font.main)
}
invisible(NULL)
}
# The function that runs the actual work of calculating correlations and
# plotting expression values.
.plotCorrelation2 <- function( expr.table, samples, method
, tagCountThreshold, applyThresholdBoth
, digits) {
# Select samples
if (all(samples %in% colnames(expr.table))) {
expr.table <- expr.table[,samples]
} else if(samples == "all"){
samples <- colnames(expr.table)
} else stop("'samples' parameter must be either \"all\" or a character vector of valid sample labels!")
nr.samples <- length(samples)
# Pre-calculate a vector of correlation coefficients
corr.v <- corVector(expr.table, method, tagCountThreshold, applyThresholdBoth)
# Add pseudocount to null values so that the plot axes are correctly set.
pseudocount <- min(sapply(expr.table, function(x) min(x[x>0]))) / 2
expr.table <- DataFrame(lapply( expr.table
, function(x) {x[x==0] <- pseudocount ; x}))
# This closure retreives correlation coefficients one after the other.
mkPanelCor <- function() {
i <- 1
function(x, y, digits=2, prefix="", cex.cor, ...) {
r <- corr.v[i]
i <<- i + 1
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep="")
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
gmean <- memoise(function(x) {
exp(mean(log(c(pseudocount, max(x)))))
})
text(gmean(x), gmean(y), txt, cex = cex.cor * sqrt(r))
}
}
panel.cor <- mkPanelCor()
# uniqueSignif returns a plain data.frame, because compression is already
# maximal in this context. See benchmarking of alternatives algorithms
# in the file "benchmarks/unique-signif.md" in the CAGEr's Git repository.
uniqueSignif <- function(x, y, digits = 0, log = c("", "xy")) {
log <- match.arg(log)
if (log == "xy") {x <- log1p(x) ; y <- log1p(y)}
u <- unique(Rle(complex( real = decode(signif(x, digits = digits))
, imaginary = decode(signif(y, digits = digits)))))
df <- data.frame(x = Re(u), y = Im(u))
if (log == "xy") df <- expm1(df)
df
}
# Thresholds are lowered of a minute amont because the rounding in the log
# scale in uniqueSignif adds minute errors to values that were already round.
pointsUnique <- function(x,y,...) {
df <- uniqueSignif(x, y, digits = digits, log = "xy")
df <- .applyThreshold(df, tagCountThreshold * 0.999, applyThresholdBoth)
df <- df[rowSums(df) > pseudocount * 1.999,] # Remove the (0,0) point.
points(df, ...)
}
pairs( expr.table
, lower.panel = pointsUnique
, upper.panel = panel.cor
, pch = "."
, cex = 4
, log = "xy"
, las = 1
, xaxp = c(1,10,1)
, yaxp = c(1,10,1)
, labels = samples)
# Return a correlation matrix
corr.m <- matrix(1, nr.samples, nr.samples)
colnames(corr.m) <- samples
rownames(corr.m) <- samples
corr.m[lower.tri(corr.m)] <- corr.v
corr.m[upper.tri(corr.m)] <- t(corr.m)[upper.tri(corr.m)]
corr.m
}
# Vanja's version of smooth scatter that allows passing range.x argument to grDevices:::.smoothScatterCalcDensity function to calculate 2D kernel smoothed density
#' @importFrom grDevices blues9 colorRamp colorRampPalette xy.coords
#' @importFrom graphics points
#' @importFrom KernSmooth bkde2D
# Local copy of grDevices:::.smoothScatterCalcDensity,
# to avoid problems in case the original function is changed
# (since the original is private, we can not assume that changes maintain
# compatibility with existing code.)
grDevices.smoothScatterCalcDensity <- function (x, nbin, bandwidth, range.x)
{
if (length(nbin) == 1)
nbin <- c(nbin, nbin)
if (!is.numeric(nbin) || length(nbin) != 2)
stop("'nbin' must be numeric of length 1 or 2")
if (missing(bandwidth)) {
bandwidth <- diff(apply(x, 2, stats::quantile, probs = c(0.05,
0.95), na.rm = TRUE, names = FALSE))/25
bandwidth[bandwidth == 0] <- 1
}
else {
if (!is.numeric(bandwidth))
stop("'bandwidth' must be numeric")
if (any(bandwidth <= 0))
stop("'bandwidth' must be positive")
}
rv <- KernSmooth::bkde2D(x, bandwidth = bandwidth, gridsize = nbin,
range.x = range.x)
rv$bandwidth <- bandwidth
rv
}
.mySmoothScatter <- function (x, y = NULL, nbin = 128, bandwidth, colramp = colorRampPalette(c("white",
blues9)), nrpoints = 100, pch = ".", cex = 1, col = "black",
transformation = function(x) x^0.25, postPlotHook = box,
xlab = NULL, ylab = NULL, xlim, ylim, xaxs = par("xaxs"),
yaxs = par("yaxs"), ...)
{
if (!is.numeric(nrpoints) | (nrpoints < 0) | (length(nrpoints) !=
1))
stop("'nrpoints' should be numeric scalar with value >= 0.")
xlabel <- if (!missing(x))
deparse(substitute(x))
ylabel <- if (!missing(y))
deparse(substitute(y))
xy <- xy.coords(x, y, xlabel, ylabel)
xlab <- if (is.null(xlab))
xy$xlab
else xlab
ylab <- if (is.null(ylab))
xy$ylab
else ylab
x <- cbind(xy$x, xy$y)[is.finite(xy$x) & is.finite(xy$y),
, drop = FALSE]
if (!missing(xlim)) {
stopifnot(is.numeric(xlim), length(xlim) == 2, is.finite(xlim))
x <- x[min(xlim) <= x[, 1] & x[, 1] <= max(xlim), ]
}
else {
xlim <- range(x[, 1])
}
if (!missing(ylim)) {
stopifnot(is.numeric(ylim), length(ylim) == 2, is.finite(ylim))
x <- x[min(ylim) <= x[, 2] & x[, 2] <= max(ylim), ]
}
else {
ylim <- range(x[, 2])
}
map <- grDevices.smoothScatterCalcDensity(x, nbin, bandwidth, range.x = list(xlim = c(xlim[1] - 1.5*bandwidth[1], xlim[2] + 1.5*bandwidth[1]), ylim = c(ylim[1] - 1.5*bandwidth[2], ylim[2] + 1.5*bandwidth[2])))
xm <- map$x1
ym <- map$x2
dens <- map$fhat
dens[] <- transformation(dens)
image(xm, ym, z = dens, col = colramp(256), xlab = xlab,
ylab = ylab, xlim = xlim, ylim = ylim, xaxs = xaxs, yaxs = yaxs,
...)
if (!is.null(postPlotHook))
postPlotHook()
if (nrpoints > 0) {
nrpoints <- min(nrow(x), ceiling(nrpoints))
stopifnot((nx <- length(xm)) == nrow(dens), (ny <- length(ym)) ==
ncol(dens))
ixm <- 1L + as.integer((nx - 1) * (x[, 1] - xm[1])/(xm[nx] -
xm[1]))
iym <- 1L + as.integer((ny - 1) * (x[, 2] - ym[1])/(ym[ny] -
ym[1]))
sel <- order(dens[cbind(ixm, iym)])[seq_len(nrpoints)]
points(x[sel, ], pch = pch, cex = cex, col = col)
}
}
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