Nothing
R/inferCNV_heatmap.R
In infercnv: Infer Copy Number Variation from Single-Cell RNA-Seq Data
Defines functions
.pairwise_dendrogram
.single_element_dendrogram
.apply_logistic_val_adj
depress_log_signal_midpt_val
depress_low_signal_midpt_ratio
get.sep
gdist
heatmap.cnv
.plot.text
.scale.x
.scale.data
.call.FUN
.is.grouped
.invalid
.plot_cnv_references
.plot_observations_layout
.plot_cnv_observations
plot_cnv
get_group_color_palette
Documented in
plot_cnv
#!/usr/bin/env Rscript
# Returns the color palette for contigs.
#
# Returns:
# Color Palette
get_group_color_palette <- function(){
return(colorRampPalette(RColorBrewer::brewer.pal(12,"Set3")))
}
#' @description Formats the data and sends it for plotting.
#'
#' @title Plot the matrix as a heatmap, with cells as rows and genes as columns, ordered according to chromosome
#'
#' @param infercnv_obj infercnv object
#' @param out_dir Directory in which to save pdf and other output.
#' @param title Plot title.
#' @param obs_title Title for the observations matrix.
#' @param ref_title Title for the reference matrix.
#' @param cluster_by_groups Whether to cluster observations by their annotations or not. Using this ignores k_obs_groups.
#' @param cluster_references Whether to cluster references within their annotations or not. (dendrogram not displayed)
#' @param plot_chr_scale Whether to scale the chromosme width on the heatmap based on their actual size rather than just the number of expressed genes.
#' @param chr_lengths A named list of chromsomes lengths to use when plot_chr_scale=TRUE, or else chromosome size is assumed to be the last chromosome's stop position + 10k bp
#' @param k_obs_groups Number of groups to break observation into.
#' @param contig_cex Contig text size.
#' @param x.center Value on which to center expression.
#' @param x.range vector containing the extreme values in the heatmap (ie. c(-3,4) )
#' @param hclust_method Clustering method to use for hclust.
#' @param custom_color_pal Specify a custom set of colors for the heatmap.
#' Has to be in the shape color.palette(c("darkblue", "white", "darkred"),
#' c(2, 2))
#' @param color_safe_pal Logical indication of using a color blindness safe palette.
#' @param output_filename Filename to save the figure to.
#' @param output_format format for heatmap image file (default: 'png'), options('png', 'pdf', NA)
#' If set to NA, will print graphics natively
#' @param png_res Resolution for png output.
#' @param dynamic_resize Factor (>= 0) by which to scale the dynamic resize of the observation
#' heatmap and the overall plot based on how many cells there are.
#' Default is 0, which disables the scaling. Try 1 first if you want to enable.
#' @param ref_contig If given, will focus cluster on only genes in this contig.
#' @param write_expr_matrix Includes writing a matrix file containing the expression data that is plotted in the heatmap.
#' @param useRaster Whether to use rasterization for drawing heatmap. Only disable if it produces an error as it is much faster than not using it.
#'
#' @return A list of all relevent settings used for the plotting to be able to reuse them in another plot call while keeping consistant plotting settings, most importantly x.range.
#'
#'
#' @export
#'
#' @examples
#' # data(infercnv_data_example)
#' # data(infercnv_annots_example)
#' # data(infercnv_genes_example)
#'
#' # infercnv_object_example <- infercnv::CreateInfercnvObject(raw_counts_matrix=infercnv_data_example,
#' # gene_order_file=infercnv_genes_example,
#' # annotations_file=infercnv_annots_example,
#' # ref_group_names=c("normal"))
#'
#' # infercnv_object_example <- infercnv::run(infercnv_object_example,
#' # cutoff=1,
#' # out_dir=tempfile(),
#' # cluster_by_groups=TRUE,
#' # denoise=TRUE,
#' # HMM=FALSE,
#' # num_threads=2,
#' # no_plot=TRUE)
#'
#' data(infercnv_object_example)
#'
#' plot_cnv(infercnv_object_example,
#' out_dir=tempfile(),
#' obs_title="Observations (Cells)",
#' ref_title="References (Cells)",
#' cluster_by_groups=TRUE,
#' x.center=1,
#' x.range="auto",
#' hclust_method='ward.D',
#' color_safe_pal=FALSE,
#' output_filename="infercnv",
#' output_format="png",
#' png_res=300,
#' dynamic_resize=0
#' )
#'
plot_cnv <- function(infercnv_obj,
out_dir=".",
title="inferCNV",
obs_title="Observations (Cells)",
ref_title="References (Cells)",
cluster_by_groups=TRUE,
cluster_references=TRUE,
plot_chr_scale=FALSE,
chr_lengths=NULL,
k_obs_groups = 3,
contig_cex=1,
x.center=mean(infercnv_obj@expr.data),
x.range="auto", #NA,
hclust_method='ward.D',
custom_color_pal=NULL,
color_safe_pal=FALSE,
output_filename="infercnv",
output_format="png", #pdf, png, NA
png_res=300,
dynamic_resize=0,
ref_contig = NULL,
write_expr_matrix=FALSE,
useRaster=TRUE) {
# arg validations
if (! hclust_method %in% C_HCLUST_METHODS) {
stop(sprintf("Error, hclust_method: %s is not supported", hclust_method))
}
if ( (! is.na(output_format) ) & (! output_format %in% C_OUTPUT_FORMAT) ) {
stop(sprintf("Error, output_format: %s is not supported", output_format) )
}
if(!file.exists(out_dir)){
dir.create(out_dir)
}
plot_data = infercnv_obj@expr.data
flog.info(paste("::plot_cnv:Start", sep=""))
flog.info(paste("::plot_cnv:Current data dimensions (r,c)=",
paste(dim(plot_data), collapse=","),
" Total=", sum(plot_data, na.rm=TRUE),
" Min=", min(plot_data, na.rm=TRUE),
" Max=", max(plot_data, na.rm=TRUE),
".", sep=""))
flog.info(paste("::plot_cnv:Depending on the size of the matrix",
" this may take a moment.",
sep=""))
if (write_expr_matrix) {
expr_dat_file <- paste(out_dir, paste("expr.", output_filename, ".dat", sep=""), sep="/")
if ("matrix" %in% is(plot_data)) {
write.table(plot_data, file=expr_dat_file, quote=FALSE, sep="\t")
}
}
if (! any(is.na(x.range))) {
if ( (length(x.range) == 1) & (x.range[1] == "auto") ) {
# examine distribution of data that's off-center, since much of the center could
# correspond to a mass of data that has been wiped out during noise reduction
quantiles = quantile(plot_data[plot_data != x.center], c(0.01, 0.99))
# determine max distance from the center.
delta = max( abs( c(x.center - quantiles[1], quantiles[2] - x.center) ) )
low_threshold = x.center - delta
high_threshold = x.center + delta
x.range = c(low_threshold, high_threshold)
flog.info(sprintf("plot_cnv(): auto thresholding at: (%f , %f)", low_threshold, high_threshold))
} else {
# use defined values
low_threshold = x.range[1]
high_threshold = x.range[2]
if (low_threshold > x.center | high_threshold < x.center | low_threshold >= high_threshold) {
stop(paste("Error, problem with relative values of x.range: ", x.range, ", and x.center: ", x.center))
}
}
plot_data[plot_data < low_threshold] <- low_threshold
plot_data[plot_data > high_threshold] <- high_threshold
infercnv_obj@expr.data <- plot_data #because used again below...
}
# Contigs
contigs = infercnv_obj@gene_order[[C_CHR]]
unique_contigs <- unique(contigs)
n_contig <- length(unique_contigs)
ct.colors <- get_group_color_palette()(n_contig)
names(ct.colors) <- unique_contigs
# Select color palette
if (!is.null(custom_color_pal)) {
custom_pal = custom_color_pal
} else if (color_safe_pal == FALSE){
custom_pal <- color.palette(c("darkblue", "white", "darkred"),
c(2, 2))
} else {
custom_pal <- color.palette(c("purple3", "white", "darkorange2"),
c(2, 2))
}
## Row separation based on reference
ref_idx <- NULL
if (has_reference_cells(infercnv_obj)) {
ref_idx <- unlist(infercnv_obj@reference_grouped_cell_indices)
ref_idx = ref_idx[order(ref_idx)]
}
# Column seperation by contig and label axes with only one instance of name
contig_tbl <- table(contigs)[unique_contigs]
col_sep <- cumsum(contig_tbl)
col_sep <- col_sep[-1 * length(col_sep)] ## FIXME: removing last entry?
# These labels are axes labels, indicating contigs at the first column only
# and leaving the rest blank.
# contig_labels <- c()
# contig_names <-c()
# for (contig_name in names(contig_tbl)){
# contig_labels <- c(contig_labels,
# contig_name,
# rep("", contig_tbl[contig_name] - 1))
# contig_names <- c(contig_names,rep(contig_name,contig_tbl[contig_name]))
# }
contig_labels = names(contig_tbl)
contig_names = unlist(lapply(contig_labels, function(contig_name) {rep(contig_name, contig_tbl[contig_name])}))
# contig_sizes = vapply(contig_labels, function(contig_name) {contig_tbl[contig_name]}, integer(1))
# contig_positions = c(1, cumsum(contig_sizes[seq_len(length(contig_sizes) - 1)]) + 1)
# Calculate how many rows will be made for the number of columns in the grouping key
grouping_key_coln <- c()
obs_annotations_names <- names(infercnv_obj@observation_grouped_cell_indices)
# obs_annotations_groups: integer vec named by cells, set to index according to category name vec above.
obs_annotations_groups = rep(-1, length(colnames(infercnv_obj@expr.data))) # init
names(obs_annotations_groups) = colnames(infercnv_obj@expr.data)
obs_index_groupings = infercnv_obj@observation_grouped_cell_indices
counter <- 1
for (obs_index_group in obs_index_groupings) {
obs_annotations_groups[ obs_index_group ] <- counter
counter <- counter + 1
}
# restrict to just the obs indices
if (! is.null(ref_idx)) {
obs_annotations_groups <- obs_annotations_groups[-ref_idx]
}
if (is.null(dynamic_resize) | dynamic_resize < 0) {
flog.warn(paste("invalid dynamic_resize value: ", dynamic_resize, sep=""))
dynamic_resize = 0
}
dynamic_extension = 0
nobs = length(unlist(infercnv_obj@observation_grouped_cell_indices))
if (nobs > 200) {
dynamic_extension = dynamic_resize * 3.6 * (nobs - 200)/200
}
grouping_key_coln[1] <- floor(123/(max(nchar(obs_annotations_names)) + 4)) ## 123 is the max width in number of characters, 4 is the space taken by the color box itself and the spacing around it
if (grouping_key_coln[1] < 1) {
grouping_key_coln[1] <- 1
}
name_ref_groups = names(infercnv_obj@reference_grouped_cell_indices)
if (is.null(name_ref_groups)) {
grouping_key_coln[2] = 1
} else {
grouping_key_coln[2] <- floor(123/(max(nchar(name_ref_groups)) + 4)) ## 123 is the max width in number of characters, 4 is the space taken by the color box itself and the spacing around it
if (grouping_key_coln[2] < 1) {
grouping_key_coln[2] <- 1
}
}
grouping_key_rown <- c()
grouping_key_rown[1] <- ceiling(length(obs_annotations_names)/grouping_key_coln[1])
grouping_key_rown[2] <- ceiling(length(name_ref_groups)/grouping_key_coln[2])
# Calculate how much bigger the output needs to be to accodomate for the grouping key
grouping_key_height <- c((grouping_key_rown[2] + 2) * 0.175, (grouping_key_rown[1] + 3) * 0.175)
# Rows observations, Columns CHR
if (! is.na(output_format)) {
if (output_format == "pdf") {
pdf(paste(out_dir, paste(output_filename, ".pdf", sep=""), sep="/"),
useDingbats=FALSE,
width=11,
height=(8.22 + sum(grouping_key_height)) + dynamic_extension,
paper="special")
} else if (output_format == "png") {
png(paste(out_dir, paste(output_filename, ".png", sep=""), sep="/"),
width=10,
height=(8.22 + sum(grouping_key_height)) + dynamic_extension,
units="in",
res=png_res)
}
}
# Plot observations
## Make Observation Samples
## Remove observation col names, too many to plot
## Will try and keep the reference names
## They are more informative anyway
obs_data <- infercnv_obj@expr.data
if (!is.null(ref_idx)){
obs_data <- plot_data[, -ref_idx, drop=FALSE]
if (ncol(obs_data) == 1) {
# hack for dealing with single entries
plot_data <- cbind(obs_data, obs_data)
names(obs_data) <- c("", names(obs_data)[1])
}
}
obs_data <- t(obs_data)
# Subsample the data to only the references and update the ref_group indexes to match their new indexes
# ref_data_t <- plot_data[, ref_idx, drop=FALSE]
ref_data_t <- NULL
updated_ref_groups <- list()
current_ref_count <- 1
current_grp_idx <- 1
plot_data <-infercnv_obj@expr.data
ref_groups = infercnv_obj@reference_grouped_cell_indices
for (ref_grp in ref_groups) {
ref_data_t <- cbind(ref_data_t, plot_data[, ref_grp, drop=FALSE])
updated_ref_groups[[current_grp_idx]] = seq(current_ref_count, current_ref_count + length(ref_grp) - 1)
current_ref_count <- current_ref_count + length(ref_grp)
current_grp_idx <- current_grp_idx + 1
}
ref_groups <- updated_ref_groups
nb_breaks <- 16
# breaksList_t <-
# seq(min(min(obs_data, na.rm=TRUE), min(ref_data_t, na.rm=TRUE)),
# max(max(obs_data,na.rm=TRUE), max(ref_data_t, na.rm=TRUE)),
# length.out=nb_breaks)
breaksList_t <- seq(x.range[1], x.range[2], length.out=nb_breaks)
gene_position_breaks = NULL
if (plot_chr_scale) {
# gene table to heatmap width
chr_name_list = unique(infercnv_obj@gene_order[["chr"]])
# get average distance for tail end?
# optionally give vector of chr lengths
# if chr_lengths not given, take furthest gene and add 10k bp
if (is.null(chr_lengths)) {
chr_lengths = c()
for (chr_name in chr_name_list) {
chr_lengths = c(chr_lengths, max(infercnv_obj@gene_order$stop[which(infercnv_obj@gene_order$chr == chr_name)]) + 10000)
}
names(chr_lengths) = chr_name_list
}
gene_position_breaks = vector(mode="integer", length=(length(unlist(infercnv_obj@gene_order$chr)) + 1))
sum_previous_contigs = 0
gene_position_breaks[1] = 1
current_idx = 2
col_sep_idx = 1
for (chr_name in chr_name_list) {
index_pos = which(infercnv_obj@gene_order$chr == chr_name)
latest_position = 1
for (i in index_pos[2:length(index_pos)]) {
gene_position_breaks[current_idx] = sum_previous_contigs + ((latest_position + infercnv_obj@gene_order$start[i]) / 2)
latest_position = max(infercnv_obj@gene_order$stop[i], latest_position)
current_idx = current_idx + 1
}
gene_position_breaks[current_idx] = sum_previous_contigs + chr_lengths[chr_name]
current_idx = current_idx + 1
sum_previous_contigs = sum_previous_contigs + chr_lengths[chr_name]
if (col_sep_idx != length(chr_name_list)) {
col_sep[col_sep_idx] = sum_previous_contigs
col_sep_idx = col_sep_idx + 1
}
}
}
# Create file base for plotting output
force_layout <- .plot_observations_layout(grouping_key_height=grouping_key_height, dynamic_extension=dynamic_extension)
.plot_cnv_observations(infercnv_obj=infercnv_obj,
obs_data=obs_data,
file_base_name=out_dir,
do_plot=!is.na(output_format),
output_filename_prefix=output_filename,
cluster_contig=ref_contig,
contigs=contigs,
contig_colors=ct.colors[contigs],
contig_labels=contig_labels,
contig_names=contig_names,
col_pal=custom_pal,
contig_seps=col_sep,
num_obs_groups=k_obs_groups,
obs_annotations_groups=obs_annotations_groups,
obs_annotations_names=obs_annotations_names,
grouping_key_coln=grouping_key_coln[1],
cluster_by_groups=cluster_by_groups,
cnv_title=title,
cnv_obs_title=obs_title,
contig_lab_size=contig_cex,
breaksList=breaksList_t,
gene_position_breaks=gene_position_breaks,
x.center=x.center,
hclust_method=hclust_method,
layout_lmat=force_layout[["lmat"]],
layout_lhei=force_layout[["lhei"]],
layout_lwid=force_layout[["lwid"]],
useRaster=useRaster)
obs_data <- NULL
if(!is.null(ref_idx)){
.plot_cnv_references(infercnv_obj=infercnv_obj,
ref_data=ref_data_t,
ref_groups=ref_groups,
name_ref_groups=name_ref_groups,
cluster_references=cluster_references,
hclust_method=hclust_method,
grouping_key_coln=grouping_key_coln[2],
col_pal=custom_pal,
contig_seps=col_sep,
file_base_name=out_dir,
do_plot=!is.na(output_format),
output_filename_prefix=output_filename,
cnv_ref_title=ref_title,
breaksList=breaksList_t,
gene_position_breaks=gene_position_breaks,
x.center=x.center,
layout_add=TRUE,
useRaster=useRaster)
}
if (! is.na(output_format)) {
dev.off()
}
return(list(cluster_by_groups = cluster_by_groups,
k_obs_groups = k_obs_groups,
contig_cex = contig_cex,
x.center = x.center,
x.range = x.range,
hclust_method = hclust_method,
color_safe_pal = color_safe_pal,
output_format = output_format,
png_res = png_res,
dynamic_resize = dynamic_resize))
}
# TODO Tested, test make files so turned off but can turn on and should pass.
# Plot the observational samples
#
# Args:
#' @param obs_data Data to plot as observations. Rows = Cells, Col = Genes.
#' @param col_pal The color palette to use.
#' @param contig_colors The colors for the contig bar.
#' @param contig_labels The labels for the contigs.
#' @param contig_names Names of the contigs.
#' @param contig_seps Indices for line seperators of contigs.
#' @param num_obs_groups Number of groups of observations to create.
#' @param file_base_name Base of the file to used to make output file names.
#' @param do_plot If FALSE, only write text files and does not run plotting.
#' @param cnv_title Title of the plot.
#' @param cnv_obs_title Title for the observation matrix.
#' @param contig_lab_size Text size for contigs.
#' @param cluster_contig A value directs cluster to only genes on this contig.
#' @param obs_annotations_groups Vector of observations annotations group assignation (as indices).
#' @param cluster_by_groups Whether to cluster observations by their annotations or not. Using this ignores num_obs_groups.
#' @param breaksList List of values used as splitters on coloring range.
#' @param gene_position_breaks List of gene break positions for scaled chromosome size plot
#' @param hclust_method Method to use for hclust.
#' @param testing If TRUE, does not plot anything.
#' @param layout_lmat lmat values to use in layout.
#' @param layout_lhei lhei values to use in layout.
#' @param layout_lwid lwid values to use in layout.
#
#' @return Void.
