Nothing
R/plotting.R
In chromstaR: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data
Defines functions
plotGenomeBrowser
plotBoxplot
plotNormalTransformation
plotHistogram
heatmapCombinations
heatmapTransitionProbs
heatmapCountCorrelation
plotHistograms
get_rightxlim
getStateColors
Documented in
getStateColors
heatmapCombinations
heatmapCountCorrelation
heatmapTransitionProbs
plotGenomeBrowser
plotHistogram
plotHistograms
#' @import ggplot2
NULL
#' Get state colors
#'
#' Get the colors that are used for plotting.
#'
#' @param labels Any combination of \code{c("zero-inflation","unmodified","modified", "total", "counts")}.
#' @return A character vector with colors.
#' @seealso \code{\link{plotting}}
#' @export
#'@examples
#'cols <- getStateColors()
#'pie(1:length(cols), col=cols, labels=names(cols))
getStateColors <- function(labels=NULL) {
if (is.null(labels)) {
labels <- c("zero-inflation","unmodified","modified", "total", "counts")
}
state.colors <- c("zero-inflation"="gray30","unmodified"="gray48","modified"="orangered3", "total"="black", "counts"="grey35")
return(state.colors[labels])
}
# ============================================================
# Helper functions
# ============================================================
get_rightxlim <- function(counts) {
if (!any(!counts==0)) {
return(rightxlim=10)
}
rightxlim1 <- median(counts[counts>0])*7
tab <- table(counts)
tab <- tab[names(tab)!='0']
breaks <- as.numeric(names(tab))
rightxlim2 <- breaks[tab<=5 & breaks>median(counts)*2][1]
rightxlim <- min(rightxlim1,rightxlim2, na.rm=TRUE)
if (length(rightxlim)==0 | is.na(rightxlim) | is.infinite(rightxlim)) {
rightxlim <- 1
}
return(rightxlim)
}
# ============================================================
# Multivariate plotting functions
# ============================================================
#' Histograms of binned read counts with fitted mixture distribution
#'
#' Plot histograms of binned read counts with fitted mixture distributions from a \code{\link{multiHMM}} object.
#'
#' @param model A \code{\link{multiHMM}} object or file that contains such an object.
#' @param ... Additional arguments (see \code{\link{plotHistogram}}).
#' @return A \code{\link[ggplot2:ggplot]{ggplot}} object.
#' @seealso \code{\link{plotting}}
plotHistograms <- function(model, ...) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=class.multivariate.hmm)[[1]] )
## Make fake uni.hmm and plot
binmapping <- dec2bin(levels(model$bins$state), colnames=model$info$ID)
ggplts <- list()
for (i1 in 1:ncol(model$bins$counts.rpkm)) {
uni.hmm <- list()
uni.hmm$info <- model$info[i1,]
uni.hmm$bincounts <- model$bincounts
uni.hmm$bincounts$counts <- model$bincounts$counts[,i1,]
uni.hmm$bins <- model$bins
mapping <- c('unmodified','modified')[binmapping[,i1]+1]
names(mapping) <- rownames(binmapping)
uni.hmm$bins$state <- mapping[uni.hmm$bins$state]
uni.hmm$bins$counts.rpkm <- model$bins$counts.rpkm[,i1]
uni.hmm$weights <- model$weights.univariate[[i1]]
uni.hmm$distributions <- model$distributions[[i1]]
class(uni.hmm) <- class.univariate.hmm
ggplts[[i1]] <- plotHistogram(uni.hmm, ...)
}
return(ggplts)
}
#' Read count correlation heatmap
#'
#' Heatmap of read count correlations (see \code{\link[stats]{cor}}).
#'
#' @param model A \code{\link{multiHMM}} or \code{\link{combinedMultiHMM}} object or file that contains such an object.
#' @param cluster Logical indicating whether or not to cluster the heatmap.
#' @return A \code{\link[ggplot2]{ggplot}} object.
#' @importFrom stats dist hclust
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @examples
#'## Get an example multiHMM ##
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'## Plot count correlations as heatmap
#'heatmapCountCorrelation(model)
#'
heatmapCountCorrelation <- function(model, cluster=TRUE) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=c(class.multivariate.hmm, class.combined.multivariate.hmm))[[1]] )
cr <- cor(model$bins$counts.rpkm)
df <- reshape2::melt(cr, value.name='correlation')
if (cluster) {
hc <- stats::hclust(stats::dist(cr))
df$Var1 <- factor(df$Var1, levels=levels(df$Var1)[hc$order])
df$Var2 <- factor(df$Var2, levels=levels(df$Var2)[hc$order])
}
ggplt <- ggplot(df) + geom_tile(aes_string(x='Var1', y='Var2', fill='correlation')) + xlab('') + ylab('') + theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5)) + scale_fill_gradient(low='red', high='yellow')
return(ggplt)
}
#' Heatmap of transition probabilities
#'
#' Plot a heatmap of transition probabilities for a \code{\link{multiHMM}} model.
#'
#' @param model A \code{\link{multiHMM}} object or file that contains such an object.
#' @param reorder.states Whether or not to reorder the states.
#' @param transitionProbs A matrix with transition probabilities where \code{dimnames(emissionProbs)} gives the state labels. This option is helpful to plot transition probabilities directly without needing a \code{\link{chromstaR-objects}}. If specified, \code{model} will be ignored.
#' @return A \code{\link[ggplot2]{ggplot}} object.
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @examples
#'## Get an example multiHMM ##
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'## Plot transition probabilites as heatmap
#'heatmapTransitionProbs(model, reorder.states=TRUE)
#'
heatmapTransitionProbs <- function(model=NULL, reorder.states=TRUE, transitionProbs=NULL) {
if (is.null(transitionProbs)) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=c(class.multivariate.hmm, class.combined.multivariate.hmm))[[1]] )
transitionProbs <- model$transitionProbs
A <- reshape2::melt(transitionProbs, varnames=c('from','to'), value.name='prob')
if (reorder.states) {
A$from <- factor(A$from, levels=stateorderByTransition(transitionProbs))
A$to <- factor(A$to, levels=stateorderByTransition(transitionProbs))
} else {
A$from <- factor(A$from, levels=levels(model$bins$combination))
A$to <- factor(A$to, levels=levels(model$bins$combination))
}
} else {
A <- reshape2::melt(transitionProbs, varnames=c('from','to'), value.name='prob')
if (reorder.states) {
A$from <- factor(A$from, levels=stateorderByTransition(transitionProbs))
A$to <- factor(A$to, levels=stateorderByTransition(transitionProbs))
} else {
A$from <- factor(A$from, levels=colnames(transitionProbs))
A$to <- factor(A$to, levels=colnames(transitionProbs))
}
}
ggplt <- ggplot(data=A) + geom_tile(aes_string(x='to', y='from', fill='prob')) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5)) + scale_fill_gradient(low="white", high="blue")
return(ggplt)
}
#' Plot a heatmap of combinatorial states
#'
#' Plot a heatmap that shows the binary presence/absence of marks for the different combinations.
