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DataTrack-class.R
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DataTrack-class.R
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#' @include NumericTrack-class.R
#' @include ReferenceTrack-class.R
NULL
## DataTrack Class -----------------------------------------------------------
#' DataTrack class and methods
#'
#' A class to store numeric data values along genomic coordinates. Multiple
#' samples as well as sample groupings are supported, with the restriction of
#' equal genomic coordinates for a single observation across samples.
#'
#' Depending on the setting of the \code{type} display parameter, the data can
#' be plotted in various different forms as well as combinations thereof.
#' Supported plotting types are:
#'
#' \describe{
#'
#' \item{\code{p}:}{ simple xy-plot.}
#'
#' \item{\code{l}:}{ lines plot. In the case of multiple samples this plotting
#' type is not overly usefull since the points in the data matrix are connected
#' in column-wise order. Type \code{a} might be more appropriate in these
#' situations.}
#'
#' \item{\code{b}:}{ combination of xy-plot and lines plot.}
#'
#' \item{\code{a}:}{ lines plot of the column-wise average values.}
#'
#' \item{\code{s}:}{ sort and connect data points along the x-axis}
#'
#' \item{\code{S}:}{ sort and connect data points along the y-axis}
#'
#' \item{\code{g}:}{ add grid lines. To ensure a consitant look and feel across
#' multiple tracks, grid lines should preferentially be added by using the
#' \code{grid} display parameter.}
#'
#' \item{\code{r}:}{ add a regression line to the plot.}
#'
#' \item{\code{h}:}{ histogram-like vertical lines centered in the middle of
#' the coordinate ranges.}
#'
#' \item{\code{smooth}:}{ add a loess fit to the plot. The following display
#' parameters can be used to control the loess calculation: \code{span, degree,
#' family, evaluation}. See \code{\link{panel.loess}} for details.}
#'
#' \item{\code{histogram}:}{ plot data as a histogram, where the width of the
#' histogram bars reflects the width of the genomic ranges in the \code{range}
#' slot.}
#'
#' \item{\code{mountain}:}{ plot a smoothed version of the data relative to a
#' baseline, as defined by the \code{baseline} display parameter. The following
#' display parameters can be used to control the smoothing: \code{span, degree,
#' family, evaluation}. See \code{\link{panel.loess}} for details. The layout
#' of the plot can be further customized via the following display parameters:
#' \code{col.mountain, lwd.mountain, lty.mountain, fill.mountain}.}
#'
#' \item{\code{polygon}:}{ plot data as a polygon (similar to
#' \code{mountain}-type but without smoothing). Data are plotted relative to a
#' baseline, as defined by the \code{baseline} display parameter. The layout of
#' the plot can be further customized via the following display parameters:
#' \code{col.mountain, lwd.mountain, lty.mountain, fill.mountain}.}
#'
#' \item{\code{boxplot}:}{ plot the data as box-and-whisker plots. The layout
#' of the plot can be further customized via the following display parameters:
#' \code{box.ratio, box.width, varwidt, notch, notch.frac, levels.fos, stats,
#' coef, do.out}. See \code{\link{panel.bwplot}} for details.}
#'
#' \item{\code{gradient}:}{ collapse the data across samples and plot this
#' average value as a color-coded gradient. Essenitally this is similar to the
#' heatmap-type plot of a single sample. The layout of the plot can be further
#' customized via the display parameters \code{ncolor} and \code{gradient}
#' which control the number of gradient colors as well as the gradient base
#' colors, respectively.}
#'
#' \item{\code{heatmap}:}{ plot the color-coded values for all samples in the
#' form of a heatmap. The data for individual samples can be visually separated
#' by setting the \code{separator} display parameter. It's value is taken as
#' the amount of spacing in pixels in between two heatmap rows. The layout of
#' the plot can be further customized via the display parameters \code{ncolor}
#' and \code{gradient} which control the number of gradient colors as well as
#' the gradient base colors, respectively.}
#'
#' \item{\code{horizon}:}{ plot continuous data by cutting the y range into
#' segments and overplotting them with color representing the magnitude and
#' direction of deviation. This is particularly useful when comparing multiple
#' samples, in which case the horizon strips are stacked. See
#' \code{\link{horizonplot}} for details. Please note that the \code{origin}
#' and \code{horizonscale} arguments of the Lattice \code{horizonplot} function
#' are available as display parameters \code{horizon.origin} and
#' \code{horizon.scale}.}
#'
#' }
#'
#' For some of the above plotting-types the \code{groups} display parameter can
#' be used to indicate sample sub-groupings. Its value is supposed to be a
#' factor vector of similar length as the number of samples. In most cases, the
#' groups are shown in different plotting colors and data aggregation
#' operations are done in a stratified fashion.
#'
#' The \code{window} display parameter can be used to aggregate the data prior
#' to plotting. Its value is taken as the number of equal-sized windows along
#' the genomic coordinates of the track for which to compute average values.
#' The special value \code{auto} can be used to automatically determine a
#' reasonable number of windows which can be particularly useful when plotting
#' very large genomic regions with many data points.