#'
#' @keywords internal
#' @noRd
#'
.plot_cnv_observations <- function(infercnv_obj,
obs_data,
col_pal,
contig_colors,
contig_labels,
contig_names,
contig_seps,
num_obs_groups,
file_base_name,
do_plot=TRUE,
output_filename_prefix,
cnv_title,
cnv_obs_title,
contig_lab_size=1,
contigs,
cluster_contig=NULL,
obs_annotations_groups,
obs_annotations_names,
grouping_key_coln,
cluster_by_groups,
breaksList,
gene_position_breaks,
x.center,
hclust_method="ward.D",
testing=FALSE,
layout_lmat=NULL,
layout_lhei=NULL,
layout_lwid=NULL,
useRaster=useRaster) {
flog.info("plot_cnv_observation:Start")
flog.info(paste("Observation data size: Cells=",
nrow(obs_data),
"Genes=",
ncol(obs_data),
sep=" "))
observation_file_base <- paste(file_base_name, sprintf("%s.observations.txt", output_filename_prefix), sep=.Platform$file.sep)
# Output dendrogram representation as Newick
# Need to precompute the dendrogram so we can manipulate
# it before the heatmap plot
## Optionally cluster by a specific contig
hcl_desc <- "General"
hcl_group_indices <- seq_len(ncol(obs_data))
if(!is.null(cluster_contig)){
# restricting to single contig
hcl_contig_indices <- which(contig_names %in% cluster_contig)
if(length(hcl_contig_indices) > 0 ) {
hcl_group_indices <- hcl_contig_indices
hcl_desc <- paste(cluster_contig, collapse="_")
flog.info(paste("plot_cnv_observation:Clustering only by contig ", cluster_contig))
infercnv_obj@tumor_subclusters = NULL # so that clustering is calculated based on selected contigs only without requiring the user to manually erase this field
} else {
flog.warn(paste("plot_cnv_observations: Not able to cluster by",
cluster_contig,
"Clustering by all genomic locations.",
"To cluster by local genomic location next time",
"select from:",
unique(contig_names),
collapse=",",
sep=" "))
}
}
obs_dendrogram <- list()
ordered_names <- NULL
isfirst <- TRUE
hcl_obs_annotations_groups <- vector()
obs_seps <- c()
sub_obs_seps <- c() # never use at this time? available if we want to add splits in the heatmap for subclusters
if (!is.null(infercnv_obj@tumor_subclusters)) {
if (cluster_by_groups) {
# for (i in seq(1, max(obs_annotations_groups))) {
for (i in seq_along(obs_annotations_names)) {
if (!is.null(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]])) {
obs_dendrogram[[i]] = as.dendrogram(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]])
ordered_names <- c(ordered_names, infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]$labels[infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]$order])
obs_seps <- c(obs_seps, length(ordered_names))
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, rep(i, length(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]$order)))
if (isfirst) {
write.tree(as.phylo(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
isfirst <- FALSE
}
else {
write.tree(as.phylo(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep), append=TRUE)
}
}
else { ## should only happen if there is only 1 cell in the group so the clustering method was not able to generate a hclust
#### actually happens with 2 cells only too, because can't cluster 2
if ((length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]])) == 2) || (length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]])) == 1)) {
if (length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]])) == 2) {
obs_dendrogram[[i]] <- .pairwise_dendrogram(colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]), drop=FALSE]))
}
else { ## == 1
obs_dendrogram[[i]] <- .single_element_dendrogram(colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]), drop=FALSE]))
}
ordered_names <- c(ordered_names, colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]), drop=FALSE]))
obs_seps <- c(obs_seps, length(ordered_names))
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, rep(i, length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]))))
}
else {
flog.error("Unexpected error, should not happen.")
stop("Error")
}
}
}
if (length(obs_dendrogram) > 1) {
obs_dendrogram <- do.call(merge, obs_dendrogram)
} else {
obs_dendrogram <- obs_dendrogram[[1]]
}
split_groups <- rep(1, dim(obs_data)[1])
names(split_groups) <- ordered_names
for(subtumor in infercnv_obj@tumor_subclusters$subclusters[[ obs_annotations_names[i] ]]) {
sub_obs_seps <- c(sub_obs_seps, (sub_obs_seps[length(sub_obs_seps)] + length(subtumor)))
}
}
else {
obs_hcl <- infercnv_obj@tumor_subclusters$hc[["all_observations"]]
write.tree(as.phylo(obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
obs_dendrogram <- as.dendrogram(obs_hcl)
ordered_names <- obs_hcl$labels[obs_hcl$order]
split_groups <- cutree(obs_hcl, k=num_obs_groups)
split_groups <- split_groups[ordered_names]
hcl_obs_annotations_groups <- obs_annotations_groups[ordered_names]
# Make a file of members of each group
flog.info("plot_cnv_observation:Writing observations by grouping.")
for (cut_group in unique(split_groups)){
group_memb <- names(split_groups)[which(split_groups == cut_group)]
# Write group to file
memb_file <- file(paste(file_base_name,
paste(hcl_desc,"HCL",cut_group,"members.txt",sep="_"),
sep=.Platform$file.sep))
write.table(obs_data[group_memb,], memb_file)
# Record seperation
ordered_memb <- which(ordered_names %in% group_memb)
if (is.null(obs_seps)) {
obs_seps <- c(length(ordered_memb))
}
else {
obs_seps <- c(obs_seps, (obs_seps[length(obs_seps)] + length(ordered_memb)))
}
}
obs_seps <- c(obs_seps, length(ordered_names))
sub_obs_seps = obs_seps
}
}
else if (cluster_by_groups) { # at this point this and the else below should only be an error fallback, or when trying to plot before the subclustering step
## Clustering separately by groups (ie. patients)
# HCL with a inversely weighted euclidean distance.
flog.info(paste("clustering observations via method: ", hclust_method, sep=""))
for (i in seq_len(max(obs_annotations_groups))) {
cell_indices_in_group <- which(obs_annotations_groups == i)
num_cells_in_group <- length(cell_indices_in_group)
flog.info(sprintf("Number of cells in group(%d) is %d", i, num_cells_in_group))
if (num_cells_in_group < 2) {
flog.info(sprintf("Skipping group: %d, since less than 2 entries", i))
ordered_names <- c(ordered_names, row.names(obs_data[which(obs_annotations_groups == i),, drop=FALSE]))
# when only 1 cell in the group, we need an artificial dendrogram for it
obs_dendrogram[[length(obs_dendrogram) + 1]] = .single_element_dendrogram(unique_label=row.names(obs_data[which(obs_annotations_groups == i),, drop=FALSE]))
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, i)
if (isfirst) {
write(row.names(obs_data[which(obs_annotations_groups == i), ]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
isfirst <- FALSE
}
else {
write(row.names(obs_data[which(obs_annotations_groups == i), ]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep), append=TRUE)
}
}
else {
data_to_cluster <- obs_data[cell_indices_in_group, hcl_group_indices, drop=FALSE]
flog.info(paste("group size being clustered: ", paste(dim(data_to_cluster), collapse=","), sep=" "))
group_obs_hcl <- hclust(dist(data_to_cluster), method=hclust_method)
ordered_names <- c(ordered_names, group_obs_hcl$labels[group_obs_hcl$order])
if (isfirst) {
write.tree(as.phylo(group_obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
isfirst <- FALSE
}
else {
write.tree(as.phylo(group_obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep), append=TRUE)
}
}
group_obs_dend <- as.dendrogram(group_obs_hcl)
obs_dendrogram[[length(obs_dendrogram) + 1]] <- group_obs_dend
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, rep(i, num_cells_in_group))
obs_seps <- c(obs_seps, length(ordered_names))
}
if (length(obs_dendrogram) > 1) {
# merge separate dendrograms into a single dendrogram
obs_dendrogram <- do.call(merge, obs_dendrogram)
} else {
obs_dendrogram <- obs_dendrogram[[1]]
}
split_groups <- rep(1, dim(obs_data)[1])
names(split_groups) <- ordered_names
sub_obs_seps = obs_seps
}
else {
# clustering all groups together
# HCL with a inversely weighted euclidean distance.
flog.info(paste("clustering observations via method: ", hclust_method, sep=""))
if (nrow(obs_data) > 1) {
obs_hcl <- hclust(dist(obs_data[, hcl_group_indices]), method=hclust_method)
write.tree(as.phylo(obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
obs_dendrogram <- as.dendrogram(obs_hcl)
ordered_names <- obs_hcl$labels[obs_hcl$order]
split_groups <- cutree(obs_hcl, k=num_obs_groups)
split_groups <- split_groups[ordered_names]
# Make a file of members of each group
flog.info("plot_cnv_observation:Writing observations by grouping.")
for (cut_group in unique(split_groups)){
group_memb <- names(split_groups)[which(split_groups == cut_group)]
# Write group to file
memb_file <- file(paste(file_base_name,
paste(hcl_desc,"HCL",cut_group,"members.txt",sep="_"),
sep=.Platform$file.sep))
write.table(obs_data[group_memb,], memb_file)
# Record seperation
ordered_memb <- which(ordered_names %in% group_memb)
if (is.null(obs_seps)) {
obs_seps <- c(length(ordered_memb))
}
else {
obs_seps <- c(obs_seps, (obs_seps[length(obs_seps)] + length(ordered_memb))) ## could have a tmp_seps that just lists them, then cumSum() on it
}
}
obs_seps <- c(obs_seps, length(ordered_names))
}
else {
obs_dendrogram <- .single_element_dendrogram(row.names(obs_data))
ordered_names <- row.names(obs_data)
obs_seps <- c(1)
split_groups <- c(1)
names(split_groups) <- ordered_names
}
# hcl_obs_annotations_groups <- obs_annotations_groups[obs_hcl$order]
sub_obs_seps = obs_seps
hcl_obs_annotations_groups <- obs_annotations_groups[ordered_names]
}
if (length(obs_seps) > 1) {
obs_seps <- obs_seps[length(obs_seps)] - obs_seps[(length(obs_seps) - 1):1]
}
# Output HCL group membership.
# Record locations of seperations
# Make colors based on groupings
row_groupings <- get_group_color_palette()(length(table(split_groups)))[split_groups]
row_groupings <- cbind(row_groupings, get_group_color_palette()(length(table(hcl_obs_annotations_groups)))[hcl_obs_annotations_groups])
annotations_legend <- cbind(obs_annotations_names, get_group_color_palette()(length(table(hcl_obs_annotations_groups))))
# Make a file of coloring and groupings
flog.info("plot_cnv_observation:Writing observation groupings/color.")
groups_file_name <- file.path(file_base_name, sprintf("%s.observation_groupings.txt", output_filename_prefix))
# file_groups <- rbind(split_groups,row_groupings)
file_groups <- cbind(split_groups, row_groupings[,1], hcl_obs_annotations_groups, row_groupings[,2])
#row.names(file_groups) <- c("Group","Color")
colnames(file_groups) <- c("Dendrogram Group", "Dendrogram Color", "Annotation Group", "Annotation Color")
# write.table(t(file_groups), groups_file_name)
write.table(file_groups, groups_file_name)
flog.info("plot_cnv_observation:Done writing observation groupings/color.")
# Generate the Sep list for heatmap.3
contigSepList <- create_sep_list(row_count=nrow(obs_data),
col_count=ncol(obs_data),
row_seps=obs_seps,
col_seps=contig_seps)
# reorder expression matrix based on dendrogram ordering
obs_data <- obs_data[ordered_names, ]
# Remove row/col labels, too cluttered
# and print.
orig_row_names <- row.names(obs_data)
row.names(obs_data) <- rep("", nrow(obs_data))
flog.info("plot_cnv_observation:Writing observation heatmap thresholds.")
heatmap_thresholds_file_name <- file.path(file_base_name, sprintf("%s.heatmap_thresholds.txt", output_filename_prefix))
write.table(breaksList, heatmap_thresholds_file_name, row.names=FALSE, col.names=FALSE)
flog.info("plot_cnv_observation:Done writing observation heatmap thresholds.")
if (do_plot) {
data_observations <- heatmap.cnv(obs_data,
Rowv=obs_dendrogram,
Colv=FALSE,
cluster.by.row=TRUE,
cluster.by.col=FALSE,
main=cnv_title,
ylab=cnv_obs_title,
margin.for.labCol=2,
xlab="Genomic Region",
key=TRUE,
labCol=contig_labels,
cexCol=contig_lab_size,
cexAt=c(1, contig_seps),
notecol="black",
density.info="histogram",
denscol="blue",
trace="none",
dendrogram="row",
cexRow=0.8,
breaks=breaksList,
gene_position_breaks=gene_position_breaks,
scale="none",
x.center=x.center,
color.FUN=col_pal,
if.plot=!testing,
# Seperate by contigs
sepList=contigSepList,
sep.color=c("black","black"),
sep.lty=1,
sep.lwd=1,
# Color rows by user defined cut
RowIndividualColors=row_groupings,
annotations_legend=annotations_legend,
grouping_key_coln=grouping_key_coln,
# Color by contigs
ColIndividualColors=contig_colors,
# Legend
key.title="Distribution of Expression",
key.xlab="Modified Expression",
key.ylab="Count",
# Layout
force_lmat=layout_lmat,
force_lwid=layout_lwid,
force_lhei=layout_lhei,
useRaster=useRaster)
}
# Write data to file.
if ("matrix" %in% is(obs_data)) {
flog.info(paste("plot_cnv_observations:Writing observation data to",
observation_file_base,
sep=" "))
row.names(obs_data) <- orig_row_names
if (do_plot) {
write.table(t(obs_data[data_observations$rowInd,data_observations$colInd]),
file=observation_file_base)
}
else {
# Rowv inherits dendrogram, Colv is FALSE
# rowInd = seq_len(nrow(ref_data)) == everything in normal order
# colInd = seq_len(ncol(ref_data)) == everything in normal order
write.table(t(obs_data), file=observation_file_base)
}
}
}
#' Not Testing, params ok.
#' Create the layout for the plot
#' This is a modification of the original
#' layout from the GMD heatmap.3 function
#'
#' Returns:
#' list with slots "lmat" (layout matrix),
#' "lhei" (height, numerix vector),
#' and "lwid" (widths, numeric vector)
#'
#' @keywords internal
#' @noRd
#'
.plot_observations_layout <- function(grouping_key_height, dynamic_extension)
{
## Plot observational samples
obs_lmat <- c(0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
# 8, 0, 10, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
8, 11, 10, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, # 9 = reference heatmap
0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, # 1 observations heatmap
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
obs_lmat <- matrix(obs_lmat,ncol=14,byrow=TRUE)
# vector of heights for each of the above rows.
obs_lhei <- c(1.125, 2.215, .15,
.5, .5, .5,
.5, .5, .5,
.5, .5, .5 + dynamic_extension,
0.1, grouping_key_height[1], grouping_key_height[2], 0.13)
return(list(lmat=obs_lmat,
lhei=obs_lhei,
lwid=NULL))
}
# TODO Tested, test make files so turned off but can turn on and should pass.
# Plot the reference samples
#
#' Args:
#' infercnv_obj: infercnv obj to access the subclusters information
#' ref_data Data to plot as references. Rows = Cells, Col = Genes
#' ref_groups Groups of references to plot together.
#' col_pal The color palette to use.
#' contig_seps Indices for line seperators of contigs.
#' file_base_name Base of the file to used to make output file names.
#' do_plot If FALSE, only write text files and does not run plotting.
#' cnv_ref_title Title for reference matrix.
#' layout_lmat lmat values to use in the layout.
#' layout_lwid lwid values to use in the layout.
#' layout_lhei lhei values to use in the layout.
#' layout_add Indicates the ref image shoudl be added to the previous plot.
#' testing: Turns off plotting when true.
#'
#' Returns:
#' Void
#'
#' @keywords internal
#' @noRd
#'
.plot_cnv_references <- function(infercnv_obj,
ref_data,
ref_groups,
name_ref_groups,
cluster_references,
hclust_method,
grouping_key_coln,
col_pal,
contig_seps,
file_base_name,
do_plot=TRUE,
output_filename_prefix,
cnv_ref_title,
breaksList,
gene_position_breaks,
x.center=x.center,
layout_lmat=NULL,
layout_lwid=NULL,
layout_lhei=NULL,
layout_add=FALSE,
testing=FALSE,
useRaster=useRaster){
flog.info("plot_cnv_references:Start")
flog.info(paste("Reference data size: Cells=",
ncol(ref_data),
"Genes=",
nrow(ref_data),
sep=" "))
number_references <- ncol(ref_data)
reference_ylab <- NA
reference_data_file <- paste(file_base_name, sprintf("%s.references.txt", output_filename_prefix), sep=.Platform$file.sep)
ref_seps <- c()
# Handle reference groups
# If there is more than one reference group, visually break
# up the groups with a row seperator. Also plot the rows in
# order so the current groups are shown and seperated.
ordered_names <- c()
if (all(name_ref_groups %in% infercnv_obj@tumor_subclusters$subclusters) || !is.null(infercnv_obj@tumor_subclusters$hc[["all_references"]])) {
if (cluster_references) {
split_groups <- c()
for (i in seq_along(name_ref_groups)) {
if (!is.null(infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]])) {
ordered_names <- c(ordered_names, infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]]$labels[infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]]$order])
ref_seps <- c(ref_seps, length(ordered_names))
split_groups <- c(split_groups, rep(i, length(infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]]$order)))
}
else { ## should only happen if there is only 1 cell in the group so the clustering method was not able to generate a hclust
#### actually happens with 2 cells only too, because can't cluster 2
if ((length(unlist(infercnv_obj@tumor_subclusters$subclusters[[name_ref_groups[i]]])) == 2) || (length(unlist(infercnv_obj@tumor_subclusters$subclusters[[name_ref_groups[i]]])) == 1)) {
ordered_names <- c(ordered_names, colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[ name_ref_groups[i] ]]), drop=FALSE]))
ref_seps <- c(ref_seps, length(ordered_names))
split_groups <- c(split_groups, rep(i, length(length(unlist(infercnv_obj@tumor_subclusters$subclusters[[name_ref_groups[i]]])))))
}
else {
flog.error("Unexpected error, should not happen.")