#'
#' @param model A \code{\link{multiHMM}} object or file that contains such an object.
#' @param marks A character vector with histone marks. If specified, \code{model} will be ignored.
#' @param emissionProbs A matrix with emission probabilities where \code{dimnames(emissionProbs)} gives the state labels and marks. This option is helpful to plot probabilistic chromatin states (not part of \pkg{\link{chromstaR}}). If specified, \code{model} and \code{marks} will be ignored.
#' @return A \code{\link[ggplot2]{ggplot}} object.
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @author Aaron Taudt
#' @examples
#'marks <- c('H3K4me3','H3K27me3','H4K20me1')
#'heatmapCombinations(marks=marks)
#'
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'heatmapCombinations(file)
#'
heatmapCombinations <- function(model=NULL, marks=NULL, emissionProbs=NULL) {
if (!is.null(emissionProbs)) {
df <- reshape2::melt(emissionProbs, value.name='emission')
names(df)[1:2] <- c('combination', 'mark')
df$combination <- factor(df$combination)
} else {
if (is.null(marks)) {
model <- loadHmmsFromFiles(model, check.class=class.multivariate.hmm)[[1]]
levels.combinations <- levels(model$bins$combination)
levels.combinations <- gsub('\\[', '', levels.combinations)
levels.combinations <- gsub('\\]', '', levels.combinations)
marks <- unique(unlist(strsplit(levels.combinations, '\\+')))
}
d <- dec2bin(0:(2^length(marks)-1), colnames=marks)
d <- as.data.frame(d)
d$combination <- apply(d, 1, function(x) { paste(colnames(d)[x],collapse='+') })
d$combination <- paste0('[',d$combination,']')
df <- reshape2::melt(d, variable.name='mark', value.name='emission', id.vars='combination')
df$emission <- as.numeric(df$emission)
}
ggplt <- ggplot(df) + geom_tile(aes_string(x='combination', y='mark', fill='emission')) + theme_bw() + theme(axis.text.x=element_text(angle=45, hjust=1))
if (!is.null(emissionProbs)) {
ggplt <- ggplt + scale_fill_gradient(low='white', high='blue', limits=c(0,1))
} else {
ggplt <- ggplt + scale_fill_gradient(low='white', high='blue', guide=FALSE, limits=c(0,1))
}
return(ggplt)
}
# ============================================================
# Univariate plotting functions
# ============================================================
#' Histogram of binned read counts with fitted mixture distribution
#'
#' Plot a histogram of binned read counts with fitted mixture distributions from a \code{\link{uniHMM}} object.
#'
#' @param model A \code{\link{uniHMM}} object or file that contains such an object.
#' @param state Plot the histogram only for the specified state. One of \code{c('unmodified','modified')}.
#' @param chromosomes,start,end Plot the histogram only for the specified chromosomes, start and end position.
#' @param linewidth Width of the distribution lines.
#' @return A \code{\link[ggplot2:ggplot]{ggplot}} object.
#' @importFrom graphics hist
#' @importFrom stats dnbinom
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @examples
#'## Get an example BAM file with ChIP-seq reads
#'file <- system.file("extdata", "euratrans",
#' "lv-H3K27me3-BN-male-bio2-tech1.bam",
#' package="chromstaRData")
#'## Bin the BED file into bin size 1000bp
#'data(rn4_chrominfo)
#'data(experiment_table)
#'binned <- binReads(file, experiment.table=experiment_table,
#' assembly=rn4_chrominfo, binsizes=1000,
#' stepsizes=500, chromosomes='chr12')
#'plotHistogram(binned)
#'## Fit the univariate Hidden Markov Model
#'hmm <- callPeaksUnivariate(binned, max.time=60, eps=1)
#'## Check if the fit is ok
#'plotHistogram(hmm)
#'
plotHistogram <- function(model, state=NULL, chromosomes=NULL, start=NULL, end=NULL, linewidth=1) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=c('GRanges', class.univariate.hmm))[[1]] )
if (is(model, 'GRanges')) {
bins <- model
} else if (is(model, class.univariate.hmm)) {
bins <- model$bincounts
}
# Select the rows to plot
selectmask <- rep(TRUE,length(bins))
if (!is.null(chromosomes)) {
if (any(! chromosomes %in% levels(seqnames(bins)))) {
stop(chromosomes[! chromosomes %in% levels(seqnames(bins))]," can't be found in the binned data.")