#'
#' The \code{aggregation} parameter can be set to define the aggregation
#' function to be used when averaging in windows or across collapsed items. It
#' takes the form of either a function which should condense a numeric vector
#' into a single number, or one of the predefined options as character scalars
#' \code{"mean"}, \code{"median"} or \code{"sum"} for mean, median or
#' summation, respectively. Defaults to computing mean values for each sample.
#' Note that the predefined options can be much faster because they are
#' optimized to work on large numeric tables.
#'
#' @template DataTrack-class_param
#'
#' @name DataTrack-class
#'
#'
#' @return
#' The return value of the constructor function is a new object of class
#' \code{DataTrack} or \code{ReferenceDataTrack}.
#' @section Objects from the class:
#'
#' Objects can be created using the constructor function \code{DataTrack}.
#'
#' @author Florian Hahne
#' @inherit GdObject-class seealso
#'
#' @examples
#' ## Object construction:
#'
#' ## An empty object
#' DataTrack()
#'
#' ## from individual arguments
#' dat <- matrix(runif(400), nrow = 4)
#' dtTrack <- DataTrack(
#' start = seq(1, 1000, len = 100), width = 10, data = dat,
#' chromosome = 1, genome = "mm9", name = "random data"
#' )
#'
#' ## from GRanges
#' library(GenomicRanges)
#' gr <- GRanges(seqnames = "chr1", ranges = IRanges(seq(1, 1000, len = 100),
#' width = 10
#' ))
#' values(gr) <- t(dat)
#' dtTrack <- DataTrack(range = gr, genome = "mm9", name = "random data")
#'
#' ## from IRanges
#' dtTrack <- DataTrack(
#' range = ranges(gr), data = dat, genome = "mm9",
#' name = "random data", chromosome = 1
#' )
#'
#' ## from a data.frame
#' df <- as.data.frame(gr)
#' colnames(df)[1] <- "chromosome"
#' dtTrack <- DataTrack(range = df, genome = "mm9", name = "random data")
#' \dontshow{
#' ## For some annoying reason the postscript device does not know about
#' ## the sans font
#' if (!interactive()) {
#' font <- ps.options()$family
#' displayPars(dtTrack) <- list(fontfamily = font, fontfamily.title = font)
#' }
#' }
#'
#' ## Plotting
#' plotTracks(dtTrack)
#'
#' ## Track names
#' names(dtTrack)
#' names(dtTrack) <- "foo"
#' plotTracks(dtTrack)
#'
#' ## Subsetting and splitting
#' subTrack <- subset(dtTrack, from = 100, to = 300)
#' length(subTrack)
#' subTrack[1:2, ]
#' subTrack[, 1:2]
#' split(dtTrack, rep(1:2, each = 50))
#'
#' ## Accessors
#' start(dtTrack)
#' end(dtTrack)
#' width(dtTrack)
#' position(dtTrack)
#' width(subTrack) <- width(subTrack) - 5
#'
#' strand(dtTrack)
#' strand(subTrack) <- "-"
#'
#' chromosome(dtTrack)
#' chromosome(subTrack) <- "chrX"
#'
#' genome(dtTrack)
#' genome(subTrack) <- "mm9"
#'
#' range(dtTrack)
#' ranges(dtTrack)
#'
#' ## Data
#' values(dtTrack)
#' score(dtTrack)
#'
#' ## coercion
#' as(dtTrack, "data.frame")
#' @importFrom grDevices boxplot.stats
#'
#' @exportClass DataTrack
setClass("DataTrack",
contains = "NumericTrack",
representation = representation(data = "matrix", strand = "character"),
prototype = prototype(
columns = c("score"),
name = "DataTrack",
dp = DisplayPars(
aggregateGroups = FALSE,
aggregation = "mean",
missingAsZero = TRUE,
alpha.confint = 0.3,
amount = NULL,
baseline = NULL,
box.legend = FALSE,
box.ratio = 1,
box.width = NULL,
grid = FALSE,
cex.legend = 0.8,
cex.sampleNames = NULL,
cex = 0.7,
coef = 1.5,
col.baseline = NULL,
col.confint = NA,
col.boxplotFrame = .DEFAULT_SHADED_COL,
col.histogram = .DEFAULT_SHADED_COL,
col.horizon = NA,
col.mountain = NULL,
col.sampleNames = "white",
col = trellis.par.get("superpose.line")[["col"]],
collapse = FALSE,
degree = 1,
do.out = TRUE,
evaluation = 50,
factor = 0.5,
family = "symmetric",
fill.confint = NULL,
fill.histogram = NULL,
fill.horizon = c("#B41414", "#E03231", "#F7A99C", "#9FC8DC", "#468CC8", "#0165B3"),
fill.mountain = c("#CCFFFF", "#FFCCFF"),
fontface.legend = NULL,
fontfamily.legend = NULL,
fontsize.legend = NULL,
fontcolor.legend = .DEFAULT_SHADED_COL,
gradient = RColorBrewer::brewer.pal(9, "Blues"),
groups = NULL,
horizon.origin = 0,
horizon.scale = NULL,
jitter.x = FALSE,
jitter.y = FALSE,
levels.fos = NULL,
legend = TRUE,
lineheight.legend = NULL,
lty.baseline = NULL,
lty.mountain = NULL,
lwd.baseline = NULL,
lwd.mountain = NULL,
min.distance = 0,
na.rm = FALSE,
ncolor = 100,
notch.frac = 0.5,
notch = FALSE,
pch = 20,
separator = 0,
showColorBar = TRUE,
showSampleNames = FALSE,
size = NULL,
span = 1 / 5,
stackedBars = TRUE,
stats = boxplot.stats,
transformation = NULL,
type = "p",
varwidth = FALSE,
window = NULL,
windowSize = NULL,
ylim = NULL,
yTicksAt = NULL
)
)
)
## Initialize ----------------------------------------------------------------
## Only pass on the stuff to the GdObject initializer
#' @describeIn DataTrack-class Initialize.