stop("Error")
}
}
}
}
else {
ordered_names <- infercnv_obj@tumor_subclusters$hc[["all_references"]]$labels[infercnv_obj@tumor_subclusters$hc[["all_references"]]$order]
split_groups <- rep(1, length(ordered_names))
}
ref_data <- ref_data[, ordered_names, drop=FALSE]
}
else {
if(length(ref_groups) > 1){
if (cluster_references) {
order_idx <- lapply(ref_groups, function(ref_grp) {
if (cluster_references && length(ref_grp) > 2) {
ref_hcl <- hclust(dist(t(ref_data[, ref_grp])), method=hclust_method)
ref_grp <- ref_grp[ref_hcl$order]
}
ref_grp
})
ref_seps <- head(cumsum(lengths(order_idx)), -1)
split_groups <- unlist(mapply(rep, seq_along(order_idx), lengths(order_idx)))
order_idx <- unlist(order_idx)
}
else {
ref_seps <- head(cumsum(lengths(ref_groups)), -1)
split_groups <- unlist(mapply(rep, seq_along(ref_groups), lengths(ref_groups)))
order_idx <- unlist(ref_groups)
}
}
else {
if (cluster_references) {
ref_hcl <- hclust(dist(t(ref_data)), method=hclust_method) # all ref_data is part of the only group
# order_idx <- unlist(ref_groups)[ref_hcl$order]
order_idx = ref_hcl$order # ref_data has been reindexed beforehand in the calling method
}
else {
order_idx = unlist(ref_groups)
}
split_groups <- rep(1, length(ref_groups[[1]]))
}
ref_data <- ref_data[, order_idx, drop=FALSE]
}
# Handle only one reference
# heatmap3 requires a 2 x 2 matrix, so with one reference
# I just duplicate the row and hid the second name so it
# visually looks like it is just taking up the full realestate.
if(number_references == 1){
ref_data <- cbind(ref_data, ref_data)
names(ref_data) <- c("",names(ref_data)[1])
colnames(ref_data) <- c("", colnames(ref_data)[1])
split_groups <- rep(1, 2)
}
# Make row color column colors from groupings
flog.info(paste("plot_cnv_references:Number reference groups=",
length(ref_groups)),
sep=" ")
# Transpose data.
ref_data <- t(ref_data)
# if (cluster_references) {
# if (number_references > 1) {
# for (i in seq_len(length(ref_groups))) {
# ref_hcl <- hclust(dist(ref_data[ref_groups[[i]], ]), method=hclust_method)
# ref_data[ref_groups[[i]], ] <- ref_data[ref_groups[[i]][ref_hcl$order], , drop=FALSE]
# }
# }
# }
# Remove labels if too many.
ref_orig_names <- row.names(ref_data)
# if (number_references > 20){
# The reference labs can become clustered
# Dynamically change labels given a certain number of labels.
# remove labels from plot even if less than 20 cells, because it changes the size of the heatmap
reference_ylab <- cnv_ref_title
if(number_references == 1) {
row.names(ref_data) = rep("", 2)
}
else {
row.names(ref_data) <- rep("", number_references)
}
# }
row_groupings <- as.matrix(get_group_color_palette()(length(table(split_groups)))[split_groups])
annotations_legend <- cbind(name_ref_groups, get_group_color_palette()(length(name_ref_groups)))
# Generate the Sep list for heatmap.3
contigSepList <- create_sep_list(row_count=nrow(ref_data),
col_count=ncol(ref_data),
row_seps=ref_seps,
col_seps=contig_seps)
# Print controls
flog.info("plot_cnv_references:Plotting heatmap.")
if (do_plot) {
data_references <- heatmap.cnv(ref_data,
main=NULL, #NA,
ylab=reference_ylab,
xlab=NULL, #NA,
key=FALSE,
labCol=rep("", nrow(ref_data)),
notecol="black",
trace="none",
dendrogram="none",
Colv=FALSE,
Rowv=FALSE,
cexRow=0.4,
breaks=breaksList,
gene_position_breaks=gene_position_breaks,
scale="none",
x.center=x.center,
color.FUN=col_pal,
sepList=contigSepList,
# Row colors and legend
RowIndividualColors=row_groupings,
annotations_legend=annotations_legend,
grouping_key_coln=grouping_key_coln,
# Seperate by contigs
sep.color=c("black","black"),
sep.lty=1,
sep.lwd=1,
if.plot=!testing,
# Layout
force_lmat=layout_lmat,
force_lwid=layout_lwid,
force_lhei=layout_lhei,
force_add=layout_add,
useRaster=useRaster)
}
# Write data to file
if ("matrix" %in% is(ref_data)) {
## TODO: write files for dgcMatrix too.
row.names(ref_data) <- ref_orig_names
flog.info(paste("plot_cnv_references:Writing reference data to",
reference_data_file,
sep=" "))
## Rowv is FALSE, Colv is FALSE
if (do_plot) {
write.table(t(ref_data[data_references$rowInd,data_references$colInd]),
file=reference_data_file)
}
else {
# colInd = seq_len(ncol(ref_data)) == everything in normal order
write.table(t(ref_data[rev(seq_len(nrow(ref_data))), ]),
file=reference_data_file)
}
}
}
#########################################
# Plotting from GMD
###################
###
# This package relies on heatmap.3 from the library GMD
# This code is a modified version of heatmap.3, some changes
# were required for our plotting.
# The heatmap.cnv function should be considered a modification
# of th GMD library function heatmap.3, all credit goes to
# their authors.
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## A copy of gtools::invalid
##
## see \code{invalid} in package:gtools for details
## Test if a value is missing, empty, or contains only NA or NULL values
## param: x value to be tested
.invalid <-
function(x)
{
if (missing(x) || is.null(x) || length(x) == 0)
return(TRUE)
if (is.list(x))
return(all(sapply(x, .invalid)))
else if (is.vector(x))
return(all(is.na(x)))
else return(FALSE)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
.is.grouped <-
function(x)
{
x <- as.character(x)
x.f <- factor(x, levels=unique(x), ordered=TRUE)
identical(as.character(sort(x.f)), x)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Call a function with arguments
##
## Call a function with arguments
## FUN function or function name
## ... unnameed function arguments
## MoreArgs named (or unnameed) function arguments
.call.FUN <-
function(FUN,...,MoreArgs)
{
FUN <- match.fun(FUN)
tmp.MoreArgs <- list(...)
if (!.invalid(MoreArgs)){
if (length(MoreArgs)>=1) {
for (i in seq_along(MoreArgs)) tmp.MoreArgs[[names(MoreArgs)[i]]] <- MoreArgs[[i]]
}
}
ret <- do.call(FUN, tmp.MoreArgs)
if ("call" %in% names(ret)){
ret$call <- match.call()
}
if ("call" %in% names(attributes(ret))){
attr(ret,"call") <- match.call()
}
return(ret)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Scale values to make them follow Standard Normal Distribution
##
## Scale values to make them follow Standard Normal Distribution
## param x numeric
## param scale character, indicating the type to scale.
## param na.rm logical
## return an object with the same dimention of `x'.
.scale.data <-
function(x,scale,na.rm=TRUE)
{
if(scale=="row"){
x <- sweep(x,1,rowMeans(x,na.rm=na.rm),FUN="-")
sx <- apply(x,1,sd,na.rm=na.rm)
x <- sweep(x,1,sx,FUN="/")
} else if(scale=="column"){
x <- sweep(x,2,colMeans(x,na.rm=na.rm),FUN="-")
sx <- apply(x,2,sd,na.rm=na.rm)
x <- sweep(x,2,sx,,FUN="/")
}
x
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's author's.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Scale values to a new range: c(low, high)
## x numeric
## low numeric, lower bound of target values
## high numeric, higher bound of target values
## return an object with the same dimention of `x'.
.scale.x <-
function(x,low=0,high=1,na.rm=TRUE)
{
if(identical(max(x,na.rm=na.rm),min(x,na.rm=na.rm))) NA
a <- 1/(max(x)-min(x))
b <- -min(x)/(max(x)-min(x))
a*x+b
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's author's.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Plot text
##
## Plot text
## x character, text to plot
## cex
## forecolor color of foreground
## bg color of background
## bordercolor color of border
## axes as in \code{graphics:::plot}
## ... additional arguments for \code{graphics:::text}
.plot.text <- function(x,xlim=c(0,1),ylim=c(0,1),cex=1,forecolor=par("fg"),bg=par("bg"),bordercolor=NA,axes=FALSE,...) {
if (.invalid(x)){
x <- NULL
}
if (is.null(x)){
x <- ""
} else if (is.na(x)){
x <- 'NA'
}
plot(xlim,ylim,type="n",ylab="",xlab="",xaxt="n",yaxt="n",axes=axes)
rect(xleft=0, ybottom=0, xright=1, ytop=1, col=bg, border=bordercolor)
text(0.5,0.5,x,cex=cex,...)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's author's.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## This was originally heatmap.3.
heatmap.cnv <-
function(x,
## whether a dissimilarity matrix
diss=inherits(x,"dist"),
## dendrogram control
Rowv=TRUE,
Colv=TRUE,
dendrogram=c("both","row","column","none"),
## dist object
dist.row,
dist.col,
dist.FUN=gdist,
dist.FUN.MoreArgs=list(method="euclidean"),
## hclust object
hclust.row,
hclust.col,
hclust.FUN=hclust,
hclust.FUN.MoreArgs=list(method="ward.D"),
## data scaling
scale=c("none","row","column"),
na.rm=TRUE,
## clustering control
cluster.by.row=FALSE,
cluster.by.col=FALSE,
kr=NA,
kc=NA,
row.clusters=NA,
col.clusters=NA,
## image plot
revR=FALSE,
revC=FALSE,
add.expr,
## mapping data to colors
breaks,
gene_position_breaks=NULL,
## centering colors to a value
x.center,
## colors
color.FUN=gplots::bluered,
##
## block sepration
sepList=list(NULL,NULL),
sep.color=c("gray45","gray45"),
sep.lty=1,
sep.lwd=2,
## cell labeling
cellnote,
cex.note=1.0,
notecol="cyan",
na.color=par("bg"),
## level trace
trace=c("none","column","row","both"),
tracecol="cyan",
hline,
vline,
linecol=tracecol,
## Row/Column Labeling
labRow=TRUE, ## shown by default
labCol=TRUE, ## shown by default
srtRow=NULL,
srtCol=NULL,
sideRow=4,
sideCol=1,
##
margin.for.labRow,
margin.for.labCol,
ColIndividualColors,
RowIndividualColors,
annotations_legend,
grouping_key_coln,
cexRow,
cexCol,
cexAt=NULL,
labRow.by.group=FALSE,
labCol.by.group=FALSE,
## plot color key + density info
key=TRUE,
key.title="Color Key",
key.xlab="Value",
key.ylab="Count",
keysize=1.5,
mapsize=9,
mapratio=4/3,
sidesize=3,
cex.key.main=0.75,
cex.key.xlab=0.75,
cex.key.ylab=0.75,
density.info=c("histogram","density","none"),
denscol=tracecol,
densadj=0.25,
## plot titles/labels
main="Heatmap",
sub="",
xlab="",
ylab="",
cex.main=2,
cex.sub=1.5,
font.main=2,
font.sub=3,
adj.main=0.5,
mgp.main=c(1.5,0.5,0),
mar.main=3,
mar.sub=3,
## plot ##
if.plot=TRUE,
## plot of partition (left/top of heatmap)
plot.row.partition=FALSE,
plot.col.partition=FALSE,
cex.partition=1.25,
color.partition.box="gray45",
color.partition.border="#FFFFFF",
## plot of summary (right/bottom of heatmap)
plot.row.individuals=FALSE,
plot.col.individuals=FALSE,
plot.row.clusters=FALSE,
plot.col.clusters=FALSE,
plot.row.clustering=FALSE,
plot.col.clustering=FALSE,
##
plot.row.individuals.list=FALSE,
plot.col.individuals.list=FALSE,
plot.row.clusters.list=FALSE,
plot.col.clusters.list=FALSE,
plot.row.clustering.list=FALSE,
plot.col.clustering.list=FALSE,
## for plot of clusters - row
row.data=FALSE,
## for plot of clusters - col
col.data=FALSE,
##
if.plot.info=FALSE,
text.box,
cex.text=1.0,
## Force in layout info
force_lmat=NULL,
force_lwid=NULL,
force_lhei=NULL,
force_add=FALSE,
useRaster=TRUE,
## extras
...
)
{
## check input - take1 ##
if (! is.matrix(x)) {
x <- as.matrix(x)
}
x.ori <- x
if(!inherits(x,"dist") & !is.matrix(x)) {
stop("`x' should either be a matrix, a data.frame or a `dist' object.")
}
if (! sideRow %in% c(2,4)) {
stop('sideRow must be either 2 or 4.')
}
if (! sideCol %in% c(1,3)) {
stop('sideCol must be either 1 or 3.')
}
## store input
Rowv.ori <- Rowv
Colv.ori <- Colv
## check
dendrogram <- match.arg(dendrogram)
if ( (dendrogram %in% c("both","row")) & !inherits(Rowv,"dendrogram") ) {
warning("Discrepancy: row dendrogram is asked; Rowv is set to `TRUE'.")
Rowv <- TRUE
}
if ( (dendrogram %in% c("both","col")) & !inherits(Colv,"dendrogram") ) {
warning("Discrepancy: col dendrogram is asked; Colv is set to `TRUE'.")
Colv <- TRUE
}
if (identical(Rowv, FALSE) | missing(Rowv)) {
if(!identical(cluster.by.row,FALSE)) {
warning("Discrepancy: No row dendrogram is asked; cluster.by.row is set to `FALSE'.")
cluster.by.row <- FALSE
}
} else {
if(!identical(cluster.by.row,TRUE)) {
warning("Discrepancy: row dendrogram is asked; cluster.by.row is set to `TRUE'.")
cluster.by.row <- TRUE
}
}
if (identical(Colv, FALSE) | .invalid(Colv)) {
if(!identical(cluster.by.col,FALSE)) {
warning("Discrepancy: No col dendrogram is asked; cluster.by.col is set to `FALSE'.")
cluster.by.col <- FALSE
}
} else {
if(!identical(cluster.by.col,TRUE)) {
warning("Discrepancy: col dendrogram is asked; cluster.by.col is set to `TRUE'.")
cluster.by.col <- TRUE
}
}
if (!.invalid(kr)) {
if (is.numeric(kr)) {
if(!plot.row.partition) {
warning("Discrepancy: kr is set, therefore plot.row.partition is set to `TRUE'.")
plot.row.partition <- TRUE
}
}
}
if (!.invalid(kc)) {
if (is.numeric(kc)) {
if(!plot.col.partition) {
warning("Discrepancy: kc is set, therefore plot.col.partition is set to `TRUE'.")
plot.col.partition <- TRUE
}
}
}
symm <- isSymmetric(x)
## check input - take2: di ##
di <- dim(x)
if(length(di)!=2 || !is.numeric(x)){
stop("`x' should only contain `numeric' values and can be converted to a 2-D matrix.")
}
## parse param ##
scale <- if(symm && .invalid(scale)) "none" else match.arg(scale) ## no scale on symmetric matrix
trace <- match.arg(trace)
density.info <- match.arg(density.info)
dist.FUN <- match.fun(dist.FUN)
hclust.FUN <- match.fun(hclust.FUN)
color.FUN <- match.fun(color.FUN)
## NG if both breaks and scale are specified ##
if(!.invalid(breaks) & (scale!="none")) {
warning("Using scale=\"row\" or scale=\"column\" when breaks are",
"specified can produce unpredictable results.",
"Please consider using only one or the other.")
}
## nr and nc ##
nr <- di[1]
nc <- di[2]
## check input - take3: nr,nc ##
if(nr <=1 || nc <=1)
stop("`x' must have at least 2 rows and 2 columns")
## font size of row/col labels ##
cexRow0 <- 0.2+1/log10(nr)
cexCol0 <- 0.2+1/log10(nc)
if (.invalid(cexRow)) {
cexRow <- cexRow0
} else {
# message('heatmap.3 | From GMD 0.3.3, please use relative values for cexRow.')
cexRow <- cexRow0*cexRow
}
if (.invalid(cexCol)) {
cexCol <- cexCol0
} else {
# message('heatmap.3 | From GMD 0.3.3, please use relative values for cexCol.')
cexCol <- cexCol0*cexCol
}
## cellnote ##
## ##if(.invalid(cellnote)) cellnote <- matrix("",ncol=ncol(x),nrow=nrow(x))
## ------------------------------------------------------------------------
## parse dendrogram ##
## ------------------------------------------------------------------------
if (missing(Rowv)) Rowv <- FALSE
if (.invalid(Colv)) Colv <- if(symm) Rowv else FALSE
if (Colv=="Rowv") {
if ((!isTRUE(Rowv) | !symm) ){
Colv <- FALSE
warning("`Colv' is specified to use \"Rowv\", but either `Rowv' is invalid or `x' is not symmetric; Colv is suppressed.")
} else{
Colv <- Rowv
}
}
## ------------------------------------------------------------------------
## generate hclust.obj - row/col
## ------------------------------------------------------------------------
flush.console()
if ( !inherits(Colv,"dendrogram") & !identical(Colv,FALSE) | (cluster.by.col & .invalid(col.clusters))) {
if (.invalid(hclust.col)){
hclust.col <- .call.FUN(hclust.FUN,dist.col,MoreArgs=hclust.FUN.MoreArgs)
} else {
if (length(hclust.col$order) != nc) {
stop("`hclust.col' should have equal size as the cols.")
}
}
} else {
hclust.col <- NULL
}
## ------------------------------------------------------------------------
## generate hclust.obj - row/col
## ------------------------------------------------------------------------
ddr <- ddc <- NULL
## get the dendrograms and reordering row/column indices - row ##
if(inherits(Rowv,"dendrogram")) {
if (attr(Rowv,"members") != nr) {
stop("`Rowv' should contain equal size of members as the rows.")
}
ddr <- Rowv ## use Rowv 'as-is',when it is dendrogram
# rowInd <- order.dendrogram(ddr)
rowInd <- seq_len(nr) ### data has already been sorted in the proper order, and coloring vectors too
} else{
rowInd <- nr:1 ## from bottom.
}
## get the dendrograms and reordering row/column indices - col ##
if(inherits(Colv,"dendrogram")) {
if (attr(Colv,"members") != nc) {
stop("`Colv' should contain equal size of members as the cols.")
}
ddc <- Colv ## use Colv 'as-is',when it is dendrogram
colInd <- order.dendrogram(ddc)
} else if(identical(Colv, "Rowv")) {
if(exists("ddr")){
ddc <- ddr
colInd <- order.dendrogram(ddc)
} else{
colInd <- rowInd
}
} else if(is.integer(Colv)) { ## compute dendrogram and do reordering based on given vector
ddc <- as.dendrogram(hclust.col)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if(nc != length(colInd)) {
stop("`colInd' is of wrong length.")
}
} else if (isTRUE(Colv)) { ## if TRUE,compute dendrogram and do reordering based on rowMeans
Colv <- colMeans(x, na.rm=TRUE)
ddc <- as.dendrogram(hclust.col)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if(nc !=length(colInd))
stop("`colInd' is of wrong length.")
} else {
colInd <- seq_len(nc) ## from left
}
## ------------------------------------------------------------------------
## check consistency
## ------------------------------------------------------------------------
## Xmisc::logme(dendrogram)
## Xmisc::logme(Colv)
## Xmisc::logme(Rowv)
## dendrogram - check consistency: Rowv ##
if ( is.null(ddr) & (dendrogram %in% c("both","row"))){
warning("Discrepancy: Rowv is invalid or FALSE, while dendrogram is `",
dendrogram,"'. Omitting row dendogram.")
if (is.logical(Colv) & (Colv.ori) & dendrogram=="both") {
dendrogram <- "column"
}
else {
dendrogram <- "none"
}
}
## dendrogram - check consistency: Colv ##
if ( is.null(ddc) & (dendrogram %in% c("both","column")) ) {
warning("Discrepancy: Colv is invalid or FALSE, while dendrogram is `",
dendrogram,"'. Omitting column dendogram.")
if (is.logical(Rowv) & (identical(Rowv.ori,TRUE) | is.numeric(Rowv.ori) | inherits(Rowv.ori,"dendrogram")) & dendrogram=="both") {
dendrogram <- "row"
}
else {
dendrogram <- "none"
}
}
## check consistency
if (is.null(ddr)){
if(isTRUE(cluster.by.row) | isTRUE(plot.row.partition) | isTRUE(plot.row.clusters) | isTRUE(plot.row.clustering) ){
warning("Using invalid `Rowv' while allowing",
"`cluster.by.row' or `plot.row.partition' or `plot.row.clusters' or `plot.row.clustering'",
"can produce unpredictable results; Forced to be disabled.")
}
}
if (is.null(ddc)){
if(isTRUE(cluster.by.col) | isTRUE(plot.col.partition) | isTRUE(plot.col.clusters) | isTRUE(plot.col.clustering) ){
warning("Using invalid `Colv' while allowing",
"`cluster.by.col' or `plot.col.partition' or `plot.col.clusters' or `plot.col.clustering'",
"can produce unpredictable results; Forced to be disabled.")