}
selectchrom <- as.logical(seqnames(bins) %in% chromosomes)
selectmask <- selectmask & selectchrom
if (!is.null(start)) {
selectstart <- as.logical(start(ranges(bins)) >= start)
selectmask <- selectmask & selectstart
}
if (!is.null(end)) {
selectend <- as.logical(end(ranges(bins)) <= end)
selectmask <- selectmask & selectend
}
}
if (!is.null(state)) {
selectmask <- selectmask & bins$state==state
}
counts <- bins$counts[selectmask, '0']
# Plot the histogram
rightxlim <- get_rightxlim(counts)
ggplt <- ggplot(data.frame(counts)) + geom_histogram(aes_string(x='counts', y='..density..'), binwidth=1, color='black', fill='white') + coord_cartesian(xlim=c(0,rightxlim)) + theme_bw() + xlab("read count")
if (is(model, 'GRanges')) {
return(ggplt)
}
### Add fits to the histogram
states <- model$bins$state[selectmask]
weights <- rep(NA, 3)
weights[1] <- length(which(states=="zero-inflation"))
weights[2] <- length(which(states=="unmodified"))
weights[3] <- length(which(states=="modified"))
weights <- weights / length(states)
x <- 0:max(counts)
distributions <- data.frame(x)
# Unmodified
w <- weights[1]/(weights[2]+weights[1]) # weight for the zero-inflation
if (is.nan(w)) { w <- 0 }
distributions$unmodified <- (1-weights[3]) * dzinbinom(x, w, model$distributions[2,'size'], model$distributions[2,'prob'])
# Modified
distributions$modified <- weights[3] * stats::dnbinom(x, model$distributions[3,'size'], model$distributions[3,'prob'])
# Total
distributions$total <- distributions$unmodified + distributions$modified
# Convert to long format
df <- reshape2::melt(distributions, id.vars='x', variable.name='state', value.name='y')
# Make legend
lmeans <- round(model$distributions[,'mu'], 2)[-1]
lvars <- round(model$distributions[,'variance'], 2)[-1]
lweights <- round(c(1-weights[3], weights[3]), 2)
legend <- paste0(c('unmodified','modified'), ", mean=", lmeans, ", var=", lvars, ", weight=", lweights)
legend <- c(legend, paste0('total, mean(data)=', round(mean(counts),2), ', var(data)=', round(var(counts),2)))
ggplt <- ggplt + ggtitle(model$info$ID)
### Plot the distributions
if (is.null(state)) {
ggplt <- ggplt + geom_line(data=df, aes_string(x='x', y='y', col='state'), size=linewidth)
ggplt <- ggplt + scale_color_manual(name="components", values=getStateColors(c('unmodified','modified','total')), labels=legend) + theme(legend.justification=c(1,1), legend.position=c(0.99,0.99))
} else {
if (state=="unmodified") {
ggplt <- ggplt + geom_line(data=df[df$state=='unmodified',], aes_string(x='x', y='y', col='state'), size=linewidth)
ggplt <- ggplt + scale_color_manual(name="components", values=getStateColors(c('unmodified')), labels=legend[1]) + theme(legend.justification=c(1,1), legend.position=c(0.99,0.99))
}
if (state=="modified") {
ggplt <- ggplt + geom_line(data=df[df$state=='modified',], aes_string(x='x', y='y', col='state'), size=linewidth)
ggplt <- ggplt + scale_color_manual(name="components", values=getStateColors(c('modified')), labels=legend[2]) + theme(legend.justification=c(1,1), legend.position=c(0.99,0.99))
}
}
return(ggplt)
}
#' @importFrom stats pnbinom qnorm dnorm
plotNormalTransformation <- function(model, state='unmodified') {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=class.univariate.hmm)[[1]] )
## Plot settings
cols <- getStateColors(c("unmodified","modified"))
## Transform the counts
df <- as.data.frame(model$bins)[,c('counts','state')]
# Transform to uniform space
mask <- df$state=='modified'
df$ucounts[!mask] <- pzinbinom(df$counts[!mask], model$weights[1], model$distributions[2,'size'], model$distributions[2,'prob'])
df$ucounts[mask] <- stats::pnbinom(df$counts[mask], model$distributions[3,'size'], model$distributions[3,'prob'])
# Transform to normal space
df$ncounts <- stats::qnorm(df$ucounts)
## Make the plots
subset <- df$ncounts[df$state==state]
breaks <- c(-Inf,sort(as.numeric(names(table(subset)))))
x <- seq(-4,4,0.1)
title <- paste0("Transformed emission density for state ",state)
ggplt <- ggplot() + geom_histogram(data=data.frame(ucounts=subset), aes_string(x='ucounts', y='..density..'), breaks=breaks, right=TRUE, col='black', fill=cols[state]) + theme_bw() + geom_line(data=data.frame(x=x, y=stats::dnorm(x, mean=0, sd=1)), aes_string(x='x', y='y')) + xlab("transformed counts") + labs(title=title)
return(ggplt)
}
plotBoxplot <- function(model) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=class.univariate.hmm)[[1]] )
## Boxplot
df <- as.data.frame(model$bins)[,c('state','counts')]
ggplt <- ggplot() + theme_bw() + geom_boxplot(data=df, aes_string(x='state', y='counts', fill='state')) + scale_fill_manual(values=getStateColors(c('zero-inflation','unmodified','modified')))
return(ggplt)
}
#' #' Plot a genome browser view
#' #'
#' #' Plot a simple genome browser view. This is useful for scripted genome browser snapshots.
#' #'
#' #' @param counts A \code{\link[GenomicRanges]{GRanges-class}} object with meta-data column 'counts'.
#' #' @param peaklist A named list() of \code{\link[GenomicRanges]{GRanges-class}} objects containing peak coordinates.
#' #' @param chr,start,end Chromosome, start and end coordinates for the plot.
#' #' @param countcol A character giving the color for the counts.
#' #' @param peakcols A character vector with colors for the peaks in \code{peaklist}.
#' #' @param style One of \code{c('peaks', 'density')}.
#' #' @param peakTrackHeight Relative height of the tracks given in \code{peaklist} compared to the \code{counts}.
#' #' @return A \code{\link[ggplot2:ggplot]{ggplot}} object.
#' #' @examples
#' #'## Get an example multiHMM ##
#' #'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' #' package="chromstaR")
#' #'model <- get(load(file))
#' #'## Plot genome browser snapshot
#' #'bins <- model$bins
#' #'bins$counts <- model$bins$counts.rpkm[,1]
#' #'plotGenomeBrowser(counts=bins, peaklist=model$peaks,
#' #' chr='chr12', start=1, end=1e6)
#' #'
#' plotGenomeBrowser2 <- function(counts, peaklist=NULL, chr, start, end, countcol='black', peakcols=NULL, style='peaks', peakTrackHeight=5) {
#'
#' ## Select ranges to plot
#' ranges2plot <- reduce(counts[counts@seqnames == chr & start(counts) >= start & start(counts) <= end])
#'
#' ## Counts
#' counts <- subsetByOverlaps(counts, ranges2plot)
#'
#' if (style == 'peaks') {
#' df <- data.frame(x=(start(counts)+end(counts))/2, counts=counts$counts) # plot triangles centered at middle of the bin
#' ggplt <- ggplot(df) + geom_area(aes_string(x='x', y='counts')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), axis.title = element_blank(), axis.ticks.x = element_blank(), axis.line = element_blank())
#' maxcounts <- max(counts$counts)
#' ggplt <- ggplt + scale_y_continuous(breaks=c(0, maxcounts))
#' } else if (style == 'density') {
#' df <- data.frame(xmin=start(counts), xmax=end(counts), counts=counts$counts)
#'
#' ggplt <- ggplot(df) + geom_rect(aes_string(xmin='xmin', xmax='xmax', ymin=0, ymax=4, alpha='counts')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank(), axis.line = element_blank())
#' } else {
#' stop("Unknown value '", style, "' for parameter 'style'. Must be one of c('peaks', 'density').")