#' @export
setMethod("initialize", "DataTrack", function(.Object, data = matrix(), strand, ...) {
## the display parameter defaults
.makeParMapping()
.Object <- .updatePars(.Object, "DataTrack")
.Object@data <- data
if (!missing(strand)) {
.Object@strand <- unique(strand)
}
.Object <- callNextMethod(.Object, ...)
return(.Object)
})
## The file-based version of the DataTrack class. This will mainly provide a means to dispatch to
## a special 'subset' method which should stream the necessary data from disk.
#' @describeIn DataTrack-class The file-based version of the `DataTrack-class`.
#' @exportClass ReferenceDataTrack
setClass("ReferenceDataTrack", contains = c("DataTrack", "ReferenceTrack"))
## This just needs to set the appropriate slots that are being inherited from ReferenceTrack because the
## multiple inheritance has some strange features with regards to method selection
#' @describeIn DataTrack-class Initialize.
#' @export
setMethod("initialize", "ReferenceDataTrack", function(.Object, stream, reference, mapping = list(),
args = list(), defaults = list(), ...) {
.Object <- selectMethod("initialize", "ReferenceTrack")(.Object = .Object, reference = reference, stream = stream,
mapping = mapping, args = args, defaults = defaults)
.Object <- callNextMethod(.Object, ...)
return(.Object)
})
## Constructor ---------------------------------------------------------------
## Constructor. The following arguments are supported:
## o range: an object of class 'GRanges' containing the data coordinates, or an object of class data.frame
## with the two mandatroy columns 'start' and 'end'. The coordinates (i.e., the length of the GRanges object)
## have to match the columns of the data matrix (see below).
## o data: a numeric matrix of data points with number of columns equal to the number of coordinates in 'range',
## or a numeric vector of appropriate length that will be coerced in such a one-row matrix.
## o start, end, width: numeric vectors of the item start and end coordinates, have to match the columns in 'data'
## o genome, chromosome: the reference genome and active chromosome for the track.
## o strand: character, display data on the plus strand ("+" or 0), the minus strand ("-" or 1) or both
## strands ("+-" or "-+" or 2). Currently has to be unique for the whole track.
## o name: the name of the track. This will be used for the title panel.
## All additional items in ... are being treated as DisplayParameters
#' @describeIn DataTrack-class Constructor function for `DataTrack-class`
#' @export
DataTrack <- function(range = NULL, start = NULL, end = NULL, width = NULL, data, chromosome, strand, genome,
name = "DataTrack", importFunction, stream = FALSE, ...) {
## Build a GRanges object from the inputs
wasGR <- is(range, "GRanges") || is.character(range)
fromFile <- is.character(range)
isStream <- FALSE
.missingToNull(c("strand", "chromosome", "importFunction", "genome"))
args <- list(strand = strand, chromosome = chromosome, genome = genome)
defs <- list(strand = "*", chromosome = "chrNA")
range <- .buildRange(
range = range, start = start, end = end, width = width, args = args, defaults = defs,
asIRanges = FALSE, chromosome = chromosome, genome = NA, trackType = "DataTrack",
importFun = importFunction, stream = stream
)
if (is.list(range)) {
isStream <- TRUE
slist <- range
range <- GRanges()
}
## Some default checking
if (length(unique(strand(range))) > 1) {
stop("The strand has to be unique for all ranges in a DataTrack object.")
}
if (!missing(data) && length(range) > 0) {
if (is.character(data)) {
if (!wasGR) {
stop("Columns indices for the data section are only allowed when 'range' is of class 'GRanges'")
}
mt <- is.na(match(data, colnames(values(range))))
if (any(mt)) {
warning("Unable to match data columns: ", paste(data[mt], collapse = ","))
}
data <- as.data.frame(values(range)[, data[!mt], drop = FALSE])
}
if (is.null(dim(data))) {
dim(data) <- c(1, length(data))
}
if (is.matrix(data)) {
data <- as.data.frame(t(data))
}
} else {
data <- if (ncol(values(range))) as.data.frame(values(range)) else matrix(nrow = 0, ncol = 0)
}
data <- .prepareDtData(data, len = length(range))
if (missing(chromosome) || is.null(chromosome)) {
chromosome <- if (length(range) > 0) .chrName(as.character(seqnames(range)[1])) else "chrNA"
}
genome <- .getGenomeFromGRange(range, ifelse(is.null(genome), character(), genome[1]))
values(range) <- NULL
if (!isStream) {
return(new("DataTrack",
chromosome = chromosome, strand = as.character(strand(range)), range = range,
name = name, genome = genome, data = data, ...