}
}
if (is.null(ddr)) cluster.by.row <- plot.row.partition <- plot.row.clusters <- plot.row.clustering <- FALSE
if (is.null(ddc)) cluster.by.col <- plot.col.partition <- plot.col.clusters <- plot.col.clustering <- FALSE
## ------------------------------------------------------------------------
## Reordering
## ------------------------------------------------------------------------
flush.console()
## reorder x and cellnote ##
# rowInd and colInd should both be seq_len
x <- x[rowInd,colInd,drop=FALSE]
if (!.invalid(cellnote)) {
cellnote <- cellnote[rowInd,colInd,drop=FALSE ]
}
## reorder labels - row ##
if(identical(labRow,TRUE)) { ## Note: x is already reorderred
labRow <- if (is.null(rownames(x))) (seq_len(nr))[rowInd] else rownames(x)
} else if(identical(labRow,FALSE) | .invalid(labRow)) {
labRow <- rep("",nrow(x))
} else if(is.character(labRow)) {
labRow <- labRow[rowInd]
}
## reorder cellnote/labels - col ##
if (identical(labCol,TRUE)) {
labCol <- if(is.null(colnames(x))) (seq_len(nc))[colInd] else colnames(x)
} else if(identical(labCol,FALSE) | .invalid(labCol)) {
labCol <- rep("",ncol(x))
} #else if(is.character(labCol)){
#labCol <- labCol[colInd]
#}
## ------------------------------------------------------------------------
## scale
## center to 0 and scale to 1 in row or col but not both! ##
## ------------------------------------------------------------------------
flush.console()
x <- .scale.data(x,scale,na.rm)
## ------------------------------------------------------------------------
## labels for observations/clusters/
## ------------------------------------------------------------------------
## margin for labels
margin.for.labRow0 <- max(nchar(labRow, keepNA = FALSE)) * 0.75 + 0.2
margin.for.labCol0 <- max(nchar(labCol, keepNA = FALSE)) * 0.75 + 0.2 ## Change compared to GMD to work with R 3.3+ that changed the default value of keepNA to "NA"
if (.invalid(margin.for.labRow)) {
margin.for.labRow <- margin.for.labRow0
}
#} else {
# message('heatmap.3 | From GMD 0.3.3, please use relative values for margin.for.labRow.')
# margin.for.labRow <- margin.for.labRow0*margin.for.labRow
#}
if (margin.for.labRow < 2) {
margin.for.labRow <- 2
}
# if (.invalid(margin.for.labCol)){
margin.for.labCol <- margin.for.labCol0
# }
# else {
# # message('heatmap.3 | From GMD 0.3.3, please use relative values for margin.for.labCol.')
# margin.for.labCol <- margin.for.labCol0*margin.for.labCol
# }
if (margin.for.labCol < 2) {
margin.for.labCol <- 2
}
## group unique labels - row ## ##??check
if (!.invalid(labRow.by.group) & !identical(labRow.by.group,FALSE)) {
group.value <- unique(labRow)
group.index <- sapply(group.value, function(x,y) min(which(y==x)), y=labRow)
labRow <- rep("",length(labRow))
labRow[group.index] <- group.value
}
## group unique labels - col ## ##??check
if (!.invalid(labCol.by.group) & !identical(labCol.by.group,FALSE)) {
group.value <- unique(labCol)
group.index <- sapply(group.value, function(x,y) min(which(y==x)), y=labCol)
labCol <- rep("",length(labCol))
labCol[group.index] <- group.value
}
## ------------------------------------------------------------------------
## color breaks
## ------------------------------------------------------------------------
flush.console()
## set breaks for binning x into colors ##
if(.invalid(breaks)) {
breaks <- 16
} else {
flog.debug(paste("inferCNV::heatmap.cnv, breaks parameter set to: [", paste(breaks, collapse=","), "]", sep=""))
}
## get x.range according to the value of x.center ##
if (!.invalid(x.center)) { ## enhanced
if (is.numeric(x.center)) {
x.range.old <- range(x,na.rm=TRUE)
if (length(breaks) > 1) {
# important, use specified breakpoint info here if set by user
x.range.old = range(breaks)
}
dist.to.x.center <- max(abs(x.range.old-x.center))
x.range <- c(x.center-dist.to.x.center,x.center+dist.to.x.center)
if (length(breaks) > 1) {
# re-set the breaks according to the new x.range
breaks=seq(x.range[1], x.range[2], length=16)
flog.debug(paste("inferCNV::heatmap.cnv, resetting breaks to adjusted x.range: [",
paste(breaks, collapse=","), "]", sep=""))
}
} else {
stop("`x.center' should be numeric.")
}
} else {
x.range <- range(x,na.rm=TRUE)
if (length(breaks) > 1) {
# important, use specified breakpoint info here if set by user
x.range = range(breaks)
}
}
flog.debug( paste("inferCNV::heatmap.cnv x range set to: ",
paste(x.range, collapse=",")), sep="" )
## set breaks for centering colors to the value of x.center ##
if(length(breaks)==1) {
breaks <-
seq(min(min(x,na.rm=TRUE), x.range[1]),
max(max(x,na.rm=TRUE), x.range[2]),
length.out=breaks)
}
## count of breaks and colors ##
nbr <- length(breaks)
ncolor <- length(breaks) - 1
## generate colors ##
colors <- color.FUN(ncolor)
## set up breaks and force values outside the range into the endmost bins ##
min.breaks <- min(breaks)
max.breaks <- max(breaks)
x[] <- ifelse(x<min.breaks, min.breaks, x)
x[] <- ifelse(x>max.breaks, max.breaks, x)
## ------------------------------------------------------------------------
## Plotting
## ------------------------------------------------------------------------
if (if.plot){
ir <- length(plot.row.individuals.list)
ic <- length(plot.col.individuals.list)
cr <- length(plot.row.clustering.list)
cc <- length(plot.col.clustering.list)
flush.console()
if(mapratio<=1){
sr <- 12
sc <- sr*mapratio
} else {
sc <- 12
sr <- sc/mapratio
}
###############################
## calculate the plot layout ##
###############################
## 1) for heatmap
lmat <- matrix(1, nrow=sr, ncol=sc)
lwid <- c(rep(mapsize/sc, sc))
lhei <- c(rep(mapsize/mapratio/sr, sr))
## 2) row.clusters
if (plot.row.partition | plot.row.clusters){
lmat <- cbind(max(lmat, na.rm=TRUE)+1, lmat)
lwid <- c(0.3, lwid)
} else {
lmat <- cbind(NA, lmat)
lwid <- c(0.02, lwid)
}
## 3) col.clusters
if (plot.col.partition | plot.col.clusters){
lmat <- rbind(c(NA, rep(max(lmat, na.rm=TRUE)+1,sc)), lmat)
lhei <- c(0.3/mapratio, lhei)
} else {
lmat <- rbind(NA, lmat)
lhei <- c(0.02/mapratio, lhei)
}
if(!.invalid(RowIndividualColors)) { ## 4) add middle column to layout for vertical sidebar ##??check
# if(!is.character(RowIndividualColors) || length(RowIndividualColors) !=nr)
if(!is.character(RowIndividualColors) || dim(RowIndividualColors)[1] !=nr) {
stop("'RowIndividualColors' must be a character vector of length nrow(x)")
}
lmat <- cbind(c(rep(NA,nrow(lmat)-sr),rep(max(lmat,na.rm=TRUE)+1,sr)), lmat)
lwid <- c(0.2, lwid)
lmat <- cbind(c(rep(NA,nrow(lmat)-sr),rep(max(lmat,na.rm=TRUE)+1,sr)), lmat) # not really useful as this if is only true in plot_cnv_observations which forces layout_lmat and layout_lhei
lwid <- c(0.2, lwid) # layout_lwid is however not forced, so need to actually update it here
} else {
lmat <- cbind(NA, lmat)
lwid <- c(0.02, lwid)
}
if(!.invalid(ColIndividualColors)) { ## 5) add middle row to layout for horizontal sidebar ##??check
if(!is.character(ColIndividualColors) || length(ColIndividualColors) !=nc) {
stop("'ColIndividualColors' must be a character vector of length ncol(x)")
}
lmat <- rbind(c(rep(NA,ncol(lmat)-sc), rep(max(lmat,na.rm=TRUE)+1,sc)), lmat)
lhei <- c(0.2/mapratio, lhei)
} else {
lmat <- rbind(NA, lmat)
lhei <- c(0.02/mapratio, lhei)
}
## 6) for row-dend
lmat <- cbind(c(rep(NA, nrow(lmat)-sr),
rep(max(lmat, na.rm=TRUE)+1, sr)),
lmat
)
lwid <- c(keysize,lwid)
## 7) for col-dend, 8) for key
lmat <- rbind(c(
max(lmat, na.rm=TRUE)+2,
rep(NA, ncol(lmat)-sc-1),
rep(max(lmat, na.rm=TRUE)+1, sc)
),
lmat
)
lhei <- c(keysize/mapratio,lhei)
## text.box##
## numbered 999 ##
## 9) for RowPlot (from bottom)
if(.invalid(text.box)){
text.box <- "made by\nFunction: heatmap.3\nPackage: GMD\nin R"
}
if(plot.row.individuals) { ## enhanced: add right column to layout for plots
lmat <- cbind(lmat,
c(rep((1+max(lmat, na.rm=TRUE)), nrow(lmat)-sr),# text
rep((ir:1)+max(lmat, na.rm=TRUE)+(1), each=floor(sr/ir)), rep(NA,sr%%ir)
)
)
lwid <- c(lwid,sidesize)
} else {
lmat <- cbind(lmat,c(rep(NA,nrow(lmat))))
lwid <- c(lwid,0.01)
}
## 10) for ColPlot from right
if(plot.col.individuals) { ## enhanced: add bottom row to layout for plots
lmat <- rbind(lmat,
c(rep((1+max(lmat, na.rm=TRUE)), ncol(lmat)-sc-1),# text
rep((seq_len(ic))+max(lmat, na.rm=TRUE)+(1), each=floor(sc/ic)), rep(NA,sc%%ic),
##NA # change to numeric if text.box
999
)
)
lhei <- c(lhei,sidesize/mapratio)
} else {
lmat <- rbind(lmat,c(rep(NA,ncol(lmat))))
lhei <- c(lhei,0.01/mapratio)
}
## 11) for RowPlot (from bottom)
if(plot.row.clusters) { ## enhanced: add right column to layout for plots
lmat <- cbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)), nrow(lmat)-sr-1), # text
rep((kr:1)+max(lmat[lmat!=999], na.rm=TRUE)+(1), each=floor(sr/kr)), rep(NA,sr%%kr),
##NA
999
)
)
lwid <- c(lwid,sidesize)
} else {
lmat <- cbind(lmat,c(rep(NA,nrow(lmat))))
lwid <- c(lwid,0.01)
}
## 12) for ColPlot from right
if(plot.col.clusters) { ## enhanced: add bottom row to layout for plots
lmat <- rbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)), ncol(lmat)-sc-2),# text
rep((seq_len(kc))+max(lmat[lmat!=999], na.rm=TRUE)+(1), each=floor(sc/kc)), rep(NA,sc%%kc),
##NA,NA # change to numeric if text.box
999,999
)
)
lhei <- c(lhei,sidesize/mapratio)
} else {
lmat <- rbind(lmat, c(rep(NA, ncol(lmat))))
lhei <- c(lhei,0.01/mapratio)
}
## 13) for RowPlot (from bottom)
if(plot.row.clustering) { ## enhanced: add right column to layout for plots
lmat <- cbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)), nrow(lmat)-sr-2), # text
rep(c((cr:1)+max(lmat[lmat!=999], na.rm=TRUE)+(1)), each=floor(sr/cr)), rep(NA,sr%%cr),
##NA,NA
999, 999
)
)
lwid <- c(lwid,sidesize)
} else {
lmat <- cbind(lmat,c(rep(NA,nrow(lmat))))
lwid <- c(lwid,0.01)
}
## 14) for ColPlot from right
if(plot.col.clustering) { ## enhanced: add bottom row to layout for plots
lmat <- rbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)),ncol(lmat)-sc-3),# text
rep((seq_len(cc))+max(lmat[lmat!=999], na.rm=TRUE)+(1), each=floor(sc/cc)), rep(NA,sc%%cc),
##NA,NA,NA # change to numeric if text.box
999, 999, 999
)
)
lhei <- c(lhei,sidesize/mapratio)
} else {
lmat <- rbind(lmat,c(rep(NA,ncol(lmat))))
lhei <- c(lhei,0.01/mapratio)
}
lmat[is.na(lmat)] <- 0
if (any(lmat==999)) {
flag.text <- TRUE
}
else {
flag.text <- FALSE
}
lmat[lmat==999] <- max(lmat[lmat!=999]) + 1
##------------------------------- Overriding the lmat settings if already given.
## Graphics `output' ##
## layout
if(!is.null(force_lmat)) {
lmat <- force_lmat
}
if(!is.null(force_lwid)) {
lwid <- force_lwid
}
if(!is.null(force_lhei)) {
lhei <- force_lhei
}
if(!force_add) {
layout(lmat, widths=lwid, heights=lhei, respect=FALSE)
}
##---------------------------------
## reverse columns
if(revC) { ## x columns reversed
iy <- nr:1
ddc <- rev(ddc)
x <- x[iy,]
if (!.invalid(cellnote)) {
cellnote <- cellnote[iy,]
}
} else {
iy <- seq_len(nr)
}
## reverse rows
if(revR){ ## x columns reversed
ix <- nc:1
ddr <- rev(ddr)
x <- x[,ix]
if (!.invalid(cellnote)) {
cellnote <- cellnote[,ix]
}
} else {
ix <- seq_len(nc)
}
## 1) draw the main carpet/heatmap
margins <- c(margin.for.labCol, 0, 0, margin.for.labRow)
mgp <- c(3, 1, 0)
par(mar=margins,mgp=mgp);outer=FALSE
x.save <- x
if(!symm || scale !="none") { ##??
x <- t(x)
if (!.invalid(cellnote)) {
cellnote <- t(cellnote)
}
}
if (!is.null(gene_position_breaks)) {
image(gene_position_breaks, seq_len(nr+1),
x,
xlim=0.5+c(0,max(gene_position_breaks)), ylim=0.5+c(0,nr),
axes=FALSE, xlab="", ylab="", col=colors, breaks=breaks, useRaster=FALSE,
...)
} else {
image(seq_len(nc), seq_len(nr),
x,
xlim=0.5+c(0,nc), ylim=0.5+c(0,nr),
axes=FALSE, xlab="", ylab="", col=colors, breaks=breaks, useRaster=useRaster,
...)
}
flog.info(paste("Colors for breaks: ", paste(colors, collapse=","), sep=" "))
flog.info(paste("Quantiles of plotted data range:", paste(quantile(x), collapse=","), sep=" "))
## plot/color NAs
if(!.invalid(na.color) & any(is.na(x))) {
mmat <- ifelse(is.na(x),1,NA)
if (!is.null(gene_position_breaks)) {
image(gene_position_breaks, seq_len(nr+1), mmat, axes=FALSE, xlab="", ylab="",
col=na.color, add=TRUE, useRaster=FALSE)
} else {
image(seq_len(nc),seq_len(nr), mmat, axes=FALSE, xlab="", ylab="",
col=na.color, add=TRUE, useRaster=useRaster)
}
}
##
## labCol (?)
if ((dendrogram %in% c("both","col")) & sideCol==3) {
warning("Discrepancy: col dendrogram is asked; srtCol is set to 1.")
sideCol <- 1
}
if (!length(srtCol)) {
if (is.null(cexAt)) {
cexAt = seq_along(labCol)
}
axis(sideCol,cexAt,labels=labCol,las=2,line=-0.5,tick=0,cex.axis=cexCol,outer=outer)
} else {
if (sideCol==1){
if (sideCol==1) .sideCol <- par("usr")[3]-0.5*srtCol/90 else .sideCol <- par("usr")[4]+0.5*srtCol/90
text(seq_len(nc),.sideCol,labels=labCol,srt=srtCol,pos=1,xpd=TRUE,cex=cexCol)
}
}
if(!.invalid(xlab)) {
mtext(xlab,side=1, line=margins[1]-1.25)
}
## labRow (?)
if ((dendrogram %in% c("both","row")) & sideRow==2) {
warning("Discrepancy: row dendrogram is asked; sideRow is set to 4.")
sideRow <- 4
}
if (!length(srtRow)) {
axis(sideRow,iy,labels=labRow,las=2,line=-0.5,tick=0,cex.axis=cexRow,outer=outer)
} else {
if (sideRow==4) {
if (sideRow==4) .sideRow <- par("usr")[2]+0.5*srtRow/90 else .sideRow <- par("usr")[1]-0.5*srtRow/90
text(.sideRow,iy, labels=labRow, srt=srtRow, pos=1, xpd=TRUE, cex=cexRow)
}
}
if(!.invalid(ylab)) mtext(ylab,side=4,line=margins[4]-1.25)
if (!.invalid(add.expr))
eval(substitute(add.expr))
## Enhanced: add 'sep.color' colored spaces to visually separate sections
if (plot.row.partition | plot.row.clusters) { ##??
plot.row.partitionList <- get.sep(clusters=row.clusters,type="row")
} else {
plot.row.partitionList <- NULL
}
if (plot.col.partition | plot.col.clusters) { ##??