#' }
#'
#' ## Peaks
#' if (!is.null(peaklist)) {
#' if (is.null(peakcols)) {
#' peakcols <- getDistinctColors(length(peaklist))
#' }
#' for (i1 in 1:length(peaklist)) {
#' p <- peakTrackHeight
#' peaks <- subsetByOverlaps(peaklist[[i1]], ranges2plot)
#' if (length(peaks) > 0) {
#' df <- data.frame(start=start(peaks), end=end(peaks), ymin=-p*i1, ymax=-p*i1+0.9*p)
#' ggplt <- ggplt + geom_rect(data=df, mapping=aes_string(xmin='start', xmax='end', ymin='ymin', ymax='ymax'), col=peakcols[i1], fill=peakcols[i1])
#' }
#' trackname <- names(peaklist)[i1]
#' df <- data.frame(x=start(counts)[1], y=-p*i1+0.5*p, label=trackname)
#' ggplt <- ggplt + geom_text(data=df, mapping=aes_string(x='x', y='y', label='label'), vjust=0.5, hjust=0.5, col=peakcols[i1])
#' }
#' }
#'
#' return(ggplt)
#' }
#' Plot a genome browser view
#'
#' Plot a simple genome browser view of \code{\link{chromstaR-objects}}. This is useful for scripted genome browser snapshots.
#'
#' @param model A \code{\link{uniHMM}}, \code{\link{multiHMM}} or \code{\link{combinedMultiHMM}} object or file that contains such an object.
#' @param chr,start,end Chromosome, start and end coordinates for the plot.
#' @param style One of \code{c('peaks', 'density')}.
#' @param peakHeight Height of the peak track relative to the count track.
#' @param peakColor Color for the peak track.
#' @param same.yaxis Whether or not the plots for the same mark have the same y-axis.
#' @return A \code{list()} of \code{\link[ggplot2:ggplot]{ggplot}} objects.
#' @export
#' @examples
#'## Get an example uniHMM ##
#'file <- system.file("data","H3K27me3-BN-rep1.RData", package="chromstaR")
#'model <- get(load(file))
#'plotGenomeBrowser(model, chr='chr12', start=1, end=1e6, style='peaks',
#' peakHeight=0.1)
#'
#'## Get an example multiHMM ##
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'plotGenomeBrowser(model, chr='chr12', start=1, end=1e6, style='peaks',
#' peakHeight=0.1)
#'
#'## Get an example combinedMultiHMM ##
#'file <- system.file("data","combined_mode-differential.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'plotlist <- plotGenomeBrowser(model, chr='chr12', start=1, end=1e6, style='peaks',
#' peakHeight=0.1)
#'
plotGenomeBrowser <- function(model, chr, start, end, style='peaks', peakHeight=0.2, peakColor='blue', same.yaxis=TRUE) {
model <- loadHmmsFromFiles(model, check.class = c('uniHMM', 'multiHMM', 'combinedMultiHMM'))[[1]]
## Select ranges to plot
bins <- model$bins
ranges2plot <- reduce(bins[bins@seqnames == chr & start(bins) >= start & start(bins) <= end, , drop=FALSE])
bins <- subsetByOverlaps(bins, ranges2plot)
if (is(model,'uniHMM')) {
peaklist <- list(model$peaks)
bins$counts.rpkm <- matrix(bins$counts.rpkm, ncol=1)
if (is.null(model$info)) {
model$info <- data.frame(file=NA, mark="peaks", condition=NA, replicate=1, ID='browser_snapshot')
}
names(peaklist) <- model$info$ID
colnames(bins$counts.rpkm) <- model$info$ID
} else {
peaklist <- model$peaks
}
## Determine y-limits, same within across marks ##
maxcounts <- apply(bins$counts.rpkm, 2, max)
maxcounts.permark <- list()
for (mark in unique(model$info$mark)) {
maxcounts.permark[[mark]] <- max(maxcounts[model$info$mark==mark])
}
ggplts <- list()
for (i1 in 1:nrow(model$info)) {
ID <- model$info$ID[i1]
if (same.yaxis) {
ymax.counts <- round(maxcounts.permark[[as.character(model$info$mark[i1])]])
} else {
ymax.counts <- round(maxcounts[[as.character(ID)]])
}
if (style == 'peaks') {
df <- data.frame(position=(start(bins)+end(bins))/2, RPKM=bins$counts.rpkm[,ID]) # plot triangles centered at middle of the bin
ggplt <- ggplot(df) + geom_area(aes_string(x='position', y='RPKM')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.line = element_blank())
} else if (style == 'density') {
df <- data.frame(xmin=start(bins), xmax=end(bins), RPKM=bins$counts.rpkm[,ID])
ggplt <- ggplot(df) + geom_rect(aes_string(xmin='xmin', xmax='xmax', ymin=0, ymax=4, alpha='RPKM')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line = element_blank(), axis.title.y = element_blank())
ggplt <- ggplt + xlab('position')
} else {
stop("Unknown value '", style, "' for parameter 'style'. Must be one of c('peaks', 'density').")
}
## Peaks
peaks <- subsetByOverlaps(peaklist[[ID]], ranges2plot)
if (length(peaks) > 0) {
df <- data.frame(start=start(peaks), end=end(peaks), ymin=-peakHeight*ymax.counts, ymax=0.1*(-peakHeight*ymax.counts))
ggplt <- ggplt + geom_rect(data=df, mapping=aes_string(xmin='start', xmax='end', ymin='ymin', ymax='ymax'), col=peakColor, fill=peakColor)
}
ggplt <- ggplt + scale_y_continuous(breaks=c(0, ymax.counts))
ggplt <- ggplt + coord_cartesian(xlim=c(start, end), ylim=c(-peakHeight*ymax.counts,ymax.counts))
ggplt <- ggplt + ggtitle(ID)
ggplts[[ID]] <- ggplt
}
return(ggplts)
}
Try the chromstaR package in your browser
Any scripts or data that you put into this service are public.
chromstaR documentation built on Nov. 8, 2020, 8:29 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions plotGenomeBrowser plotBoxplot plotNormalTransformation plotHistogram heatmapCombinations heatmapTransitionProbs heatmapCountCorrelation plotHistograms get_rightxlim getStateColors
Documented in getStateColors heatmapCombinations heatmapCountCorrelation heatmapTransitionProbs plotGenomeBrowser plotHistogram plotHistograms
#' @import ggplot2
NULL
#' Get state colors
#'
#' Get the colors that are used for plotting.