))
} else {
## A bit hackish but for some functions we may want to know which track type we need but at the
## same time we do not want to enforce this as an additional argument
e <- new.env()
e[["._trackType"]] <- "DataTrack"
environment(slist[["stream"]]) <- e
return(new("ReferenceDataTrack",
chromosome = chromosome, strand = as.character(strand(range)), range = range,
name = name, genome = genome, data = data, stream = slist[["stream"]], reference = slist[["reference"]],
mapping = slist[["mapping"]], args = args, defaults = defs, ...
))
}
}
## General accessors ---------------------------------------------------------
#' @describeIn DataTrack-class return the raw data values of the object, i.e.,
#' the data matrix in the `data` slot.
#' @export
setMethod("values", "DataTrack", function(x, all = FALSE) {
if (sum(dim(x@data)) == 0) {
x@data
} else {
sel <- if (all) rep(TRUE, ncol(x@data)) else seqnames(x) == chromosome(x)
x@data[, sel, drop = FALSE]
}
})
#' @describeIn DataTrack-class Replace the data matrix in the `data` slot.
#' @export
setReplaceMethod("values", "DataTrack", function(x, value) {
if (!is.matrix(value)) {
if (!is.numeric(value) || length(value) != length(x)) {
stop("Not numeric or invalid length of replacement vector.")
} else {
value <- t(as.matrix(value))
}
} else {
if (!is.numeric(value) || ncol(value) != length(x)) {
stop("Not numeric or dimensions of replacement value do not match.")
}
}
x@data <- value
return(x)
})
#' @describeIn DataTrack-class return a vector of strand specifiers for all
#' track items, in the form '+' for the Watson strand, '-' for the Crick
#' strand or '*' for either of the two.
#' @export
setMethod("strand", "DataTrack", function(x) x@strand)
#' @describeIn DataTrack-class replace the strand information for the track items.
#' The replacement value needs to be an appropriate scalar or vector of strand values.
#' @export
setReplaceMethod("strand", "DataTrack", function(x, value) {
if (!is.character(value) || length(value) != 1 || !value %in% c("+", "-", "*")) {
stop("Invalid replacement value")
}
x@strand <- value
return(x)
})
#' @describeIn DataTrack-class Split a `DataTrack` object by an appropriate
#' factor vector (or another vector that can be coerced into one).
#' The output of this operation is a list of `DataTrack` objects.
#' @export
setMethod("split", signature("DataTrack"),
definition = function(x, f, ...) {
f <- factor(f)
rs <- as.list(split(ranges(x), f))
ds <- split(t(values(x)), f)
nr <- nrow(values(x))
rnms <- rownames(values(x))
mapply(function(y, z) {
x@range <- y
x@data <- matrix(z, nrow = nr, byrow = TRUE, dimnames = list(rnms, NULL))
return(x)
}, rs, ds)
}
)
## Annotation Accessors ------------------------------------------------------
#' @describeIn DataTrack-class returns NULL since there is no grouping
#' information for the ranges in a `DataTrack`.
#' @export
setMethod("feature", signature(GdObject = "DataTrack"), function(GdObject) NULL)
#' @describeIn DataTrack-class this return the unaltered input object since
#' there is no grouping information for the ranges in a `DataTrack`.
#' @export
setReplaceMethod("feature", signature("DataTrack", "character"), function(GdObject, value) GdObject)
## Consolidate ---------------------------------------------------------------
## Collapse -----------------------------------------------------------------
#' @importFrom Biobase rowMax rowMin
#' @importFrom matrixStats rowMedians rowMins
.aggregator <- function(GdObject) {
agFun <- .dpOrDefault(GdObject, "aggregation", "mean")
if (is.list(agFun)) {
agFun <- agFun[[1]]
}
fun <- if (is.character(agFun)) {
switch(agFun,
"mean" = rowMeans,
"sum" = rowSums,
"median" = rowMedians,
"extreme" = function(x) apply(x, 1, .extreme),
"min" = Biobase::rowMin,
"max" = Biobase::rowMax,
rowMeans
)
} else {
if (is.function(agFun)) {
function(x) apply(x, 1, agFun)
} else {
stop(
"display parameter 'aggregation' has to be a function or a character ",
"scalar in c('mean', 'median', 'sum', 'min', 'max')"
)
}
}
return(fun)
}
.runaggregator <- function(GdObject) {
agFun <- .dpOrDefault(GdObject, "aggregation", "mean")
if (is.list(agFun)) {
agFun <- agFun[[1]]
}
fun <- if (is.character(agFun)) {
switch(agFun,
"mean" = runmean,
"sum" = runsum,
"median" = runmed2 <- function(x, k, na.rm = FALSE, ...) {
na.action <- if (na.rm) {
"na.omit"
} else {
"+Big_alternate"
}
runmed(x = as.numeric(x), k = k, na.action = na.action)
},
"min" = runqmin <- function(x, k, i = 1, ...) {
runq(x = x, k = k, i = i, ...)