plot.col.partitionList <- get.sep(clusters=col.clusters,type="column")
} else {
plot.col.partitionList <- NULL
}
row.sepList <- sepList[[1]]
if (!.invalid(row.sepList)) {
for (i in seq_along(row.sepList)) {
i.sep <- row.sepList[[i]]
rect(
xleft=i.sep[1]+0.5,
ybottom=i.sep[2]+0.5,
xright=i.sep[3]+0.5,
ytop=i.sep[4]+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=FALSE,
border=sep.color[1]
)
}
}
col.sepList <- sepList[[2]]
if (!.invalid(col.sepList)) {
for (i in seq_along(col.sepList)) {
i.sep <- col.sepList[[i]]
rect(
xleft=i.sep[1]+0.5,
ybottom=i.sep[2]+0.5,
xright=i.sep[3]+0.5,
ytop=i.sep[4]+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=FALSE,
border=sep.color[2]
)
}
}
## show traces
min.scale <- min(breaks)
max.scale <- max(breaks)
x.scaled <- .scale.x(t(x),min.scale,max.scale)
if(.invalid(hline)) hline=median(breaks)
if(.invalid(vline)) vline=median(breaks)
if(trace %in% c("both","column")) {
for( i in colInd ) {
if(!.invalid(vline)) {
vline.vals <- .scale.x(vline,min.scale,max.scale)
abline(v=i-0.5+vline.vals, col=linecol, lty=2)
}
xv <- rep(i, nrow(x.scaled)) + x.scaled[,i] - 0.5
xv <- c(xv[1], xv)
yv <- seq_along(xv)-0.5
lines(x=xv, y=yv, lwd=1, col=tracecol, type="s")
}
}
if(trace %in% c("both","row")) {
for( i in rowInd ) {
if(!.invalid(hline)) {
hline.vals <- .scale.x(hline, min.scale, max.scale)
abline(h=i+hline, col=linecol, lty=2)
}
yv <- rep(i,ncol(x.scaled)) + x.scaled[i,] - 0.5
yv <- rev(c(yv[1],yv))
xv <- length(yv):1-0.5
lines(x=xv, y=yv, lwd=1, col=tracecol, type="s")
}
}
## cellnote
if(!.invalid(cellnote)) {
text(x=c(row(cellnote)),
y=c(col(cellnote)),
labels=c(cellnote),
col=notecol,
cex=cex.note)
}
## 2) plot.row.partition
if(plot.row.partition |plot.row.clusters) { ##row.clusters
par(mar=c(margins[1], 0.5, 0, 0.1))
row.clusters.unique <- unique(row.clusters)
row.clusters.unique <- row.clusters.unique[!is.na(row.clusters.unique)]
image(rbind(seq_len(nr)),
xlim=0.5+c(0,1), ylim=0.5+c(0,nr),
col=par("bg"),
axes=FALSE, add=force_add, useRaster=useRaster)
if (!.invalid(plot.row.partitionList)) {
for (i in seq_along(plot.row.partitionList)) {
i.sep <- plot.row.partitionList[[i]]
rect(
xleft=0+0.5,
ybottom=i.sep[2]+0.5,
xright=1+0.5,
ytop=i.sep[4]+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=color.partition.box,
border=color.partition.border
)
g <- row.clusters.unique[i]
s <- g
text(x=1,y=(i.sep[2]+0.5+i.sep[4]+0.5)/2, labels=s, col=color.partition.border,
cex=cex.partition,
srt=90
)
}
}
}
## 3) plot.col.partition
if(plot.col.partition | plot.col.clusters) {
par(mar=c(0.1,0,0,margins[4]))
col.clusters.unique <- unique(col.clusters)
col.clusters.unique <- col.clusters.unique[!is.na(col.clusters.unique)]
if (!is.null(gene_position_breaks)) {
image(cbind(seq_len(nc)),
xlim=0.5+c(0,nc), ylim=0.5+c(0,1),
col=par("bg"),
axes=FALSE, add=force_add, useRaster=useRaster)
} else {
image(cbind(seq_len(nc)),
xlim=0.5+c(0,nc), ylim=0.5+c(0,1),
col=par("bg"),
axes=FALSE, add=force_add, useRaster=useRaster)
}
if (!.invalid(plot.col.partitionList)) {
for (i in seq_along(plot.col.partitionList)) {
i.sep <- plot.col.partitionList[[i]]
rect(
xleft=i.sep[1]+0.5,
ybottom=0+0.5,
xright=i.sep[3]+0.5,
ytop=1+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=color.partition.box,
border=color.partition.border
)
g <- col.clusters.unique[i]
s <- g
text(x=(i.sep[1]+0.5+i.sep[3]+0.5)/2, y=1, labels=s, col=color.partition.border,
cex=cex.partition,
srt=0
)
}
}
}
## 4) draw the side color bars - for row
if(!.invalid(RowIndividualColors)) {
par(mar=c(margins[1],0,0,0.5))
# image(rbind(1:nr),col=RowIndividualColors[rowInd, 1],axes=FALSE,add=force_add)
image(rbind(seq_len(nr)),col=RowIndividualColors[rowInd, 1], axes=FALSE, add=FALSE, useRaster=useRaster)
if (dim(RowIndividualColors)[2] > 1) {
par(mar=c(margins[1],0,0,0.5))
image(rbind(seq_len(nr)),col=RowIndividualColors[rowInd, 2], axes=FALSE, add=force_add, useRaster=useRaster)
}
}
## 5) draw the side color bars - for col
if(!.invalid(ColIndividualColors)) {
par(mar=c(0.5,0,0,margins[4]))
if (!is.null(gene_position_breaks)) {
#image(cbind(gene_position_breaks), col=ColIndividualColors[colInd], axes=FALSE, add=force_add, useRaster=FALSE)
image(gene_position_breaks, 1, cbind(seq_len(nc)), col=ColIndividualColors[colInd], axes=FALSE, add=force_add, useRaster=FALSE)
} else {
image(cbind(seq_len(nc)), col=ColIndividualColors[colInd], axes=FALSE, add=force_add, useRaster=useRaster)
}
}
## 6) row-dend
par(mar=c(margins[1], 0, 0, 0))
if(dendrogram %in% c("both","row")) {
plot(ddr,horiz=TRUE,axes=FALSE,yaxs="i",leaflab="none")
} else {
.plot.text(ylim=range(iy))
if (sideRow==2) {
.sideRow <- par("usr")[2]-0.5*srtCol/90
text(.sideRow,iy,labels=labRow,srt=srtRow,pos=1,xpd=TRUE,cex=cexRow)
}
}
par(mar=c(0,0,0,0))
plot(1, type="n", axes=FALSE, xlab="", ylab="")
legend(x=0.6,y=1.2 ,legend=annotations_legend[,1],
cex=1.2,
fill=annotations_legend[,2],
ncol=grouping_key_coln)
## 7) col-dend and title
mar3 <- (if(!is.null(main)) mar.main else 0) +
(if(!is.null(sub)) mar.sub else 0)
par(mar=c(0,0,mar3,margins[4]))
if(dendrogram %in% c("both","column")) {
plot(ddc,axes=FALSE,xaxs="i",leaflab="none")
} else {
if(key) {
.plot.text(xlim=range(seq_len(nc)))
}
if (sideCol==3){
.sideCol <- par("usr")[3]+0.5*srtCol/90
text(seq_len(nc),.sideCol,labels=labCol,srt=srtCol,pos=1,xpd=TRUE,cex=cexCol)
}
}
## title
if (is.null(sub)) {
main.line <- 1
}
else {
main.line <- 3
}
if(!is.null(main)) {
title(main,cex.main=cex.main, adj=adj.main, mgp=mgp.main, font.main=font.main, line=main.line)
}
if(!is.null(sub)) {
title(sub,cex.main=cex.sub, adj=adj.main, mgp=mgp.main, font.main=font.sub, line=0)
}
##if(!is.null(main)) title(main,cex.main=1.5*op[["cex.main"]])
## 8) plot the color-key
if(key) {
cex.key <- 0.75
op.ori <- par()
par(mar=c(2,1.5,0.75,1)*keysize,cex=cex.key,mgp=c(0.75,0,0),tcl=-0.05)
z <- seq(x.range[1],x.range[2],length=length(colors))
flog.debug(paste("::inferCNV::heatmap.cnv colorkey z range: ", paste(z, collapse=","), sep=""))
flog.debug(paste("::inferCNV::heatmap.cnv colorkey breaks range: ", paste(breaks, collapse=","), sep=""))
flog.debug(paste("::inferCNV::heatmap.cnv colorkey colors range: ", paste(colors, collapse=","), sep=""))
image(z=matrix(z, ncol=1),
col=colors,
breaks=breaks,
xaxt="n",
yaxt="n",
xlab=key.xlab,
ylab="",
main="",
add=force_add,
useRaster=useRaster
)
par(usr=c(0,1,0,1))
lv <- pretty(breaks)
xv <- .scale.x(as.numeric(lv), x.range[1], x.range[2])
axis(1,at=xv,labels=lv,cex.axis=cex.key*1)
if(density.info=="density") {
## Experimental : also plot density of data
dens <- density(x, adjust=densadj,na.rm=TRUE)
omit <- dens$x < min(breaks) | dens$x > max(breaks)
dens$x <- dens$x[-omit]
dens$y <- dens$y[-omit]
dens$x <- .scale.x(dens$x, x.range[1], x.range[2])
lines(dens$x,dens$y / max(dens$y) * 0.95, col=denscol,lwd=1)
axis(2, at=pretty(dens$y)/max(dens$y) * 0.95, pretty(dens$y), cex.axis=cex.key*1)
##title("Color Key and Density",cex.lab=cex.key*0.25)
title(key.title, cex.main=cex.key, font.main=1)
mtext(side=2, "Density", line=0.75, cex=cex.key)
} else if(density.info=="histogram") {
h <- hist(x, plot=FALSE, breaks=breaks)
hx <- .scale.x(breaks, x.range[1], x.range[2])
hy <- c(h$counts, h$counts[length(h$counts)])
lines(hx, hy/max(hy)*0.95, lwd=1,type="s", col=denscol)
axis(2, at=pretty(hy)/max(hy)*0.95, pretty(hy), cex.axis=cex.key*1)
##title("Color Key and Histogram",cex.main=cex.key*0.25)
title(key.title, cex.main=cex.key, font.main=1)
mtext(side=2, key.ylab, line=0.75, cex=cex.key)
} else {
title(key.title, cex.main=cex.key, font.main=1)
}
par(mar=op.ori$mar, cex=op.ori$cex, mgp=op.ori$mgp, tcl=op.ori$tcl, usr=op.ori$usr)
} else{
if(!force_add){
.plot.text()
}
}
## 9)
if(plot.row.individuals) {
.plot.text("Row\nIndividuals", cex=cex.text, bg="white")
for (i in seq_len(ir)) {
##.plot.text()
tmp <- plot.row.individuals.list[[i]]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
}
}
## 10)
if(plot.col.individuals) {
.plot.text("Column\nIndividuals",cex=cex.text,bg="white",srt=90)
for (i in seq_len(ic)) {
##.plot.text()
tmp <- plot.col.individuals.list[[i]]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
}
}
## 11) for RowPlot from bottom
if (plot.row.clusters) {
.plot.text("Row\nClusters", cex=cex.text, bg="white")
tmp <- plot.row.clusters.list[[1]]
row.data <- row.data[rowInd]
for (i in unique(row.clusters)) {
i.x <- row.data[row.clusters==i]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
i.main <- sprintf("Row group %s (n=%s)",i,length(i.x))
title(i.main, cex.main=1, font.main=1)
}
}
## 12) for ColPlot from left
if (plot.col.clusters) {
.plot.text("Col\nClusters", cex=cex.text, bg="white", srt=90)
tmp <- plot.col.clusters.list[[1]]
col.data <- if(revC) col.data[rev(colInd)] else col.data[colInd]
for (i in unique(col.clusters)){
i.x <- col.data[col.clusters==i]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
i.main <- sprintf("Col group %s (n=%s)", i, length(i.x))
title(i.main, cex.main=1, font.main=1)
}
}
## 13)
if(plot.row.clustering) {
.plot.text("Row\nClustering",cex=cex.text,bg="white")
for (i in seq_len(cr)) {
##.plot.text()
tmp <- plot.row.clustering.list[[i]]
for(j in seq_along(tmp)){
eval(tmp[[j]])
}
}
}
## 14)
if(plot.col.clustering) {
.plot.text("Column\nClustering", cex=cex.text, bg="white", srt=90)
for (i in seq_len(cc)) {
##.plot.text()
tmp <- plot.col.clustering.list[[i]]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
}
}
## 15) text
if (!.invalid(text.box) & if.plot.info) {
.plot.text(text.box, cex=cex.text, bg="gray75")
} else{
if (flag.text){
.plot.text()
}
}
} else {
x.save <- x
}
# ret <-
# list(x.ori=x.ori,
# x=x.save,
# rowInd=rowInd,colInd=colInd,
# row.clusters=row.clusters,col.clusters=col.clusters,
# dist.row=dist.row,dist.col=dist.col,
# hclust.row=hclust.row,hclust.col=hclust.col,
# kr=kr,kc=kc
# )
ret <-
list(x.ori=x.ori,
x=x.save,
rowInd=rowInd, colInd=colInd,
# row.clusters=row.clusters, col.clusters=col.clusters,
kr=kr, kc=kc
)
# class(ret) <- c("hclustering",class(ret))
invisible(ret)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## The official version from the package GMD is now archived
## https://cran.r-project.org/web/packages/GMD/index.html
gdist <-
function(x,
method="euclidean",
MoreArgs=NULL,
diag=FALSE,
upper=FALSE
)
{
if(method %in% c("correlation","correlation.of.observations")) {
FUN <- function(x,...) {
as.dist(1-cor(t(x),y=NULL,...),diag=diag,upper=upper)
}
if (.invalid(MoreArgs)) {
MoreArgs=list(method="pearson",use="everything")
}
} else if(method %in% c("correlation.of.variables")) {
FUN <- function(x,...) {
as.dist(1-cor(x,y=NULL,...),diag=diag,upper=upper)
}
if (.invalid(MoreArgs)) {
MoreArgs=list(method="pearson",use="everything")
}
}
COMMON_METHODS <-
c("euclidean","maximum",
"manhattan","canberra",
"binary","minkowski"
)
if(method %in% COMMON_METHODS) {
d <- dist(x=x, method=method, diag=diag, upper=upper, p=MoreArgs$p)
} else if (method %in% c("correlation","correlation.of.observations","correlation.of.variables")) {
##d <- .call.FUN(FUN,x,MoreArgs)
d <- FUN(x, method=MoreArgs$method, use=MoreArgs$use)
attr(d, "method") <- method
} else {
FUN <- match.fun(method)
MoreArgs[["diag"]] <- diag
MoreArgs[["upper"]] <- upper
d <- .call.FUN(FUN,x,MoreArgs)
## check attributes of the dist object ##
if(is.null(attr(d,"method"))) {
attr(d,"method") <- method
}
if(is.null(attr(d,"call"))) {
attr(d,"call") <- match.call()
}
if(is.null(attr(d,"Size"))) {
warning(sprintf("The `dist' object returned by %s does not contain a specified attribute of `Size'.",attr(d,"method")))
}
if(is.null(attr(d,"Labels"))) {
warning(sprintf("The `dist' object returned by %s does not contain a specified attribute of `Labels'.",attr(d,"method")))
}
}
attr(d,"Diag") <- diag
attr(d,"Upper") <- upper
class(d) <- "dist"
return(d)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Get row or column lines of separation for \code{heatmap.3} according to clusters
## param clusters a numerical vector, indicating the cluster labels of observations.
## param type string, one of the following: \code{c("row","column","both")}
get.sep <-
function(clusters,type=c("row","column","both"))
{
## if(!is.numeric(clusters) | !(.is.grouped(clusters))){
## stop("`clusters' should be a grouped numeric vector.")
## }
tmp.whichna <- which(is.na(clusters))
tmp.which <- which(!duplicated(clusters))
tmp.sep <- data.frame(start=tmp.which, end=c(tmp.which[-1], length(clusters)+1)-1)
tmp.sep2 <- tmp.sep[tmp.sep$start<=tmp.sep$end,]
## lines of separation
sepList <- list()
if (type=="row") {
xmax <- length(clusters)
for(i.s in seq_len(nrow(tmp.sep2))) {
sepList[[i.s]] <- c(0, tmp.sep2[i.s, 1]-1, xmax, tmp.sep2[i.s, 2])
}
} else if (type=="column") {
ymax <- length(clusters)
for(i.s in seq_len(nrow(tmp.sep2))) {
sepList[[i.s]] <- c(tmp.sep2[i.s, 1]-1, 0, tmp.sep2[i.s, 2], ymax)
}
} else if (type=="both") {
for(i.s in seq_len(nrow(tmp.sep2))) {
sepList[[i.s]] <- c(tmp.sep2[i.s, 1]-1, tmp.sep2[i.s, 1] - 1, tmp.sep2[i.s, 2], tmp.sep2[i.s, 2])
}
}
sepList
}
#####################################################
## Custom infercnv functions related to visualization
depress_low_signal_midpt_ratio <- function(infercnv_obj, expr_mean, midpt_ratio=0.2, slope=20) {
expr_bounds = get_average_bounds(infercnv_obj)
delta_mean = max(expr_mean - expr_bounds[1], expr_bounds[2] - expr_mean)
delta_midpt = delta_mean * midpt_ratio
infercnv_obj <- depress_log_signal_midpt_val(infercnv_obj, expr_mean, delta_midpt, slope)
return(infercnv_obj)
}
depress_log_signal_midpt_val <- function(infercnv_obj, expr_mean, delta_midpt, slope=20) {
infercnv_obj@expr.data <- .apply_logistic_val_adj(infercnv_obj@expr.data, expr_mean, delta_midpt, slope)
return(infercnv_obj)
}
.apply_logistic_val_adj <- function(vals_matrix, expr_mean, delta_midpt, slope) {
adjust_value <- function(x) {
newval = x
val = abs(x - expr_mean)
p = .logistic(val, delta_midpt, slope)
if (x > expr_mean) {
newval = expr_mean + p*val
} else if (x < expr_mean) {
newval = expr_mean - p*val
}
return(newval)
}
vals_matrix <- apply(vals_matrix, seq_len(2), adjust_value)
return(vals_matrix)
}
# method that returns an artificial dendrogram with only 1 branch
# since at least 2 elements need to be present for building one normally
# but we need to be able to merge this lone branch to the bigger dendrogram for plotting
.single_element_dendrogram <- function(unique_label) {
dfake = list()
dfake[[1]] = 1L
attr(dfake, "class") = "dendrogram"
attr(dfake, "height") = 1
attr(dfake, "members") = 1L
attr(dfake[[1]], "class") = "dendrogram"
attr(dfake[[1]], "leaf") = TRUE
attr(dfake[[1]], "label") = unique_label
attr(dfake[[1]], "height") = 0
return(dfake)
}
.pairwise_dendrogram <- function(labels) {
dfake = list()
dfake[[1]] = 1L
dfake[[2]] = 1L
attr(dfake, "class") = "dendrogram"
attr(dfake, "height") = 1
attr(dfake, "members") = 2L
attr(dfake[[1]], "class") = "dendrogram"
attr(dfake[[1]], "leaf") = TRUE
attr(dfake[[1]], "label") = labels[1]
attr(dfake[[1]], "height") = 0
attr(dfake[[2]], "class") = "dendrogram"
attr(dfake[[2]], "leaf") = TRUE
attr(dfake[[2]], "label") = labels[2]
attr(dfake[[2]], "height") = 0
return(dfake)
}
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Defines functions .pairwise_dendrogram .single_element_dendrogram .apply_logistic_val_adj depress_log_signal_midpt_val depress_low_signal_midpt_ratio get.sep gdist heatmap.cnv .plot.text .scale.x .scale.data .call.FUN .is.grouped .invalid .plot_cnv_references .plot_observations_layout .plot_cnv_observations plot_cnv get_group_color_palette
Documented in plot_cnv
#!/usr/bin/env Rscript
# Returns the color palette for contigs.
#
# Returns:
# Color Palette
get_group_color_palette <- function(){
return(colorRampPalette(RColorBrewer::brewer.pal(12,"Set3")))
}
#' @description Formats the data and sends it for plotting.
#'
#' @title Plot the matrix as a heatmap, with cells as rows and genes as columns, ordered according to chromosome
#'
#' @param infercnv_obj infercnv object
#' @param out_dir Directory in which to save pdf and other output.
#' @param title Plot title.
#' @param obs_title Title for the observations matrix.
#' @param ref_title Title for the reference matrix.
#' @param cluster_by_groups Whether to cluster observations by their annotations or not. Using this ignores k_obs_groups.
#' @param cluster_references Whether to cluster references within their annotations or not. (dendrogram not displayed)
#' @param plot_chr_scale Whether to scale the chromosme width on the heatmap based on their actual size rather than just the number of expressed genes.
#' @param chr_lengths A named list of chromsomes lengths to use when plot_chr_scale=TRUE, or else chromosome size is assumed to be the last chromosome's stop position + 10k bp
#' @param k_obs_groups Number of groups to break observation into.
#' @param contig_cex Contig text size.
#' @param x.center Value on which to center expression.
#' @param x.range vector containing the extreme values in the heatmap (ie. c(-3,4) )
#' @param hclust_method Clustering method to use for hclust.