#'
#' @param labels Any combination of \code{c("zero-inflation","unmodified","modified", "total", "counts")}.
#' @return A character vector with colors.
#' @seealso \code{\link{plotting}}
#' @export
#'@examples
#'cols <- getStateColors()
#'pie(1:length(cols), col=cols, labels=names(cols))
getStateColors <- function(labels=NULL) {
if (is.null(labels)) {
labels <- c("zero-inflation","unmodified","modified", "total", "counts")
}
state.colors <- c("zero-inflation"="gray30","unmodified"="gray48","modified"="orangered3", "total"="black", "counts"="grey35")
return(state.colors[labels])
}
# ============================================================
# Helper functions
# ============================================================
get_rightxlim <- function(counts) {
if (!any(!counts==0)) {
return(rightxlim=10)
}
rightxlim1 <- median(counts[counts>0])*7
tab <- table(counts)
tab <- tab[names(tab)!='0']
breaks <- as.numeric(names(tab))
rightxlim2 <- breaks[tab<=5 & breaks>median(counts)*2][1]
rightxlim <- min(rightxlim1,rightxlim2, na.rm=TRUE)
if (length(rightxlim)==0 | is.na(rightxlim) | is.infinite(rightxlim)) {
rightxlim <- 1
}
return(rightxlim)
}
# ============================================================
# Multivariate plotting functions
# ============================================================
#' Histograms of binned read counts with fitted mixture distribution
#'
#' Plot histograms of binned read counts with fitted mixture distributions from a \code{\link{multiHMM}} object.
#'
#' @param model A \code{\link{multiHMM}} object or file that contains such an object.
#' @param ... Additional arguments (see \code{\link{plotHistogram}}).
#' @return A \code{\link[ggplot2:ggplot]{ggplot}} object.
#' @seealso \code{\link{plotting}}
plotHistograms <- function(model, ...) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=class.multivariate.hmm)[[1]] )
## Make fake uni.hmm and plot
binmapping <- dec2bin(levels(model$bins$state), colnames=model$info$ID)
ggplts <- list()
for (i1 in 1:ncol(model$bins$counts.rpkm)) {
uni.hmm <- list()
uni.hmm$info <- model$info[i1,]
uni.hmm$bincounts <- model$bincounts
uni.hmm$bincounts$counts <- model$bincounts$counts[,i1,]
uni.hmm$bins <- model$bins
mapping <- c('unmodified','modified')[binmapping[,i1]+1]
names(mapping) <- rownames(binmapping)
uni.hmm$bins$state <- mapping[uni.hmm$bins$state]
uni.hmm$bins$counts.rpkm <- model$bins$counts.rpkm[,i1]
uni.hmm$weights <- model$weights.univariate[[i1]]
uni.hmm$distributions <- model$distributions[[i1]]
class(uni.hmm) <- class.univariate.hmm
ggplts[[i1]] <- plotHistogram(uni.hmm, ...)
}
return(ggplts)
}
#' Read count correlation heatmap
#'
#' Heatmap of read count correlations (see \code{\link[stats]{cor}}).
#'
#' @param model A \code{\link{multiHMM}} or \code{\link{combinedMultiHMM}} object or file that contains such an object.
#' @param cluster Logical indicating whether or not to cluster the heatmap.
#' @return A \code{\link[ggplot2]{ggplot}} object.
#' @importFrom stats dist hclust
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @examples
#'## Get an example multiHMM ##
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'## Plot count correlations as heatmap
#'heatmapCountCorrelation(model)
#'
heatmapCountCorrelation <- function(model, cluster=TRUE) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=c(class.multivariate.hmm, class.combined.multivariate.hmm))[[1]] )
cr <- cor(model$bins$counts.rpkm)
df <- reshape2::melt(cr, value.name='correlation')
if (cluster) {
hc <- stats::hclust(stats::dist(cr))
df$Var1 <- factor(df$Var1, levels=levels(df$Var1)[hc$order])
df$Var2 <- factor(df$Var2, levels=levels(df$Var2)[hc$order])
}
ggplt <- ggplot(df) + geom_tile(aes_string(x='Var1', y='Var2', fill='correlation')) + xlab('') + ylab('') + theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5)) + scale_fill_gradient(low='red', high='yellow')
return(ggplt)
}
#' Heatmap of transition probabilities
#'
#' Plot a heatmap of transition probabilities for a \code{\link{multiHMM}} model.
#'
#' @param model A \code{\link{multiHMM}} object or file that contains such an object.
#' @param reorder.states Whether or not to reorder the states.
#' @param transitionProbs A matrix with transition probabilities where \code{dimnames(emissionProbs)} gives the state labels. This option is helpful to plot transition probabilities directly without needing a \code{\link{chromstaR-objects}}. If specified, \code{model} will be ignored.
#' @return A \code{\link[ggplot2]{ggplot}} object.
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @examples
#'## Get an example multiHMM ##
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'## Plot transition probabilites as heatmap
#'heatmapTransitionProbs(model, reorder.states=TRUE)
#'
heatmapTransitionProbs <- function(model=NULL, reorder.states=TRUE, transitionProbs=NULL) {
if (is.null(transitionProbs)) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=c(class.multivariate.hmm, class.combined.multivariate.hmm))[[1]] )
transitionProbs <- model$transitionProbs
A <- reshape2::melt(transitionProbs, varnames=c('from','to'), value.name='prob')
if (reorder.states) {
A$from <- factor(A$from, levels=stateorderByTransition(transitionProbs))
A$to <- factor(A$to, levels=stateorderByTransition(transitionProbs))
} else {
A$from <- factor(A$from, levels=levels(model$bins$combination))
A$to <- factor(A$to, levels=levels(model$bins$combination))
}
} else {
A <- reshape2::melt(transitionProbs, varnames=c('from','to'), value.name='prob')
if (reorder.states) {
A$from <- factor(A$from, levels=stateorderByTransition(transitionProbs))
A$to <- factor(A$to, levels=stateorderByTransition(transitionProbs))
} else {
A$from <- factor(A$from, levels=colnames(transitionProbs))
A$to <- factor(A$to, levels=colnames(transitionProbs))
}
}
ggplt <- ggplot(data=A) + geom_tile(aes_string(x='to', y='from', fill='prob')) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5)) + scale_fill_gradient(low="white", high="blue")
return(ggplt)
}
#' Plot a heatmap of combinatorial states
#'
#' Plot a heatmap that shows the binary presence/absence of marks for the different combinations.