},
"max" = runqmax <- function(x, k, i = k, ...) {
runq(x = x, k = k, i = i, ...)
},
runmean
)
} else {
if (is.function(agFun)) {
# na.rm currently not implemented...
function(x, k, na.rm = FALSE, endrule = "constant") {
ans <- vapply(0:(length(x) - k), function(offset) agFun(x[seq_len(k) + offset]), FUN.VALUE = numeric(1))
ans <- Rle(ans)
if (endrule == "constant") {
j <- (k + 1L) %/% 2L
runLength(ans)[1L] <- runLength(ans)[1L] + (j - 1L)
runLength(ans)[nrun(ans)] <- runLength(ans)[nrun(ans)] + (j - 1L)
}
ans
}
} else {
stop(
"display parameter 'aggregation' has to be a function or a character ",
"scalar in c('mean', 'median', 'sum', 'min', 'max')"
)
}
}
return(fun)
}
## For DataTracks we want to collapse data values using the aggregation function provided by calling .aggregator().
## In addition values can be aggregated over fixed window slices when gpar 'window' is not NULL, and using a sliding
## window approach when 'window' == -1
#' @describeIn DataTrack-class preprocess the track before plotting.
#' This will collapse overlapping track items based on the available resolution
#' and increase the width and height of all track objects to a minimum value
#' to avoid rendering issues. See collapsing for details.
#' @keywords internal
setMethod("collapseTrack", signature(GdObject = "DataTrack"), function(GdObject, diff = .pxResolution(coord = "x"), xrange) {
if (!length(GdObject)) {
return(GdObject)
}
## first the data transformation if needed
values(GdObject) <- score(GdObject)
collapse <- .dpOrDefault(GdObject, "collapse", FALSE)
min.width <- .dpOrDefault(GdObject, "min.width", 2)
min.distance <- max(0, .dpOrDefault(GdObject, "min.distance", 0))
## When an averaging window has been set, split the data up into these average chunks
window <- .dpOrDefault(GdObject, "window")
windowSize <- .dpOrDefault(GdObject, "windowSize")
missingAsZero <- .dpOrDefault(GdObject, "missingAsZero", TRUE)
if (!is.null(window) || collapse) {
GdObject <- GdObject[, order(range(GdObject))]
}
r <- ranges(GdObject)
drange <- c(floor(xrange[1]), ceiling(xrange[2]))
if (!is.null(window)) {
rr <- if (is(r, "GRanges")) ranges(r) else r
fw <- FALSE
if (window == "auto") {
window <- min(
ncol(values(GdObject)), 1000,
ceiling(width(range(rr)) / (min.width * diff))
)
}
if (window == "fixed") {
fw <- TRUE
window <- 100
}
if (!is.numeric(window) || length(window) != 1L) {
stop("gpar 'window' must be a numeric scalar")
}
window <- as.integer(window)
sc <- values(GdObject)
agFun <- .aggregator(GdObject)
if (window == 1) {
sc <- matrix(agFun(sc), ncol = 1)
rtmp <- IRanges(start = max(1, drange[1]), end = max(1, drange[2] - 1))
r <- if (is(r, "GRanges")) GRanges(seqnames = seqnames(r)[1], ranges = rtmp) else rtmp
} else if (window < 1) {
if (is.null(windowSize)) {
windowSize <- (max(GdObject) - min(GdObject)) / 100
}
if (windowSize %% 2 != 1) {
windowSize <- windowSize + 1
}
if (missingAsZero) {
rm <- vector("integer", width(range(range(GdObject))))
} else {
rm <- as.integer(rep(NA, width(range(range(GdObject)))))
}
ind <- unlist(mapply(function(x, y) x:y, start(GdObject), end(GdObject))) - min(GdObject) + 1
rm[ind] <- rep(sc[1, ], width(GdObject))
runAgFun <- .runaggregator(GdObject)
runwin <- suppressWarnings(runAgFun(Rle(as.numeric(rm)), k = windowSize, endrule = "constant", na.rm = TRUE))
seqSel <- findRun(as.integer(position(GdObject)) - min(GdObject) + 1, runwin)
newDat <- matrix(runValue(runwin)[seqSel], nrow = 1)
if (nrow(sc) > 1) {
newDat <- rbind(
newDat,
do.call(rbind, lapply(2:nrow(sc), function(x) {
rm[ind] <- rep(sc[x, ], width(GdObject))
runwin <- suppressWarnings(runAgFun(Rle(as.numeric(rm)), k = windowSize, endrule = "constant", na.rm = TRUE))
seqSel <- findRun(as.integer(position(GdObject)) - min(GdObject) + 1, runwin)
runValue(runwin)[seqSel]
# suppressWarnings(runValue(runmean(Rle(as.numeric(rm)), k = windowSize, endrule = "constant", na.rm = TRUE)))[seqSel]
}))
)
}
sc <- newDat
} else {
if (!is.null(window) && window > diff(drange)) {
window <- diff(drange)
}
if (!fw || is.null(windowSize)) {
windowSize <- diff(drange) %/% window