#' @param custom_color_pal Specify a custom set of colors for the heatmap.
#' Has to be in the shape color.palette(c("darkblue", "white", "darkred"),
#' c(2, 2))
#' @param color_safe_pal Logical indication of using a color blindness safe palette.
#' @param output_filename Filename to save the figure to.
#' @param output_format format for heatmap image file (default: 'png'), options('png', 'pdf', NA)
#' If set to NA, will print graphics natively
#' @param png_res Resolution for png output.
#' @param dynamic_resize Factor (>= 0) by which to scale the dynamic resize of the observation
#' heatmap and the overall plot based on how many cells there are.
#' Default is 0, which disables the scaling. Try 1 first if you want to enable.
#' @param ref_contig If given, will focus cluster on only genes in this contig.
#' @param write_expr_matrix Includes writing a matrix file containing the expression data that is plotted in the heatmap.
#' @param useRaster Whether to use rasterization for drawing heatmap. Only disable if it produces an error as it is much faster than not using it.
#'
#' @return A list of all relevent settings used for the plotting to be able to reuse them in another plot call while keeping consistant plotting settings, most importantly x.range.
#'
#'
#' @export
#'
#' @examples
#' # data(infercnv_data_example)
#' # data(infercnv_annots_example)
#' # data(infercnv_genes_example)
#'
#' # infercnv_object_example <- infercnv::CreateInfercnvObject(raw_counts_matrix=infercnv_data_example,
#' # gene_order_file=infercnv_genes_example,
#' # annotations_file=infercnv_annots_example,
#' # ref_group_names=c("normal"))
#'
#' # infercnv_object_example <- infercnv::run(infercnv_object_example,
#' # cutoff=1,
#' # out_dir=tempfile(),
#' # cluster_by_groups=TRUE,
#' # denoise=TRUE,
#' # HMM=FALSE,
#' # num_threads=2,
#' # no_plot=TRUE)
#'
#' data(infercnv_object_example)
#'
#' plot_cnv(infercnv_object_example,
#' out_dir=tempfile(),
#' obs_title="Observations (Cells)",
#' ref_title="References (Cells)",
#' cluster_by_groups=TRUE,
#' x.center=1,
#' x.range="auto",
#' hclust_method='ward.D',
#' color_safe_pal=FALSE,
#' output_filename="infercnv",
#' output_format="png",
#' png_res=300,
#' dynamic_resize=0
#' )
#'
plot_cnv <- function(infercnv_obj,
out_dir=".",
title="inferCNV",
obs_title="Observations (Cells)",
ref_title="References (Cells)",
cluster_by_groups=TRUE,
cluster_references=TRUE,
plot_chr_scale=FALSE,
chr_lengths=NULL,
k_obs_groups = 3,
contig_cex=1,
x.center=mean(infercnv_obj@expr.data),
x.range="auto", #NA,
hclust_method='ward.D',
custom_color_pal=NULL,
color_safe_pal=FALSE,
output_filename="infercnv",
output_format="png", #pdf, png, NA
png_res=300,
dynamic_resize=0,
ref_contig = NULL,
write_expr_matrix=FALSE,
useRaster=TRUE) {
# arg validations
if (! hclust_method %in% C_HCLUST_METHODS) {
stop(sprintf("Error, hclust_method: %s is not supported", hclust_method))
}
if ( (! is.na(output_format) ) & (! output_format %in% C_OUTPUT_FORMAT) ) {
stop(sprintf("Error, output_format: %s is not supported", output_format) )
}
if(!file.exists(out_dir)){
dir.create(out_dir)
}
plot_data = infercnv_obj@expr.data
flog.info(paste("::plot_cnv:Start", sep=""))
flog.info(paste("::plot_cnv:Current data dimensions (r,c)=",
paste(dim(plot_data), collapse=","),
" Total=", sum(plot_data, na.rm=TRUE),
" Min=", min(plot_data, na.rm=TRUE),
" Max=", max(plot_data, na.rm=TRUE),
".", sep=""))
flog.info(paste("::plot_cnv:Depending on the size of the matrix",
" this may take a moment.",
sep=""))
if (write_expr_matrix) {
expr_dat_file <- paste(out_dir, paste("expr.", output_filename, ".dat", sep=""), sep="/")
if ("matrix" %in% is(plot_data)) {
write.table(plot_data, file=expr_dat_file, quote=FALSE, sep="\t")
}
}
if (! any(is.na(x.range))) {
if ( (length(x.range) == 1) & (x.range[1] == "auto") ) {
# examine distribution of data that's off-center, since much of the center could
# correspond to a mass of data that has been wiped out during noise reduction
quantiles = quantile(plot_data[plot_data != x.center], c(0.01, 0.99))
# determine max distance from the center.
delta = max( abs( c(x.center - quantiles[1], quantiles[2] - x.center) ) )
low_threshold = x.center - delta
high_threshold = x.center + delta
x.range = c(low_threshold, high_threshold)
flog.info(sprintf("plot_cnv(): auto thresholding at: (%f , %f)", low_threshold, high_threshold))
} else {
# use defined values
low_threshold = x.range[1]
high_threshold = x.range[2]
if (low_threshold > x.center | high_threshold < x.center | low_threshold >= high_threshold) {
stop(paste("Error, problem with relative values of x.range: ", x.range, ", and x.center: ", x.center))
}
}
plot_data[plot_data < low_threshold] <- low_threshold
plot_data[plot_data > high_threshold] <- high_threshold
infercnv_obj@expr.data <- plot_data #because used again below...
}
# Contigs
contigs = infercnv_obj@gene_order[[C_CHR]]
unique_contigs <- unique(contigs)
n_contig <- length(unique_contigs)
ct.colors <- get_group_color_palette()(n_contig)
names(ct.colors) <- unique_contigs
# Select color palette
if (!is.null(custom_color_pal)) {
custom_pal = custom_color_pal
} else if (color_safe_pal == FALSE){
custom_pal <- color.palette(c("darkblue", "white", "darkred"),
c(2, 2))
} else {
custom_pal <- color.palette(c("purple3", "white", "darkorange2"),
c(2, 2))
}
## Row separation based on reference
ref_idx <- NULL
if (has_reference_cells(infercnv_obj)) {
ref_idx <- unlist(infercnv_obj@reference_grouped_cell_indices)
ref_idx = ref_idx[order(ref_idx)]
}
# Column seperation by contig and label axes with only one instance of name
contig_tbl <- table(contigs)[unique_contigs]
col_sep <- cumsum(contig_tbl)
col_sep <- col_sep[-1 * length(col_sep)] ## FIXME: removing last entry?
# These labels are axes labels, indicating contigs at the first column only
# and leaving the rest blank.
# contig_labels <- c()
# contig_names <-c()
# for (contig_name in names(contig_tbl)){
# contig_labels <- c(contig_labels,
# contig_name,
# rep("", contig_tbl[contig_name] - 1))
# contig_names <- c(contig_names,rep(contig_name,contig_tbl[contig_name]))
# }
contig_labels = names(contig_tbl)
contig_names = unlist(lapply(contig_labels, function(contig_name) {rep(contig_name, contig_tbl[contig_name])}))
# contig_sizes = vapply(contig_labels, function(contig_name) {contig_tbl[contig_name]}, integer(1))
# contig_positions = c(1, cumsum(contig_sizes[seq_len(length(contig_sizes) - 1)]) + 1)
# Calculate how many rows will be made for the number of columns in the grouping key
grouping_key_coln <- c()
obs_annotations_names <- names(infercnv_obj@observation_grouped_cell_indices)
# obs_annotations_groups: integer vec named by cells, set to index according to category name vec above.
obs_annotations_groups = rep(-1, length(colnames(infercnv_obj@expr.data))) # init
names(obs_annotations_groups) = colnames(infercnv_obj@expr.data)
obs_index_groupings = infercnv_obj@observation_grouped_cell_indices
counter <- 1
for (obs_index_group in obs_index_groupings) {
obs_annotations_groups[ obs_index_group ] <- counter
counter <- counter + 1
}
# restrict to just the obs indices
if (! is.null(ref_idx)) {
obs_annotations_groups <- obs_annotations_groups[-ref_idx]
}
if (is.null(dynamic_resize) | dynamic_resize < 0) {
flog.warn(paste("invalid dynamic_resize value: ", dynamic_resize, sep=""))
dynamic_resize = 0
}
dynamic_extension = 0
nobs = length(unlist(infercnv_obj@observation_grouped_cell_indices))
if (nobs > 200) {
dynamic_extension = dynamic_resize * 3.6 * (nobs - 200)/200
}
grouping_key_coln[1] <- floor(123/(max(nchar(obs_annotations_names)) + 4)) ## 123 is the max width in number of characters, 4 is the space taken by the color box itself and the spacing around it
if (grouping_key_coln[1] < 1) {
grouping_key_coln[1] <- 1
}
name_ref_groups = names(infercnv_obj@reference_grouped_cell_indices)
if (is.null(name_ref_groups)) {
grouping_key_coln[2] = 1
} else {
grouping_key_coln[2] <- floor(123/(max(nchar(name_ref_groups)) + 4)) ## 123 is the max width in number of characters, 4 is the space taken by the color box itself and the spacing around it
if (grouping_key_coln[2] < 1) {
grouping_key_coln[2] <- 1
}
}
grouping_key_rown <- c()
grouping_key_rown[1] <- ceiling(length(obs_annotations_names)/grouping_key_coln[1])
grouping_key_rown[2] <- ceiling(length(name_ref_groups)/grouping_key_coln[2])
# Calculate how much bigger the output needs to be to accodomate for the grouping key
grouping_key_height <- c((grouping_key_rown[2] + 2) * 0.175, (grouping_key_rown[1] + 3) * 0.175)
# Rows observations, Columns CHR
if (! is.na(output_format)) {
if (output_format == "pdf") {
pdf(paste(out_dir, paste(output_filename, ".pdf", sep=""), sep="/"),
useDingbats=FALSE,
width=11,
height=(8.22 + sum(grouping_key_height)) + dynamic_extension,
paper="special")
} else if (output_format == "png") {
png(paste(out_dir, paste(output_filename, ".png", sep=""), sep="/"),
width=10,
height=(8.22 + sum(grouping_key_height)) + dynamic_extension,
units="in",
res=png_res)
}
}
# Plot observations
## Make Observation Samples
## Remove observation col names, too many to plot
## Will try and keep the reference names
## They are more informative anyway
obs_data <- infercnv_obj@expr.data
if (!is.null(ref_idx)){
obs_data <- plot_data[, -ref_idx, drop=FALSE]
if (ncol(obs_data) == 1) {
# hack for dealing with single entries
plot_data <- cbind(obs_data, obs_data)
names(obs_data) <- c("", names(obs_data)[1])
}
}
obs_data <- t(obs_data)
# Subsample the data to only the references and update the ref_group indexes to match their new indexes
# ref_data_t <- plot_data[, ref_idx, drop=FALSE]
ref_data_t <- NULL
updated_ref_groups <- list()
current_ref_count <- 1
current_grp_idx <- 1
plot_data <-infercnv_obj@expr.data
ref_groups = infercnv_obj@reference_grouped_cell_indices
for (ref_grp in ref_groups) {
ref_data_t <- cbind(ref_data_t, plot_data[, ref_grp, drop=FALSE])
updated_ref_groups[[current_grp_idx]] = seq(current_ref_count, current_ref_count + length(ref_grp) - 1)
current_ref_count <- current_ref_count + length(ref_grp)
current_grp_idx <- current_grp_idx + 1
}
ref_groups <- updated_ref_groups
nb_breaks <- 16
# breaksList_t <-
# seq(min(min(obs_data, na.rm=TRUE), min(ref_data_t, na.rm=TRUE)),
# max(max(obs_data,na.rm=TRUE), max(ref_data_t, na.rm=TRUE)),
# length.out=nb_breaks)
breaksList_t <- seq(x.range[1], x.range[2], length.out=nb_breaks)
gene_position_breaks = NULL
if (plot_chr_scale) {
# gene table to heatmap width
chr_name_list = unique(infercnv_obj@gene_order[["chr"]])
# get average distance for tail end?
# optionally give vector of chr lengths
# if chr_lengths not given, take furthest gene and add 10k bp
if (is.null(chr_lengths)) {
chr_lengths = c()
for (chr_name in chr_name_list) {
chr_lengths = c(chr_lengths, max(infercnv_obj@gene_order$stop[which(infercnv_obj@gene_order$chr == chr_name)]) + 10000)
}
names(chr_lengths) = chr_name_list
}
gene_position_breaks = vector(mode="integer", length=(length(unlist(infercnv_obj@gene_order$chr)) + 1))
sum_previous_contigs = 0
gene_position_breaks[1] = 1
current_idx = 2
col_sep_idx = 1
for (chr_name in chr_name_list) {
index_pos = which(infercnv_obj@gene_order$chr == chr_name)
latest_position = 1
for (i in index_pos[2:length(index_pos)]) {
gene_position_breaks[current_idx] = sum_previous_contigs + ((latest_position + infercnv_obj@gene_order$start[i]) / 2)
latest_position = max(infercnv_obj@gene_order$stop[i], latest_position)
current_idx = current_idx + 1
}
gene_position_breaks[current_idx] = sum_previous_contigs + chr_lengths[chr_name]
current_idx = current_idx + 1
sum_previous_contigs = sum_previous_contigs + chr_lengths[chr_name]
if (col_sep_idx != length(chr_name_list)) {
col_sep[col_sep_idx] = sum_previous_contigs
col_sep_idx = col_sep_idx + 1
}
}
}
# Create file base for plotting output
force_layout <- .plot_observations_layout(grouping_key_height=grouping_key_height, dynamic_extension=dynamic_extension)
.plot_cnv_observations(infercnv_obj=infercnv_obj,
obs_data=obs_data,
file_base_name=out_dir,
do_plot=!is.na(output_format),
output_filename_prefix=output_filename,
cluster_contig=ref_contig,
contigs=contigs,
contig_colors=ct.colors[contigs],
contig_labels=contig_labels,
contig_names=contig_names,
col_pal=custom_pal,
contig_seps=col_sep,
num_obs_groups=k_obs_groups,
obs_annotations_groups=obs_annotations_groups,
obs_annotations_names=obs_annotations_names,
grouping_key_coln=grouping_key_coln[1],
cluster_by_groups=cluster_by_groups,
cnv_title=title,
cnv_obs_title=obs_title,
contig_lab_size=contig_cex,
breaksList=breaksList_t,
gene_position_breaks=gene_position_breaks,
x.center=x.center,
hclust_method=hclust_method,
layout_lmat=force_layout[["lmat"]],
layout_lhei=force_layout[["lhei"]],
layout_lwid=force_layout[["lwid"]],
useRaster=useRaster)
obs_data <- NULL
if(!is.null(ref_idx)){
.plot_cnv_references(infercnv_obj=infercnv_obj,
ref_data=ref_data_t,
ref_groups=ref_groups,
name_ref_groups=name_ref_groups,
cluster_references=cluster_references,
hclust_method=hclust_method,
grouping_key_coln=grouping_key_coln[2],
col_pal=custom_pal,
contig_seps=col_sep,
file_base_name=out_dir,
do_plot=!is.na(output_format),
output_filename_prefix=output_filename,
cnv_ref_title=ref_title,
breaksList=breaksList_t,
gene_position_breaks=gene_position_breaks,
x.center=x.center,
layout_add=TRUE,
useRaster=useRaster)
}
if (! is.na(output_format)) {
dev.off()
}
return(list(cluster_by_groups = cluster_by_groups,
k_obs_groups = k_obs_groups,
contig_cex = contig_cex,
x.center = x.center,
x.range = x.range,
hclust_method = hclust_method,
color_safe_pal = color_safe_pal,
output_format = output_format,
png_res = png_res,
dynamic_resize = dynamic_resize))
}
# TODO Tested, test make files so turned off but can turn on and should pass.
# Plot the observational samples
#
# Args:
#' @param obs_data Data to plot as observations. Rows = Cells, Col = Genes.
#' @param col_pal The color palette to use.
#' @param contig_colors The colors for the contig bar.
#' @param contig_labels The labels for the contigs.
#' @param contig_names Names of the contigs.
#' @param contig_seps Indices for line seperators of contigs.
#' @param num_obs_groups Number of groups of observations to create.
#' @param file_base_name Base of the file to used to make output file names.
#' @param do_plot If FALSE, only write text files and does not run plotting.
#' @param cnv_title Title of the plot.
#' @param cnv_obs_title Title for the observation matrix.
#' @param contig_lab_size Text size for contigs.
#' @param cluster_contig A value directs cluster to only genes on this contig.
#' @param obs_annotations_groups Vector of observations annotations group assignation (as indices).
#' @param cluster_by_groups Whether to cluster observations by their annotations or not. Using this ignores num_obs_groups.
#' @param breaksList List of values used as splitters on coloring range.
#' @param gene_position_breaks List of gene break positions for scaled chromosome size plot
#' @param hclust_method Method to use for hclust.
#' @param testing If TRUE, does not plot anything.
#' @param layout_lmat lmat values to use in layout.
#' @param layout_lhei lhei values to use in layout.
#' @param layout_lwid lwid values to use in layout.
#
#' @return Void.