#'
#' @param model A \code{\link{multiHMM}} object or file that contains such an object.
#' @param marks A character vector with histone marks. If specified, \code{model} will be ignored.
#' @param emissionProbs A matrix with emission probabilities where \code{dimnames(emissionProbs)} gives the state labels and marks. This option is helpful to plot probabilistic chromatin states (not part of \pkg{\link{chromstaR}}). If specified, \code{model} and \code{marks} will be ignored.
#' @return A \code{\link[ggplot2]{ggplot}} object.
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @author Aaron Taudt
#' @examples
#'marks <- c('H3K4me3','H3K27me3','H4K20me1')
#'heatmapCombinations(marks=marks)
#'
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'heatmapCombinations(file)
#'
heatmapCombinations <- function(model=NULL, marks=NULL, emissionProbs=NULL) {
if (!is.null(emissionProbs)) {
df <- reshape2::melt(emissionProbs, value.name='emission')
names(df)[1:2] <- c('combination', 'mark')
df$combination <- factor(df$combination)
} else {
if (is.null(marks)) {
model <- loadHmmsFromFiles(model, check.class=class.multivariate.hmm)[[1]]
levels.combinations <- levels(model$bins$combination)
levels.combinations <- gsub('\\[', '', levels.combinations)
levels.combinations <- gsub('\\]', '', levels.combinations)
marks <- unique(unlist(strsplit(levels.combinations, '\\+')))
}
d <- dec2bin(0:(2^length(marks)-1), colnames=marks)
d <- as.data.frame(d)
d$combination <- apply(d, 1, function(x) { paste(colnames(d)[x],collapse='+') })
d$combination <- paste0('[',d$combination,']')
df <- reshape2::melt(d, variable.name='mark', value.name='emission', id.vars='combination')
df$emission <- as.numeric(df$emission)
}
ggplt <- ggplot(df) + geom_tile(aes_string(x='combination', y='mark', fill='emission')) + theme_bw() + theme(axis.text.x=element_text(angle=45, hjust=1))
if (!is.null(emissionProbs)) {
ggplt <- ggplt + scale_fill_gradient(low='white', high='blue', limits=c(0,1))
} else {
ggplt <- ggplt + scale_fill_gradient(low='white', high='blue', guide=FALSE, limits=c(0,1))
}
return(ggplt)
}
# ============================================================
# Univariate plotting functions
# ============================================================
#' Histogram of binned read counts with fitted mixture distribution
#'
#' Plot a histogram of binned read counts with fitted mixture distributions from a \code{\link{uniHMM}} object.
#'
#' @param model A \code{\link{uniHMM}} object or file that contains such an object.
#' @param state Plot the histogram only for the specified state. One of \code{c('unmodified','modified')}.
#' @param chromosomes,start,end Plot the histogram only for the specified chromosomes, start and end position.
#' @param linewidth Width of the distribution lines.
#' @return A \code{\link[ggplot2:ggplot]{ggplot}} object.
#' @importFrom graphics hist
#' @importFrom stats dnbinom
#' @importFrom reshape2 melt
#' @seealso \code{\link{plotting}}
#' @export
#' @examples
#'## Get an example BAM file with ChIP-seq reads
#'file <- system.file("extdata", "euratrans",
#' "lv-H3K27me3-BN-male-bio2-tech1.bam",
#' package="chromstaRData")
#'## Bin the BED file into bin size 1000bp
#'data(rn4_chrominfo)
#'data(experiment_table)
#'binned <- binReads(file, experiment.table=experiment_table,
#' assembly=rn4_chrominfo, binsizes=1000,
#' stepsizes=500, chromosomes='chr12')
#'plotHistogram(binned)
#'## Fit the univariate Hidden Markov Model
#'hmm <- callPeaksUnivariate(binned, max.time=60, eps=1)
#'## Check if the fit is ok
#'plotHistogram(hmm)
#'
plotHistogram <- function(model, state=NULL, chromosomes=NULL, start=NULL, end=NULL, linewidth=1) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=c('GRanges', class.univariate.hmm))[[1]] )
if (is(model, 'GRanges')) {
bins <- model
} else if (is(model, class.univariate.hmm)) {
bins <- model$bincounts
}
# Select the rows to plot
selectmask <- rep(TRUE,length(bins))
if (!is.null(chromosomes)) {
if (any(! chromosomes %in% levels(seqnames(bins)))) {
stop(chromosomes[! chromosomes %in% levels(seqnames(bins))]," can't be found in the binned data.")