} else {
window <- max(1, diff(drange) %/% windowSize)
}
remain <- (diff(drange) - (window * windowSize)) / 2
ir <- IRanges(
start = seq(from = drange[1] + remain, to = drange[2] - remain - windowSize, length.out = window),
width = windowSize
)
if (remain > 0) {
ir <- c(
IRanges(start = drange[1], width = ceiling(remain)), ir,
IRanges(start = drange[2] - ceiling(remain), width = ceiling(remain))
)
}
ol <- as.matrix(findOverlaps(ir, rr))
scn <- lapply(split(ol[, 2], ol[, 1]), function(i) agFun(sc[, i, drop = FALSE]))
scn <- do.call(cbind, scn)
colnames(scn) <- as.character(unique(ol[, 1]))
sc <- matrix(NA, ncol = length(ir), nrow = nrow(scn))
sc[, as.integer(colnames(scn))] <- scn
r <- if (is(r, "GRanges")) {
GRanges(
seqnames = chromosome(GdObject), ranges = ir,
strand = unique(as.character(strand(GdObject)))
)
} else {
ir
}
}
GdObject@range <- r
GdObject@data <- sc
}
## If groups need to be averaged we have to do it here
groups <- .dpOrDefault(GdObject, "groups")
if (!is.null(groups) && .dpOrDefault(GdObject, "aggregateGroups", FALSE)) {
if (!is.factor(groups)) {
groups <- factor(groups)
}
agFun <- .aggregator(GdObject)
dat <- values(GdObject)
rownames(dat) <- groups
datNew <- do.call(rbind, lapply(levels(groups), function(x) agFun(t(dat[groups == x, , drop = FALSE]))))
GdObject@data <- datNew
displayPars(GdObject) <- list(groups = levels(groups))
}
## Compute native coordinate equivalent to 1 pixel and resize
r <- .resize(r, min.width, diff)
## Collapse overlapping ranges (less than minXDist space between them) including the associated attributes using
## "|" as separator. For both "strand" and "feature" we take the first available entry, which is not optimal but
## seems to be the sanest thing to do here...
if (collapse) {
minXDist <- min.distance * diff
rr <- if (is(r, "GRanges")) ranges(r) else r
if (minXDist < 1) {
## We have to fake smaller ranges because reduce will merge also neighboring ranges
width(rr) <- width(rr) - 1
rr <- reduce(rr, min.gapwidth = minXDist)
width(rr) <- width(rr) + 1
} else {
rr <- reduce(r, min.gapwidth = minXDist)
}
sc <- values(GdObject)
if (length(rr) == 1) {
r <- GRanges(seqnames = 1, strand = strand(GdObject)[1], ranges = rr)
GdObject@range <- r
GdObject@data <- matrix(rowMeans(sc, na.rm = TRUE), ncol = 1)
} else if (length(rr) < length(r)) {
startInd <- sort(unique(vapply(start(rr), function(x) which(start(r) == x), FUN.VALUE = numeric(1L))))
st <- strand(GdObject)
startInd <- if (tail(startInd, 1) == length(r)) c(startInd, length(r) + 1) else c(startInd, length(r))
vsplit <- split(t(as.data.frame(sc, stringsAsFactors = FALSE)), cut(seq_len(length(r)), startInd, iclude.lowest = TRUE, right = FALSE))
agFun <- .dpOrDefault(GdObject, "aggregation", "mean")
if (is.list(agFun)) {
agFun <- agFun[[1]]
}
newScore <- if (is.character(agFun)) {
switch(agFun,
"mean" = lapply(vsplit, function(x) rowMeans(matrix(x, nrow = nrow(sc), byrow = TRUE), na.rm = TRUE)),
"sum" = lapply(vsplit, function(x) rowSums(matrix(x, nrow = nrow(sc), byrow = TRUE), na.rm = TRUE)),
"median" = lapply(vsplit, function(x) rowMedians(matrix(x, nrow = nrow(sc), byrow = TRUE), na.rm = TRUE)),
lapply(vsplit, function(x) rowMeans(matrix(x, nrow = nrow(sc), byrow = TRUE), na.rm = TRUE))
)
} else {
if (is.function(agFun)) {
lapply(vsplit, function(x) apply(matrix(x, nrow = nrow(sc), byrow = TRUE), 1, function(y) agFun(y)[1]))
} else {
stop("display parameter 'aggregation' has to be a function or a character ", "scalar in c('mean', 'median', 'sum')")
}
}
newScore <- unlist(newScore)
r <- GRanges(seqnames = seq_len(length(rr)), strand = st, ranges = rr)
GdObject@data <- newScore
GdObject@range <- r
}
}
## Reconstruct the RangedData object and return
GdObject@range <- r
return(GdObject)
})
## Subset --------------------------------------------------------------------
#' @describeIn DataTrack-class subset the items in the `DataTrack` object.
#' This is essentially similar to subsetting of the `GRanges` object in the
#' `range` slot. For most applications, the subset method may be more appropriate.