#'
#' @keywords internal
#' @noRd
#'
.plot_cnv_observations <- function(infercnv_obj,
obs_data,
col_pal,
contig_colors,
contig_labels,
contig_names,
contig_seps,
num_obs_groups,
file_base_name,
do_plot=TRUE,
output_filename_prefix,
cnv_title,
cnv_obs_title,
contig_lab_size=1,
contigs,
cluster_contig=NULL,
obs_annotations_groups,
obs_annotations_names,
grouping_key_coln,
cluster_by_groups,
breaksList,
gene_position_breaks,
x.center,
hclust_method="ward.D",
testing=FALSE,
layout_lmat=NULL,
layout_lhei=NULL,
layout_lwid=NULL,
useRaster=useRaster) {
flog.info("plot_cnv_observation:Start")
flog.info(paste("Observation data size: Cells=",
nrow(obs_data),
"Genes=",
ncol(obs_data),
sep=" "))
observation_file_base <- paste(file_base_name, sprintf("%s.observations.txt", output_filename_prefix), sep=.Platform$file.sep)
# Output dendrogram representation as Newick
# Need to precompute the dendrogram so we can manipulate
# it before the heatmap plot
## Optionally cluster by a specific contig
hcl_desc <- "General"
hcl_group_indices <- seq_len(ncol(obs_data))
if(!is.null(cluster_contig)){
# restricting to single contig
hcl_contig_indices <- which(contig_names %in% cluster_contig)
if(length(hcl_contig_indices) > 0 ) {
hcl_group_indices <- hcl_contig_indices
hcl_desc <- paste(cluster_contig, collapse="_")
flog.info(paste("plot_cnv_observation:Clustering only by contig ", cluster_contig))
infercnv_obj@tumor_subclusters = NULL # so that clustering is calculated based on selected contigs only without requiring the user to manually erase this field
} else {
flog.warn(paste("plot_cnv_observations: Not able to cluster by",
cluster_contig,
"Clustering by all genomic locations.",
"To cluster by local genomic location next time",
"select from:",
unique(contig_names),
collapse=",",
sep=" "))
}
}
obs_dendrogram <- list()
ordered_names <- NULL
isfirst <- TRUE
hcl_obs_annotations_groups <- vector()
obs_seps <- c()
sub_obs_seps <- c() # never use at this time? available if we want to add splits in the heatmap for subclusters
if (!is.null(infercnv_obj@tumor_subclusters)) {
if (cluster_by_groups) {
# for (i in seq(1, max(obs_annotations_groups))) {
for (i in seq_along(obs_annotations_names)) {
if (!is.null(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]])) {
obs_dendrogram[[i]] = as.dendrogram(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]])
ordered_names <- c(ordered_names, infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]$labels[infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]$order])
obs_seps <- c(obs_seps, length(ordered_names))
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, rep(i, length(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]$order)))
if (isfirst) {
write.tree(as.phylo(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
isfirst <- FALSE
}
else {
write.tree(as.phylo(infercnv_obj@tumor_subclusters$hc[[ obs_annotations_names[i] ]]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep), append=TRUE)
}
}
else { ## should only happen if there is only 1 cell in the group so the clustering method was not able to generate a hclust
#### actually happens with 2 cells only too, because can't cluster 2
if ((length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]])) == 2) || (length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]])) == 1)) {
if (length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]])) == 2) {
obs_dendrogram[[i]] <- .pairwise_dendrogram(colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]), drop=FALSE]))
}
else { ## == 1
obs_dendrogram[[i]] <- .single_element_dendrogram(colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]), drop=FALSE]))
}
ordered_names <- c(ordered_names, colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]), drop=FALSE]))
obs_seps <- c(obs_seps, length(ordered_names))
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, rep(i, length(unlist(infercnv_obj@tumor_subclusters$subclusters[[obs_annotations_names[i]]]))))
}
else {
flog.error("Unexpected error, should not happen.")
stop("Error")
}
}
}
if (length(obs_dendrogram) > 1) {
obs_dendrogram <- do.call(merge, obs_dendrogram)
} else {
obs_dendrogram <- obs_dendrogram[[1]]
}
split_groups <- rep(1, dim(obs_data)[1])
names(split_groups) <- ordered_names
for(subtumor in infercnv_obj@tumor_subclusters$subclusters[[ obs_annotations_names[i] ]]) {
sub_obs_seps <- c(sub_obs_seps, (sub_obs_seps[length(sub_obs_seps)] + length(subtumor)))
}
}
else {
obs_hcl <- infercnv_obj@tumor_subclusters$hc[["all_observations"]]
write.tree(as.phylo(obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
obs_dendrogram <- as.dendrogram(obs_hcl)
ordered_names <- obs_hcl$labels[obs_hcl$order]
split_groups <- cutree(obs_hcl, k=num_obs_groups)
split_groups <- split_groups[ordered_names]
hcl_obs_annotations_groups <- obs_annotations_groups[ordered_names]
# Make a file of members of each group
flog.info("plot_cnv_observation:Writing observations by grouping.")
for (cut_group in unique(split_groups)){
group_memb <- names(split_groups)[which(split_groups == cut_group)]
# Write group to file
memb_file <- file(paste(file_base_name,
paste(hcl_desc,"HCL",cut_group,"members.txt",sep="_"),
sep=.Platform$file.sep))
write.table(obs_data[group_memb,], memb_file)
# Record seperation
ordered_memb <- which(ordered_names %in% group_memb)
if (is.null(obs_seps)) {
obs_seps <- c(length(ordered_memb))
}
else {
obs_seps <- c(obs_seps, (obs_seps[length(obs_seps)] + length(ordered_memb)))
}
}
obs_seps <- c(obs_seps, length(ordered_names))
sub_obs_seps = obs_seps
}
}
else if (cluster_by_groups) { # at this point this and the else below should only be an error fallback, or when trying to plot before the subclustering step
## Clustering separately by groups (ie. patients)
# HCL with a inversely weighted euclidean distance.
flog.info(paste("clustering observations via method: ", hclust_method, sep=""))
for (i in seq_len(max(obs_annotations_groups))) {
cell_indices_in_group <- which(obs_annotations_groups == i)
num_cells_in_group <- length(cell_indices_in_group)
flog.info(sprintf("Number of cells in group(%d) is %d", i, num_cells_in_group))
if (num_cells_in_group < 2) {
flog.info(sprintf("Skipping group: %d, since less than 2 entries", i))
ordered_names <- c(ordered_names, row.names(obs_data[which(obs_annotations_groups == i),, drop=FALSE]))
# when only 1 cell in the group, we need an artificial dendrogram for it
obs_dendrogram[[length(obs_dendrogram) + 1]] = .single_element_dendrogram(unique_label=row.names(obs_data[which(obs_annotations_groups == i),, drop=FALSE]))
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, i)
if (isfirst) {
write(row.names(obs_data[which(obs_annotations_groups == i), ]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
isfirst <- FALSE
}
else {
write(row.names(obs_data[which(obs_annotations_groups == i), ]),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep), append=TRUE)
}
}
else {
data_to_cluster <- obs_data[cell_indices_in_group, hcl_group_indices, drop=FALSE]
flog.info(paste("group size being clustered: ", paste(dim(data_to_cluster), collapse=","), sep=" "))
group_obs_hcl <- hclust(dist(data_to_cluster), method=hclust_method)
ordered_names <- c(ordered_names, group_obs_hcl$labels[group_obs_hcl$order])
if (isfirst) {
write.tree(as.phylo(group_obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
isfirst <- FALSE
}
else {
write.tree(as.phylo(group_obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep), append=TRUE)
}
}
group_obs_dend <- as.dendrogram(group_obs_hcl)
obs_dendrogram[[length(obs_dendrogram) + 1]] <- group_obs_dend
hcl_obs_annotations_groups <- c(hcl_obs_annotations_groups, rep(i, num_cells_in_group))
obs_seps <- c(obs_seps, length(ordered_names))
}
if (length(obs_dendrogram) > 1) {
# merge separate dendrograms into a single dendrogram
obs_dendrogram <- do.call(merge, obs_dendrogram)
} else {
obs_dendrogram <- obs_dendrogram[[1]]
}
split_groups <- rep(1, dim(obs_data)[1])
names(split_groups) <- ordered_names
sub_obs_seps = obs_seps
}
else {
# clustering all groups together
# HCL with a inversely weighted euclidean distance.
flog.info(paste("clustering observations via method: ", hclust_method, sep=""))
if (nrow(obs_data) > 1) {
obs_hcl <- hclust(dist(obs_data[, hcl_group_indices]), method=hclust_method)
write.tree(as.phylo(obs_hcl),
file=paste(file_base_name, sprintf("%s.observations_dendrogram.txt", output_filename_prefix), sep=.Platform$file.sep))
obs_dendrogram <- as.dendrogram(obs_hcl)
ordered_names <- obs_hcl$labels[obs_hcl$order]
split_groups <- cutree(obs_hcl, k=num_obs_groups)
split_groups <- split_groups[ordered_names]
# Make a file of members of each group
flog.info("plot_cnv_observation:Writing observations by grouping.")
for (cut_group in unique(split_groups)){
group_memb <- names(split_groups)[which(split_groups == cut_group)]
# Write group to file
memb_file <- file(paste(file_base_name,
paste(hcl_desc,"HCL",cut_group,"members.txt",sep="_"),
sep=.Platform$file.sep))
write.table(obs_data[group_memb,], memb_file)
# Record seperation
ordered_memb <- which(ordered_names %in% group_memb)
if (is.null(obs_seps)) {
obs_seps <- c(length(ordered_memb))
}
else {
obs_seps <- c(obs_seps, (obs_seps[length(obs_seps)] + length(ordered_memb))) ## could have a tmp_seps that just lists them, then cumSum() on it
}
}
obs_seps <- c(obs_seps, length(ordered_names))
}
else {
obs_dendrogram <- .single_element_dendrogram(row.names(obs_data))
ordered_names <- row.names(obs_data)
obs_seps <- c(1)
split_groups <- c(1)
names(split_groups) <- ordered_names
}
# hcl_obs_annotations_groups <- obs_annotations_groups[obs_hcl$order]
sub_obs_seps = obs_seps
hcl_obs_annotations_groups <- obs_annotations_groups[ordered_names]
}
if (length(obs_seps) > 1) {
obs_seps <- obs_seps[length(obs_seps)] - obs_seps[(length(obs_seps) - 1):1]
}
# Output HCL group membership.
# Record locations of seperations
# Make colors based on groupings
row_groupings <- get_group_color_palette()(length(table(split_groups)))[split_groups]
row_groupings <- cbind(row_groupings, get_group_color_palette()(length(table(hcl_obs_annotations_groups)))[hcl_obs_annotations_groups])
annotations_legend <- cbind(obs_annotations_names, get_group_color_palette()(length(table(hcl_obs_annotations_groups))))
# Make a file of coloring and groupings
flog.info("plot_cnv_observation:Writing observation groupings/color.")
groups_file_name <- file.path(file_base_name, sprintf("%s.observation_groupings.txt", output_filename_prefix))
# file_groups <- rbind(split_groups,row_groupings)
file_groups <- cbind(split_groups, row_groupings[,1], hcl_obs_annotations_groups, row_groupings[,2])
#row.names(file_groups) <- c("Group","Color")
colnames(file_groups) <- c("Dendrogram Group", "Dendrogram Color", "Annotation Group", "Annotation Color")
# write.table(t(file_groups), groups_file_name)
write.table(file_groups, groups_file_name)
flog.info("plot_cnv_observation:Done writing observation groupings/color.")
# Generate the Sep list for heatmap.3
contigSepList <- create_sep_list(row_count=nrow(obs_data),
col_count=ncol(obs_data),
row_seps=obs_seps,
col_seps=contig_seps)
# reorder expression matrix based on dendrogram ordering
obs_data <- obs_data[ordered_names, ]
# Remove row/col labels, too cluttered
# and print.
orig_row_names <- row.names(obs_data)
row.names(obs_data) <- rep("", nrow(obs_data))
flog.info("plot_cnv_observation:Writing observation heatmap thresholds.")
heatmap_thresholds_file_name <- file.path(file_base_name, sprintf("%s.heatmap_thresholds.txt", output_filename_prefix))
write.table(breaksList, heatmap_thresholds_file_name, row.names=FALSE, col.names=FALSE)
flog.info("plot_cnv_observation:Done writing observation heatmap thresholds.")
if (do_plot) {
data_observations <- heatmap.cnv(obs_data,
Rowv=obs_dendrogram,
Colv=FALSE,
cluster.by.row=TRUE,
cluster.by.col=FALSE,
main=cnv_title,
ylab=cnv_obs_title,
margin.for.labCol=2,
xlab="Genomic Region",
key=TRUE,
labCol=contig_labels,
cexCol=contig_lab_size,
cexAt=c(1, contig_seps),
notecol="black",
density.info="histogram",
denscol="blue",
trace="none",
dendrogram="row",
cexRow=0.8,
breaks=breaksList,
gene_position_breaks=gene_position_breaks,
scale="none",
x.center=x.center,
color.FUN=col_pal,
if.plot=!testing,
# Seperate by contigs
sepList=contigSepList,
sep.color=c("black","black"),
sep.lty=1,
sep.lwd=1,
# Color rows by user defined cut
RowIndividualColors=row_groupings,
annotations_legend=annotations_legend,
grouping_key_coln=grouping_key_coln,
# Color by contigs
ColIndividualColors=contig_colors,
# Legend
key.title="Distribution of Expression",
key.xlab="Modified Expression",
key.ylab="Count",
# Layout
force_lmat=layout_lmat,
force_lwid=layout_lwid,
force_lhei=layout_lhei,
useRaster=useRaster)
}
# Write data to file.
if ("matrix" %in% is(obs_data)) {
flog.info(paste("plot_cnv_observations:Writing observation data to",
observation_file_base,
sep=" "))
row.names(obs_data) <- orig_row_names
if (do_plot) {
write.table(t(obs_data[data_observations$rowInd,data_observations$colInd]),
file=observation_file_base)
}
else {
# Rowv inherits dendrogram, Colv is FALSE
# rowInd = seq_len(nrow(ref_data)) == everything in normal order
# colInd = seq_len(ncol(ref_data)) == everything in normal order
write.table(t(obs_data), file=observation_file_base)
}
}
}
#' Not Testing, params ok.
#' Create the layout for the plot
#' This is a modification of the original
#' layout from the GMD heatmap.3 function
#'
#' Returns:
#' list with slots "lmat" (layout matrix),
#' "lhei" (height, numerix vector),
#' and "lwid" (widths, numeric vector)
#'
#' @keywords internal
#' @noRd
#'
.plot_observations_layout <- function(grouping_key_height, dynamic_extension)
{
## Plot observational samples
obs_lmat <- c(0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
# 8, 0, 10, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
8, 11, 10, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, # 9 = reference heatmap
0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, # 1 observations heatmap
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
5, 2, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
obs_lmat <- matrix(obs_lmat,ncol=14,byrow=TRUE)
# vector of heights for each of the above rows.
obs_lhei <- c(1.125, 2.215, .15,
.5, .5, .5,
.5, .5, .5,
.5, .5, .5 + dynamic_extension,
0.1, grouping_key_height[1], grouping_key_height[2], 0.13)
return(list(lmat=obs_lmat,
lhei=obs_lhei,
lwid=NULL))
}
# TODO Tested, test make files so turned off but can turn on and should pass.
# Plot the reference samples
#
#' Args:
#' infercnv_obj: infercnv obj to access the subclusters information
#' ref_data Data to plot as references. Rows = Cells, Col = Genes
#' ref_groups Groups of references to plot together.
#' col_pal The color palette to use.
#' contig_seps Indices for line seperators of contigs.
#' file_base_name Base of the file to used to make output file names.
#' do_plot If FALSE, only write text files and does not run plotting.
#' cnv_ref_title Title for reference matrix.
#' layout_lmat lmat values to use in the layout.
#' layout_lwid lwid values to use in the layout.
#' layout_lhei lhei values to use in the layout.
#' layout_add Indicates the ref image shoudl be added to the previous plot.
#' testing: Turns off plotting when true.
#'
#' Returns:
#' Void
#'
#' @keywords internal
#' @noRd
#'
.plot_cnv_references <- function(infercnv_obj,
ref_data,
ref_groups,
name_ref_groups,
cluster_references,
hclust_method,
grouping_key_coln,
col_pal,
contig_seps,
file_base_name,
do_plot=TRUE,
output_filename_prefix,
cnv_ref_title,
breaksList,
gene_position_breaks,
x.center=x.center,
layout_lmat=NULL,
layout_lwid=NULL,
layout_lhei=NULL,
layout_add=FALSE,
testing=FALSE,
useRaster=useRaster){
flog.info("plot_cnv_references:Start")
flog.info(paste("Reference data size: Cells=",
ncol(ref_data),
"Genes=",
nrow(ref_data),
sep=" "))
number_references <- ncol(ref_data)
reference_ylab <- NA
reference_data_file <- paste(file_base_name, sprintf("%s.references.txt", output_filename_prefix), sep=.Platform$file.sep)
ref_seps <- c()
# Handle reference groups
# If there is more than one reference group, visually break
# up the groups with a row seperator. Also plot the rows in
# order so the current groups are shown and seperated.
ordered_names <- c()
if (all(name_ref_groups %in% infercnv_obj@tumor_subclusters$subclusters) || !is.null(infercnv_obj@tumor_subclusters$hc[["all_references"]])) {
if (cluster_references) {
split_groups <- c()
for (i in seq_along(name_ref_groups)) {
if (!is.null(infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]])) {
ordered_names <- c(ordered_names, infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]]$labels[infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]]$order])
ref_seps <- c(ref_seps, length(ordered_names))
split_groups <- c(split_groups, rep(i, length(infercnv_obj@tumor_subclusters$hc[[ name_ref_groups[i] ]]$order)))
}
else { ## should only happen if there is only 1 cell in the group so the clustering method was not able to generate a hclust
#### actually happens with 2 cells only too, because can't cluster 2
if ((length(unlist(infercnv_obj@tumor_subclusters$subclusters[[name_ref_groups[i]]])) == 2) || (length(unlist(infercnv_obj@tumor_subclusters$subclusters[[name_ref_groups[i]]])) == 1)) {
ordered_names <- c(ordered_names, colnames(infercnv_obj@expr.data[, unlist(infercnv_obj@tumor_subclusters$subclusters[[ name_ref_groups[i] ]]), drop=FALSE]))
ref_seps <- c(ref_seps, length(ordered_names))
split_groups <- c(split_groups, rep(i, length(length(unlist(infercnv_obj@tumor_subclusters$subclusters[[name_ref_groups[i]]])))))
}
else {
flog.error("Unexpected error, should not happen.")
stop("Error")
}
}
}
}
else {
ordered_names <- infercnv_obj@tumor_subclusters$hc[["all_references"]]$labels[infercnv_obj@tumor_subclusters$hc[["all_references"]]$order]
split_groups <- rep(1, length(ordered_names))
}
ref_data <- ref_data[, ordered_names, drop=FALSE]
}
else {
if(length(ref_groups) > 1){
if (cluster_references) {
order_idx <- lapply(ref_groups, function(ref_grp) {
if (cluster_references && length(ref_grp) > 2) {
ref_hcl <- hclust(dist(t(ref_data[, ref_grp])), method=hclust_method)
ref_grp <- ref_grp[ref_hcl$order]
}
ref_grp
})
ref_seps <- head(cumsum(lengths(order_idx)), -1)
split_groups <- unlist(mapply(rep, seq_along(order_idx), lengths(order_idx)))
order_idx <- unlist(order_idx)
}
else {
ref_seps <- head(cumsum(lengths(ref_groups)), -1)
split_groups <- unlist(mapply(rep, seq_along(ref_groups), lengths(ref_groups)))
order_idx <- unlist(ref_groups)
}
}
else {
if (cluster_references) {
ref_hcl <- hclust(dist(t(ref_data)), method=hclust_method) # all ref_data is part of the only group
# order_idx <- unlist(ref_groups)[ref_hcl$order]
order_idx = ref_hcl$order # ref_data has been reindexed beforehand in the calling method
}
else {
order_idx = unlist(ref_groups)
}
split_groups <- rep(1, length(ref_groups[[1]]))
}
ref_data <- ref_data[, order_idx, drop=FALSE]
}
# Handle only one reference
# heatmap3 requires a 2 x 2 matrix, so with one reference
# I just duplicate the row and hid the second name so it
# visually looks like it is just taking up the full realestate.
if(number_references == 1){
ref_data <- cbind(ref_data, ref_data)
names(ref_data) <- c("",names(ref_data)[1])
colnames(ref_data) <- c("", colnames(ref_data)[1])
split_groups <- rep(1, 2)
}
# Make row color column colors from groupings
flog.info(paste("plot_cnv_references:Number reference groups=",
length(ref_groups)),
sep=" ")
# Transpose data.
ref_data <- t(ref_data)
# if (cluster_references) {
# if (number_references > 1) {
# for (i in seq_len(length(ref_groups))) {
# ref_hcl <- hclust(dist(ref_data[ref_groups[[i]], ]), method=hclust_method)
# ref_data[ref_groups[[i]], ] <- ref_data[ref_groups[[i]][ref_hcl$order], , drop=FALSE]
# }
# }
# }
# Remove labels if too many.
ref_orig_names <- row.names(ref_data)
# if (number_references > 20){
# The reference labs can become clustered
# Dynamically change labels given a certain number of labels.
# remove labels from plot even if less than 20 cells, because it changes the size of the heatmap
reference_ylab <- cnv_ref_title
if(number_references == 1) {
row.names(ref_data) = rep("", 2)
}
else {
row.names(ref_data) <- rep("", number_references)
}
# }
row_groupings <- as.matrix(get_group_color_palette()(length(table(split_groups)))[split_groups])
annotations_legend <- cbind(name_ref_groups, get_group_color_palette()(length(name_ref_groups)))
# Generate the Sep list for heatmap.3
contigSepList <- create_sep_list(row_count=nrow(ref_data),
col_count=ncol(ref_data),
row_seps=ref_seps,
col_seps=contig_seps)
# Print controls
flog.info("plot_cnv_references:Plotting heatmap.")