}
selectchrom <- as.logical(seqnames(bins) %in% chromosomes)
selectmask <- selectmask & selectchrom
if (!is.null(start)) {
selectstart <- as.logical(start(ranges(bins)) >= start)
selectmask <- selectmask & selectstart
}
if (!is.null(end)) {
selectend <- as.logical(end(ranges(bins)) <= end)
selectmask <- selectmask & selectend
}
}
if (!is.null(state)) {
selectmask <- selectmask & bins$state==state
}
counts <- bins$counts[selectmask, '0']
# Plot the histogram
rightxlim <- get_rightxlim(counts)
ggplt <- ggplot(data.frame(counts)) + geom_histogram(aes_string(x='counts', y='..density..'), binwidth=1, color='black', fill='white') + coord_cartesian(xlim=c(0,rightxlim)) + theme_bw() + xlab("read count")
if (is(model, 'GRanges')) {
return(ggplt)
}
### Add fits to the histogram
states <- model$bins$state[selectmask]
weights <- rep(NA, 3)
weights[1] <- length(which(states=="zero-inflation"))
weights[2] <- length(which(states=="unmodified"))
weights[3] <- length(which(states=="modified"))
weights <- weights / length(states)
x <- 0:max(counts)
distributions <- data.frame(x)
# Unmodified
w <- weights[1]/(weights[2]+weights[1]) # weight for the zero-inflation
if (is.nan(w)) { w <- 0 }
distributions$unmodified <- (1-weights[3]) * dzinbinom(x, w, model$distributions[2,'size'], model$distributions[2,'prob'])
# Modified
distributions$modified <- weights[3] * stats::dnbinom(x, model$distributions[3,'size'], model$distributions[3,'prob'])
# Total
distributions$total <- distributions$unmodified + distributions$modified
# Convert to long format
df <- reshape2::melt(distributions, id.vars='x', variable.name='state', value.name='y')
# Make legend
lmeans <- round(model$distributions[,'mu'], 2)[-1]
lvars <- round(model$distributions[,'variance'], 2)[-1]
lweights <- round(c(1-weights[3], weights[3]), 2)
legend <- paste0(c('unmodified','modified'), ", mean=", lmeans, ", var=", lvars, ", weight=", lweights)
legend <- c(legend, paste0('total, mean(data)=', round(mean(counts),2), ', var(data)=', round(var(counts),2)))
ggplt <- ggplt + ggtitle(model$info$ID)
### Plot the distributions
if (is.null(state)) {
ggplt <- ggplt + geom_line(data=df, aes_string(x='x', y='y', col='state'), size=linewidth)
ggplt <- ggplt + scale_color_manual(name="components", values=getStateColors(c('unmodified','modified','total')), labels=legend) + theme(legend.justification=c(1,1), legend.position=c(0.99,0.99))
} else {
if (state=="unmodified") {
ggplt <- ggplt + geom_line(data=df[df$state=='unmodified',], aes_string(x='x', y='y', col='state'), size=linewidth)
ggplt <- ggplt + scale_color_manual(name="components", values=getStateColors(c('unmodified')), labels=legend[1]) + theme(legend.justification=c(1,1), legend.position=c(0.99,0.99))
}
if (state=="modified") {
ggplt <- ggplt + geom_line(data=df[df$state=='modified',], aes_string(x='x', y='y', col='state'), size=linewidth)
ggplt <- ggplt + scale_color_manual(name="components", values=getStateColors(c('modified')), labels=legend[2]) + theme(legend.justification=c(1,1), legend.position=c(0.99,0.99))
}
}
return(ggplt)
}
#' @importFrom stats pnbinom qnorm dnorm
plotNormalTransformation <- function(model, state='unmodified') {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=class.univariate.hmm)[[1]] )
## Plot settings
cols <- getStateColors(c("unmodified","modified"))
## Transform the counts
df <- as.data.frame(model$bins)[,c('counts','state')]
# Transform to uniform space
mask <- df$state=='modified'
df$ucounts[!mask] <- pzinbinom(df$counts[!mask], model$weights[1], model$distributions[2,'size'], model$distributions[2,'prob'])
df$ucounts[mask] <- stats::pnbinom(df$counts[mask], model$distributions[3,'size'], model$distributions[3,'prob'])
# Transform to normal space
df$ncounts <- stats::qnorm(df$ucounts)
## Make the plots
subset <- df$ncounts[df$state==state]
breaks <- c(-Inf,sort(as.numeric(names(table(subset)))))
x <- seq(-4,4,0.1)
title <- paste0("Transformed emission density for state ",state)
ggplt <- ggplot() + geom_histogram(data=data.frame(ucounts=subset), aes_string(x='ucounts', y='..density..'), breaks=breaks, right=TRUE, col='black', fill=cols[state]) + theme_bw() + geom_line(data=data.frame(x=x, y=stats::dnorm(x, mean=0, sd=1)), aes_string(x='x', y='y')) + xlab("transformed counts") + labs(title=title)
return(ggplt)
}
plotBoxplot <- function(model) {
model <- suppressMessages( loadHmmsFromFiles(model, check.class=class.univariate.hmm)[[1]] )
## Boxplot
df <- as.data.frame(model$bins)[,c('state','counts')]
ggplt <- ggplot() + theme_bw() + geom_boxplot(data=df, aes_string(x='state', y='counts', fill='state')) + scale_fill_manual(values=getStateColors(c('zero-inflation','unmodified','modified')))
return(ggplt)
}
#' #' Plot a genome browser view
#' #'
#' #' Plot a simple genome browser view. This is useful for scripted genome browser snapshots.
#' #'
#' #' @param counts A \code{\link[GenomicRanges]{GRanges-class}} object with meta-data column 'counts'.
#' #' @param peaklist A named list() of \code{\link[GenomicRanges]{GRanges-class}} objects containing peak coordinates.
#' #' @param chr,start,end Chromosome, start and end coordinates for the plot.
#' #' @param countcol A character giving the color for the counts.
#' #' @param peakcols A character vector with colors for the peaks in \code{peaklist}.
#' #' @param style One of \code{c('peaks', 'density')}.
#' #' @param peakTrackHeight Relative height of the tracks given in \code{peaklist} compared to the \code{counts}.
#' #' @return A \code{\link[ggplot2:ggplot]{ggplot}} object.
#' #' @examples
#' #'## Get an example multiHMM ##
#' #'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' #' package="chromstaR")
#' #'model <- get(load(file))
#' #'## Plot genome browser snapshot
#' #'bins <- model$bins
#' #'bins$counts <- model$bins$counts.rpkm[,1]
#' #'plotGenomeBrowser(counts=bins, peaklist=model$peaks,
#' #' chr='chr12', start=1, end=1e6)
#' #'
#' plotGenomeBrowser2 <- function(counts, peaklist=NULL, chr, start, end, countcol='black', peakcols=NULL, style='peaks', peakTrackHeight=5) {
#'
#' ## Select ranges to plot
#' ranges2plot <- reduce(counts[counts@seqnames == chr & start(counts) >= start & start(counts) <= end])
#'
#' ## Counts
#' counts <- subsetByOverlaps(counts, ranges2plot)
#'
#' if (style == 'peaks') {
#' df <- data.frame(x=(start(counts)+end(counts))/2, counts=counts$counts) # plot triangles centered at middle of the bin
#' ggplt <- ggplot(df) + geom_area(aes_string(x='x', y='counts')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), axis.title = element_blank(), axis.ticks.x = element_blank(), axis.line = element_blank())
#' maxcounts <- max(counts$counts)
#' ggplt <- ggplt + scale_y_continuous(breaks=c(0, maxcounts))
#' } else if (style == 'density') {
#' df <- data.frame(xmin=start(counts), xmax=end(counts), counts=counts$counts)
#'
#' ggplt <- ggplot(df) + geom_rect(aes_string(xmin='xmin', xmax='xmax', ymin=0, ymax=4, alpha='counts')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank(), axis.line = element_blank())
#' } else {
#' stop("Unknown value '", style, "' for parameter 'style'. Must be one of c('peaks', 'density').")