#' @export
setMethod("[", signature(x = "DataTrack"), function(x, i, j, ..., drop = FALSE) {
x <- .deepCopyPars(x)
if (!missing(i)) {
x@data <- x@data[i, , drop = drop]
displayPars(x) <- list(groups = as.vector(.dpOrDefault(x, "groups")[i]))
}
if (!missing(j)) {
x@range <- x@range[j, ]
if (ncol(x@data) > 0) {
x@data <- x@data[, j, drop = drop]
}
}
return(x)
})
## For DataTracks we cut exactly, and also reduce to the current chromosome unless told explicitly not to
#' @describeIn DataTrack-class Subset a `DataTrack` by coordinates
#' and sort if necessary.
#' @export
setMethod("subset", signature(x = "DataTrack"), function(x, from = NULL, to = NULL, sort = FALSE, drop = TRUE, use.defaults = TRUE, ...) {
## Subset to a single chromosome first
if (drop) {
csel <- seqnames(x) != chromosome(x)
if (any(csel)) {
x <- x[, !csel]
}
}
if (!length(x)) {
return(x)
}
ranges <- if (use.defaults) .defaultRange(x, from = from, to = to) else c(from = ifelse(is.null(from), -Inf, from), to = ifelse(is.null(to), Inf, to))
x <- x[, start(x) >= ranges["from"] & end(x) <= ranges["to"]]
if (sort) {
x <- x[, order(range(x))]
}
return(x)
})
## ReferenceDataTracks need to stream the data from file and then pass the results on to the next method
#' @describeIn DataTrack-class Subset a `ReferenceDataTrack` by coordinates
#' and sort if necessary.
#' @export
setMethod("subset", signature(x = "ReferenceDataTrack"), function(x, from, to, chromosome, ...) {
## We only need to reach out into the referenced file once if the range is already contained in the object
if (missing(from) || is.null(from) || missing(to) || is.null(to)) {
stop("Need both start and end location to subset a ReferenceDataTrack")
}
if (missing(chromosome) || is.null(chromosome)) {
chromosome <- Gviz::chromosome(x)
}
subRegion <- GRanges(seqnames = chromosome[1], ranges = IRanges(start = from, end = to))
if (length(ranges(x)) == 0 || !all(overlapsAny(ranges(x), subRegion))) {
vals <- x@stream(x@reference, subRegion)
x@range <- vals
mcols(x@range) <- NULL
x@data <- .prepareDtData(if (ncol(values(vals))) as.data.frame(values(vals)) else matrix(nrow = 0, ncol = 0), length(vals))
chromosome(x) <- chromosome[1]
}
return(callNextMethod(x = x, from = from, to = to, drop = FALSE, ...))
})
## Position ------------------------------------------------------------------
setMethod("score", signature("DataTrack"), function(x, from = NULL, to = NULL, sort = FALSE, transformation = TRUE, ...) {
if (!is.null(from) && !is.null(to)) {
x <- subset(x, from = from, to = to, sort = sort, ...)
}
vals <- values(x)
## apply data transformation if one is set up
trans <- .dpOrDefault(x, "transformation")
if (is.list(trans)) {
trans <- trans[[1]]
}
if (transformation && !is.null(trans)) {
if (!is.function(trans) || length(formals(trans)) != 1L) {
stop("gpar 'transformation' must be a function with a single argument")
}
test <- trans(vals)
if (!is.numeric(test) || !is.matrix(test) || !all(dim(test) == dim(vals))) {
stop(
"The function in gpar 'transformation' results in invalid output.\n",
"It has to return a numeric matrix with the same dimensions as the input data."
)
}
vals <- test
}
return(vals)
})
## DrawAxis ------------------------------------------------------------------
#' @describeIn DataTrack-class add a y-axis to the title panel of a track.
#' @export
setMethod("drawAxis", signature(GdObject = "DataTrack"), function(GdObject, ...) {
if (as.logical(.dpOrDefault(GdObject, "legend", FALSE)) && !is.null(.dpOrDefault(GdObject, ".__groupLevels"))) {
pushViewport(viewport(
y = 1, height = unit(1, "npc") - unit(.dpOrDefault(GdObject, ".__verticalSpace"), "inches"),
just = c(0.5, 1)
))
on.exit(popViewport(1))
}
type <- match.arg(.dpOrDefault(GdObject, "type", "p"), .PLOT_TYPES, several.ok = TRUE)
isOnlyHoriz <- length(setdiff(type, "horizon")) == 0
if (!isOnlyHoriz && .dpOrDefault(GdObject, "showAxis", TRUE)) {
callNextMethod()
} else {
if (.dpOrDefault(GdObject, "showSampleNames", FALSE)) {
groups <- .dpOrDefault(GdObject, "groups")
sn <- if (is.null(groups)) rownames(values(GdObject)) else rev(unlist(split(rownames(values(GdObject)), factor(groups))))
cex.sn <- .dpOrDefault(GdObject, "cex.sampleNames", .dpOrDefault(GdObject, "cex.axis", 1))
col.cn <- .dpOrDefault(GdObject, "col.sampleNames", "white")
wd <- max(as.numeric(convertWidth(stringWidth(sn) + unit(10, "points"), "npc"))) * cex.sn
samNames <- viewport(x = 1, width = wd, just = 1, yscale = c(-0.05, 1.05))
pushViewport(samNames)
nr <- nrow(values(GdObject))
if (nr > 1) {
yy <- head(seq(0.05, 0.95, len = nr + 1), -1)
yy <- yy + diff(yy)[[1]] / 2
} else {
yy <- 0.5
}
grid.text(x = rep(0.5, nr), y = yy, label = rev(sn), just = 0.5, gp = gpar(cex = cex.sn, col = col.cn))
popViewport(1)
}
}
})
## DrawGD --------------------------------------------------------------------
## Helper function to return the absolute extreme value in a vector
.extreme <- function(x) if (all(is.na(x))) NA else x[which.max(abs(x))]
## Map numeric range to values from 1 to n
.z2icol <- function(z, n, xrange = range(z, na.rm = TRUE)) {
res <- round((z - xrange[1]) / diff(xrange) * (n - 1)) + 1
res[res > n] <- n
res[res < 1] <- 1
return(res)
}
#' @describeIn DataTrack-class plot the object to a graphics device.
#' The return value of this method is the input object, potentially updated
#' during the plotting operation. Internally, there are two modes in which the
#' method can be called. Either in 'prepare' mode, in which case no plotting is
#' done but the object is preprocessed based on the available space, or in
#' 'plotting' mode, in which case the actual graphical output is created.
#' Since subsetting of the object can be potentially costly, this can be
#' switched off in case subsetting has already been performed before or
#' is not necessary.
#'
#' @importFrom grDevices boxplot.stats
#' @importFrom latticeExtra panel.horizonplot
#' @export
setMethod("drawGD", signature("DataTrack"), function(GdObject, minBase, maxBase, prepare = FALSE, subset = TRUE, ...) {
debug <- .dpOrDefault(GdObject, "debug", FALSE)
if ((is.logical(debug) && debug) || debug == "prepare") {
browser()
}
imageMap(GdObject) <- NULL
type <- .dpOrDefault(GdObject, "type", "p")
type <- match.arg(type, .PLOT_TYPES, several.ok = TRUE)
## Grouping may be useful for some of the plot types, may be ignored for others
vals <- values(GdObject)
groups <- .dpOrDefault(GdObject, "groups")
if (!is.null(groups) && length(groups) != nrow(vals)) {
stop("'groups' must be a vector of the same length as the number of rows in the data matrix (", nrow(vals), ")")
}
if (!is.null(groups) && !is.factor(groups)) {
groups <- factor(groups)
}
stacked <- .dpOrDefault(GdObject, "stackedBars", FALSE)
## The general "col" parameter should be the default for all relevant colors except when there are groups.
pcols <- .getPlottingFeatures(GdObject)
## In prepare mode we collapse the track to allow for aggregation and so on since we need the final data
## values to draw the axis.
if (prepare) {
if (subset) {
GdObject <- subset(GdObject, from = minBase, to = maxBase)
}
xscale <- if (!.dpOrDefault(GdObject, "reverseStrand", FALSE)) c(minBase, maxBase) else c(maxBase, minBase)
pushViewport(viewport(xscale = xscale, yscale = c(0, 1), clip = TRUE))
diff <- .pxResolution(coord = "x")
GdObject <- collapseTrack(GdObject, diff = diff, xrange = c(minBase, maxBase))
popViewport(1)
## If we have groups and stacked histograms we have to adjust the ylim values, also for regular histograms
if ("histogram" %in% type) {
vals <- values(GdObject)
groups <- rep(groups, ncol(vals))
ylim <- .dpOrDefault(GdObject, "ylim")
if (!is.null(groups) && nlevels(groups) > 1) {
if (ncol(vals)) {
valsS <- split(vals, groups)
valsS <- do.call(cbind, lapply(seq_along(valsS), function(i) {
tmp <- t(matrix(valsS[[i]], ncol = ncol(vals)))
if (ncol(tmp)) {
colnames(tmp) <- rep(names(valsS)[i], ncol(tmp))
}
tmp
}))
} else {
valsS <- matrix(nrow = nlevels(groups), ncol = 0, dimnames = list(levels(groups)))
}
displayPars(GdObject) <- list(".__valsS" = valsS)
if (stacked == TRUE && is.null(ylim)) {
ylim <- suppressWarnings(range(unlist(apply(valsS, 1, function(x) {
x <- x[!is.na(x)]
sel <- x >= 0
tmp <- NULL
if (!all(is.na(sel))) {
if (any(sel)) {
tmp <- c(min(x[sel]), sum(x[sel]))
}
if (any(!sel)) {
tmp <- c(max(x[!sel]), tmp, sum(x[!sel]))
}
}
tmp
}))))
if (length(type) > 1) {
ylim <- range(c(ylim, vals))
}
displayPars(GdObject) <- list(ylim = ylim)
}
} else {
if (is.null(ylim)) {
valsA <- t(vals)