#' }
#'
#' ## Peaks
#' if (!is.null(peaklist)) {
#' if (is.null(peakcols)) {
#' peakcols <- getDistinctColors(length(peaklist))
#' }
#' for (i1 in 1:length(peaklist)) {
#' p <- peakTrackHeight
#' peaks <- subsetByOverlaps(peaklist[[i1]], ranges2plot)
#' if (length(peaks) > 0) {
#' df <- data.frame(start=start(peaks), end=end(peaks), ymin=-p*i1, ymax=-p*i1+0.9*p)
#' ggplt <- ggplt + geom_rect(data=df, mapping=aes_string(xmin='start', xmax='end', ymin='ymin', ymax='ymax'), col=peakcols[i1], fill=peakcols[i1])
#' }
#' trackname <- names(peaklist)[i1]
#' df <- data.frame(x=start(counts)[1], y=-p*i1+0.5*p, label=trackname)
#' ggplt <- ggplt + geom_text(data=df, mapping=aes_string(x='x', y='y', label='label'), vjust=0.5, hjust=0.5, col=peakcols[i1])
#' }
#' }
#'
#' return(ggplt)
#' }
#' Plot a genome browser view
#'
#' Plot a simple genome browser view of \code{\link{chromstaR-objects}}. This is useful for scripted genome browser snapshots.
#'
#' @param model A \code{\link{uniHMM}}, \code{\link{multiHMM}} or \code{\link{combinedMultiHMM}} object or file that contains such an object.
#' @param chr,start,end Chromosome, start and end coordinates for the plot.
#' @param style One of \code{c('peaks', 'density')}.
#' @param peakHeight Height of the peak track relative to the count track.
#' @param peakColor Color for the peak track.
#' @param same.yaxis Whether or not the plots for the same mark have the same y-axis.
#' @return A \code{list()} of \code{\link[ggplot2:ggplot]{ggplot}} objects.
#' @export
#' @examples
#'## Get an example uniHMM ##
#'file <- system.file("data","H3K27me3-BN-rep1.RData", package="chromstaR")
#'model <- get(load(file))
#'plotGenomeBrowser(model, chr='chr12', start=1, end=1e6, style='peaks',
#' peakHeight=0.1)
#'
#'## Get an example multiHMM ##
#'file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'plotGenomeBrowser(model, chr='chr12', start=1, end=1e6, style='peaks',
#' peakHeight=0.1)
#'
#'## Get an example combinedMultiHMM ##
#'file <- system.file("data","combined_mode-differential.RData",
#' package="chromstaR")
#'model <- get(load(file))
#'plotlist <- plotGenomeBrowser(model, chr='chr12', start=1, end=1e6, style='peaks',
#' peakHeight=0.1)
#'
plotGenomeBrowser <- function(model, chr, start, end, style='peaks', peakHeight=0.2, peakColor='blue', same.yaxis=TRUE) {
model <- loadHmmsFromFiles(model, check.class = c('uniHMM', 'multiHMM', 'combinedMultiHMM'))[[1]]
## Select ranges to plot
bins <- model$bins
ranges2plot <- reduce(bins[bins@seqnames == chr & start(bins) >= start & start(bins) <= end, , drop=FALSE])
bins <- subsetByOverlaps(bins, ranges2plot)
if (is(model,'uniHMM')) {
peaklist <- list(model$peaks)
bins$counts.rpkm <- matrix(bins$counts.rpkm, ncol=1)
if (is.null(model$info)) {
model$info <- data.frame(file=NA, mark="peaks", condition=NA, replicate=1, ID='browser_snapshot')
}
names(peaklist) <- model$info$ID
colnames(bins$counts.rpkm) <- model$info$ID
} else {
peaklist <- model$peaks
}
## Determine y-limits, same within across marks ##
maxcounts <- apply(bins$counts.rpkm, 2, max)
maxcounts.permark <- list()
for (mark in unique(model$info$mark)) {
maxcounts.permark[[mark]] <- max(maxcounts[model$info$mark==mark])
}
ggplts <- list()
for (i1 in 1:nrow(model$info)) {
ID <- model$info$ID[i1]
if (same.yaxis) {
ymax.counts <- round(maxcounts.permark[[as.character(model$info$mark[i1])]])
} else {
ymax.counts <- round(maxcounts[[as.character(ID)]])
}
if (style == 'peaks') {
df <- data.frame(position=(start(bins)+end(bins))/2, RPKM=bins$counts.rpkm[,ID]) # plot triangles centered at middle of the bin
ggplt <- ggplot(df) + geom_area(aes_string(x='position', y='RPKM')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.line = element_blank())
} else if (style == 'density') {
df <- data.frame(xmin=start(bins), xmax=end(bins), RPKM=bins$counts.rpkm[,ID])
ggplt <- ggplot(df) + geom_rect(aes_string(xmin='xmin', xmax='xmax', ymin=0, ymax=4, alpha='RPKM')) + theme(panel.grid = element_blank(), panel.background = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line = element_blank(), axis.title.y = element_blank())
ggplt <- ggplt + xlab('position')
} else {
stop("Unknown value '", style, "' for parameter 'style'. Must be one of c('peaks', 'density').")
}
## Peaks
peaks <- subsetByOverlaps(peaklist[[ID]], ranges2plot)
if (length(peaks) > 0) {
df <- data.frame(start=start(peaks), end=end(peaks), ymin=-peakHeight*ymax.counts, ymax=0.1*(-peakHeight*ymax.counts))
ggplt <- ggplt + geom_rect(data=df, mapping=aes_string(xmin='start', xmax='end', ymin='ymin', ymax='ymax'), col=peakColor, fill=peakColor)
}
ggplt <- ggplt + scale_y_continuous(breaks=c(0, ymax.counts))
ggplt <- ggplt + coord_cartesian(xlim=c(start, end), ylim=c(-peakHeight*ymax.counts,ymax.counts))
ggplt <- ggplt + ggtitle(ID)
ggplts[[ID]] <- ggplt
}
return(ggplts)
}
Try the chromstaR package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.