if (do_plot) {
data_references <- heatmap.cnv(ref_data,
main=NULL, #NA,
ylab=reference_ylab,
xlab=NULL, #NA,
key=FALSE,
labCol=rep("", nrow(ref_data)),
notecol="black",
trace="none",
dendrogram="none",
Colv=FALSE,
Rowv=FALSE,
cexRow=0.4,
breaks=breaksList,
gene_position_breaks=gene_position_breaks,
scale="none",
x.center=x.center,
color.FUN=col_pal,
sepList=contigSepList,
# Row colors and legend
RowIndividualColors=row_groupings,
annotations_legend=annotations_legend,
grouping_key_coln=grouping_key_coln,
# Seperate by contigs
sep.color=c("black","black"),
sep.lty=1,
sep.lwd=1,
if.plot=!testing,
# Layout
force_lmat=layout_lmat,
force_lwid=layout_lwid,
force_lhei=layout_lhei,
force_add=layout_add,
useRaster=useRaster)
}
# Write data to file
if ("matrix" %in% is(ref_data)) {
## TODO: write files for dgcMatrix too.
row.names(ref_data) <- ref_orig_names
flog.info(paste("plot_cnv_references:Writing reference data to",
reference_data_file,
sep=" "))
## Rowv is FALSE, Colv is FALSE
if (do_plot) {
write.table(t(ref_data[data_references$rowInd,data_references$colInd]),
file=reference_data_file)
}
else {
# colInd = seq_len(ncol(ref_data)) == everything in normal order
write.table(t(ref_data[rev(seq_len(nrow(ref_data))), ]),
file=reference_data_file)
}
}
}
#########################################
# Plotting from GMD
###################
###
# This package relies on heatmap.3 from the library GMD
# This code is a modified version of heatmap.3, some changes
# were required for our plotting.
# The heatmap.cnv function should be considered a modification
# of th GMD library function heatmap.3, all credit goes to
# their authors.
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## A copy of gtools::invalid
##
## see \code{invalid} in package:gtools for details
## Test if a value is missing, empty, or contains only NA or NULL values
## param: x value to be tested
.invalid <-
function(x)
{
if (missing(x) || is.null(x) || length(x) == 0)
return(TRUE)
if (is.list(x))
return(all(sapply(x, .invalid)))
else if (is.vector(x))
return(all(is.na(x)))
else return(FALSE)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
.is.grouped <-
function(x)
{
x <- as.character(x)
x.f <- factor(x, levels=unique(x), ordered=TRUE)
identical(as.character(sort(x.f)), x)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Call a function with arguments
##
## Call a function with arguments
## FUN function or function name
## ... unnameed function arguments
## MoreArgs named (or unnameed) function arguments
.call.FUN <-
function(FUN,...,MoreArgs)
{
FUN <- match.fun(FUN)
tmp.MoreArgs <- list(...)
if (!.invalid(MoreArgs)){
if (length(MoreArgs)>=1) {
for (i in seq_along(MoreArgs)) tmp.MoreArgs[[names(MoreArgs)[i]]] <- MoreArgs[[i]]
}
}
ret <- do.call(FUN, tmp.MoreArgs)
if ("call" %in% names(ret)){
ret$call <- match.call()
}
if ("call" %in% names(attributes(ret))){
attr(ret,"call") <- match.call()
}
return(ret)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Scale values to make them follow Standard Normal Distribution
##
## Scale values to make them follow Standard Normal Distribution
## param x numeric
## param scale character, indicating the type to scale.
## param na.rm logical
## return an object with the same dimention of `x'.
.scale.data <-
function(x,scale,na.rm=TRUE)
{
if(scale=="row"){
x <- sweep(x,1,rowMeans(x,na.rm=na.rm),FUN="-")
sx <- apply(x,1,sd,na.rm=na.rm)
x <- sweep(x,1,sx,FUN="/")
} else if(scale=="column"){
x <- sweep(x,2,colMeans(x,na.rm=na.rm),FUN="-")
sx <- apply(x,2,sd,na.rm=na.rm)
x <- sweep(x,2,sx,,FUN="/")
}
x
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's author's.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Scale values to a new range: c(low, high)
## x numeric
## low numeric, lower bound of target values
## high numeric, higher bound of target values
## return an object with the same dimention of `x'.
.scale.x <-
function(x,low=0,high=1,na.rm=TRUE)
{
if(identical(max(x,na.rm=na.rm),min(x,na.rm=na.rm))) NA
a <- 1/(max(x)-min(x))
b <- -min(x)/(max(x)-min(x))
a*x+b
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's author's.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Plot text
##
## Plot text
## x character, text to plot
## cex
## forecolor color of foreground
## bg color of background
## bordercolor color of border
## axes as in \code{graphics:::plot}
## ... additional arguments for \code{graphics:::text}
.plot.text <- function(x,xlim=c(0,1),ylim=c(0,1),cex=1,forecolor=par("fg"),bg=par("bg"),bordercolor=NA,axes=FALSE,...) {
if (.invalid(x)){
x <- NULL
}
if (is.null(x)){
x <- ""
} else if (is.na(x)){
x <- 'NA'
}
plot(xlim,ylim,type="n",ylab="",xlab="",xaxt="n",yaxt="n",axes=axes)
rect(xleft=0, ybottom=0, xright=1, ytop=1, col=bg, border=bordercolor)
text(0.5,0.5,x,cex=cex,...)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's author's.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## This was originally heatmap.3.
heatmap.cnv <-
function(x,
## whether a dissimilarity matrix
diss=inherits(x,"dist"),
## dendrogram control
Rowv=TRUE,
Colv=TRUE,
dendrogram=c("both","row","column","none"),
## dist object
dist.row,
dist.col,
dist.FUN=gdist,
dist.FUN.MoreArgs=list(method="euclidean"),
## hclust object
hclust.row,
hclust.col,
hclust.FUN=hclust,
hclust.FUN.MoreArgs=list(method="ward.D"),
## data scaling
scale=c("none","row","column"),
na.rm=TRUE,
## clustering control
cluster.by.row=FALSE,
cluster.by.col=FALSE,
kr=NA,
kc=NA,
row.clusters=NA,
col.clusters=NA,
## image plot
revR=FALSE,
revC=FALSE,
add.expr,
## mapping data to colors
breaks,
gene_position_breaks=NULL,
## centering colors to a value
x.center,
## colors
color.FUN=gplots::bluered,
##
## block sepration
sepList=list(NULL,NULL),
sep.color=c("gray45","gray45"),
sep.lty=1,
sep.lwd=2,
## cell labeling
cellnote,
cex.note=1.0,
notecol="cyan",
na.color=par("bg"),
## level trace
trace=c("none","column","row","both"),
tracecol="cyan",
hline,
vline,
linecol=tracecol,
## Row/Column Labeling
labRow=TRUE, ## shown by default
labCol=TRUE, ## shown by default
srtRow=NULL,
srtCol=NULL,
sideRow=4,
sideCol=1,
##
margin.for.labRow,
margin.for.labCol,
ColIndividualColors,
RowIndividualColors,
annotations_legend,
grouping_key_coln,
cexRow,
cexCol,
cexAt=NULL,
labRow.by.group=FALSE,
labCol.by.group=FALSE,
## plot color key + density info
key=TRUE,
key.title="Color Key",
key.xlab="Value",
key.ylab="Count",
keysize=1.5,
mapsize=9,
mapratio=4/3,
sidesize=3,
cex.key.main=0.75,
cex.key.xlab=0.75,
cex.key.ylab=0.75,
density.info=c("histogram","density","none"),
denscol=tracecol,
densadj=0.25,
## plot titles/labels
main="Heatmap",
sub="",
xlab="",
ylab="",
cex.main=2,
cex.sub=1.5,
font.main=2,
font.sub=3,
adj.main=0.5,
mgp.main=c(1.5,0.5,0),
mar.main=3,
mar.sub=3,
## plot ##
if.plot=TRUE,
## plot of partition (left/top of heatmap)
plot.row.partition=FALSE,
plot.col.partition=FALSE,
cex.partition=1.25,
color.partition.box="gray45",
color.partition.border="#FFFFFF",
## plot of summary (right/bottom of heatmap)
plot.row.individuals=FALSE,
plot.col.individuals=FALSE,
plot.row.clusters=FALSE,
plot.col.clusters=FALSE,
plot.row.clustering=FALSE,
plot.col.clustering=FALSE,
##
plot.row.individuals.list=FALSE,
plot.col.individuals.list=FALSE,
plot.row.clusters.list=FALSE,
plot.col.clusters.list=FALSE,
plot.row.clustering.list=FALSE,
plot.col.clustering.list=FALSE,
## for plot of clusters - row
row.data=FALSE,
## for plot of clusters - col
col.data=FALSE,
##
if.plot.info=FALSE,
text.box,
cex.text=1.0,
## Force in layout info
force_lmat=NULL,
force_lwid=NULL,
force_lhei=NULL,
force_add=FALSE,
useRaster=TRUE,
## extras
...
)
{
## check input - take1 ##
if (! is.matrix(x)) {
x <- as.matrix(x)
}
x.ori <- x
if(!inherits(x,"dist") & !is.matrix(x)) {
stop("`x' should either be a matrix, a data.frame or a `dist' object.")
}
if (! sideRow %in% c(2,4)) {
stop('sideRow must be either 2 or 4.')
}
if (! sideCol %in% c(1,3)) {
stop('sideCol must be either 1 or 3.')
}
## store input
Rowv.ori <- Rowv
Colv.ori <- Colv
## check
dendrogram <- match.arg(dendrogram)
if ( (dendrogram %in% c("both","row")) & !inherits(Rowv,"dendrogram") ) {
warning("Discrepancy: row dendrogram is asked; Rowv is set to `TRUE'.")
Rowv <- TRUE
}
if ( (dendrogram %in% c("both","col")) & !inherits(Colv,"dendrogram") ) {
warning("Discrepancy: col dendrogram is asked; Colv is set to `TRUE'.")
Colv <- TRUE
}
if (identical(Rowv, FALSE) | missing(Rowv)) {
if(!identical(cluster.by.row,FALSE)) {
warning("Discrepancy: No row dendrogram is asked; cluster.by.row is set to `FALSE'.")
cluster.by.row <- FALSE
}
} else {
if(!identical(cluster.by.row,TRUE)) {
warning("Discrepancy: row dendrogram is asked; cluster.by.row is set to `TRUE'.")
cluster.by.row <- TRUE
}
}
if (identical(Colv, FALSE) | .invalid(Colv)) {
if(!identical(cluster.by.col,FALSE)) {
warning("Discrepancy: No col dendrogram is asked; cluster.by.col is set to `FALSE'.")
cluster.by.col <- FALSE
}
} else {
if(!identical(cluster.by.col,TRUE)) {
warning("Discrepancy: col dendrogram is asked; cluster.by.col is set to `TRUE'.")
cluster.by.col <- TRUE
}
}
if (!.invalid(kr)) {
if (is.numeric(kr)) {
if(!plot.row.partition) {
warning("Discrepancy: kr is set, therefore plot.row.partition is set to `TRUE'.")
plot.row.partition <- TRUE
}
}
}
if (!.invalid(kc)) {
if (is.numeric(kc)) {
if(!plot.col.partition) {
warning("Discrepancy: kc is set, therefore plot.col.partition is set to `TRUE'.")
plot.col.partition <- TRUE
}
}
}
symm <- isSymmetric(x)
## check input - take2: di ##
di <- dim(x)
if(length(di)!=2 || !is.numeric(x)){
stop("`x' should only contain `numeric' values and can be converted to a 2-D matrix.")
}
## parse param ##
scale <- if(symm && .invalid(scale)) "none" else match.arg(scale) ## no scale on symmetric matrix
trace <- match.arg(trace)
density.info <- match.arg(density.info)
dist.FUN <- match.fun(dist.FUN)
hclust.FUN <- match.fun(hclust.FUN)
color.FUN <- match.fun(color.FUN)
## NG if both breaks and scale are specified ##
if(!.invalid(breaks) & (scale!="none")) {
warning("Using scale=\"row\" or scale=\"column\" when breaks are",
"specified can produce unpredictable results.",
"Please consider using only one or the other.")
}
## nr and nc ##
nr <- di[1]
nc <- di[2]
## check input - take3: nr,nc ##
if(nr <=1 || nc <=1)
stop("`x' must have at least 2 rows and 2 columns")
## font size of row/col labels ##
cexRow0 <- 0.2+1/log10(nr)
cexCol0 <- 0.2+1/log10(nc)
if (.invalid(cexRow)) {
cexRow <- cexRow0
} else {
# message('heatmap.3 | From GMD 0.3.3, please use relative values for cexRow.')
cexRow <- cexRow0*cexRow
}
if (.invalid(cexCol)) {
cexCol <- cexCol0
} else {
# message('heatmap.3 | From GMD 0.3.3, please use relative values for cexCol.')
cexCol <- cexCol0*cexCol
}
## cellnote ##
## ##if(.invalid(cellnote)) cellnote <- matrix("",ncol=ncol(x),nrow=nrow(x))
## ------------------------------------------------------------------------
## parse dendrogram ##
## ------------------------------------------------------------------------
if (missing(Rowv)) Rowv <- FALSE
if (.invalid(Colv)) Colv <- if(symm) Rowv else FALSE
if (Colv=="Rowv") {
if ((!isTRUE(Rowv) | !symm) ){
Colv <- FALSE
warning("`Colv' is specified to use \"Rowv\", but either `Rowv' is invalid or `x' is not symmetric; Colv is suppressed.")
} else{
Colv <- Rowv
}
}
## ------------------------------------------------------------------------
## generate hclust.obj - row/col
## ------------------------------------------------------------------------
flush.console()
if ( !inherits(Colv,"dendrogram") & !identical(Colv,FALSE) | (cluster.by.col & .invalid(col.clusters))) {
if (.invalid(hclust.col)){
hclust.col <- .call.FUN(hclust.FUN,dist.col,MoreArgs=hclust.FUN.MoreArgs)
} else {
if (length(hclust.col$order) != nc) {
stop("`hclust.col' should have equal size as the cols.")
}
}
} else {
hclust.col <- NULL
}
## ------------------------------------------------------------------------
## generate hclust.obj - row/col
## ------------------------------------------------------------------------
ddr <- ddc <- NULL
## get the dendrograms and reordering row/column indices - row ##
if(inherits(Rowv,"dendrogram")) {
if (attr(Rowv,"members") != nr) {
stop("`Rowv' should contain equal size of members as the rows.")
}
ddr <- Rowv ## use Rowv 'as-is',when it is dendrogram
# rowInd <- order.dendrogram(ddr)
rowInd <- seq_len(nr) ### data has already been sorted in the proper order, and coloring vectors too
} else{
rowInd <- nr:1 ## from bottom.
}
## get the dendrograms and reordering row/column indices - col ##
if(inherits(Colv,"dendrogram")) {
if (attr(Colv,"members") != nc) {
stop("`Colv' should contain equal size of members as the cols.")
}
ddc <- Colv ## use Colv 'as-is',when it is dendrogram
colInd <- order.dendrogram(ddc)
} else if(identical(Colv, "Rowv")) {
if(exists("ddr")){
ddc <- ddr
colInd <- order.dendrogram(ddc)
} else{
colInd <- rowInd
}
} else if(is.integer(Colv)) { ## compute dendrogram and do reordering based on given vector
ddc <- as.dendrogram(hclust.col)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if(nc != length(colInd)) {
stop("`colInd' is of wrong length.")
}
} else if (isTRUE(Colv)) { ## if TRUE,compute dendrogram and do reordering based on rowMeans
Colv <- colMeans(x, na.rm=TRUE)
ddc <- as.dendrogram(hclust.col)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if(nc !=length(colInd))
stop("`colInd' is of wrong length.")
} else {
colInd <- seq_len(nc) ## from left
}
## ------------------------------------------------------------------------
## check consistency
## ------------------------------------------------------------------------
## Xmisc::logme(dendrogram)
## Xmisc::logme(Colv)
## Xmisc::logme(Rowv)
## dendrogram - check consistency: Rowv ##
if ( is.null(ddr) & (dendrogram %in% c("both","row"))){
warning("Discrepancy: Rowv is invalid or FALSE, while dendrogram is `",
dendrogram,"'. Omitting row dendogram.")
if (is.logical(Colv) & (Colv.ori) & dendrogram=="both") {
dendrogram <- "column"
}
else {
dendrogram <- "none"
}
}
## dendrogram - check consistency: Colv ##
if ( is.null(ddc) & (dendrogram %in% c("both","column")) ) {
warning("Discrepancy: Colv is invalid or FALSE, while dendrogram is `",
dendrogram,"'. Omitting column dendogram.")
if (is.logical(Rowv) & (identical(Rowv.ori,TRUE) | is.numeric(Rowv.ori) | inherits(Rowv.ori,"dendrogram")) & dendrogram=="both") {
dendrogram <- "row"
}
else {
dendrogram <- "none"
}
}
## check consistency
if (is.null(ddr)){
if(isTRUE(cluster.by.row) | isTRUE(plot.row.partition) | isTRUE(plot.row.clusters) | isTRUE(plot.row.clustering) ){
warning("Using invalid `Rowv' while allowing",
"`cluster.by.row' or `plot.row.partition' or `plot.row.clusters' or `plot.row.clustering'",
"can produce unpredictable results; Forced to be disabled.")
}
}
if (is.null(ddc)){
if(isTRUE(cluster.by.col) | isTRUE(plot.col.partition) | isTRUE(plot.col.clusters) | isTRUE(plot.col.clustering) ){
warning("Using invalid `Colv' while allowing",
"`cluster.by.col' or `plot.col.partition' or `plot.col.clusters' or `plot.col.clustering'",
"can produce unpredictable results; Forced to be disabled.")
}
}
if (is.null(ddr)) cluster.by.row <- plot.row.partition <- plot.row.clusters <- plot.row.clustering <- FALSE
if (is.null(ddc)) cluster.by.col <- plot.col.partition <- plot.col.clusters <- plot.col.clustering <- FALSE
## ------------------------------------------------------------------------
## Reordering
## ------------------------------------------------------------------------
flush.console()
## reorder x and cellnote ##
# rowInd and colInd should both be seq_len
x <- x[rowInd,colInd,drop=FALSE]
if (!.invalid(cellnote)) {
cellnote <- cellnote[rowInd,colInd,drop=FALSE ]
}
## reorder labels - row ##
if(identical(labRow,TRUE)) { ## Note: x is already reorderred
labRow <- if (is.null(rownames(x))) (seq_len(nr))[rowInd] else rownames(x)
} else if(identical(labRow,FALSE) | .invalid(labRow)) {
labRow <- rep("",nrow(x))
} else if(is.character(labRow)) {
labRow <- labRow[rowInd]
}
## reorder cellnote/labels - col ##
if (identical(labCol,TRUE)) {
labCol <- if(is.null(colnames(x))) (seq_len(nc))[colInd] else colnames(x)
} else if(identical(labCol,FALSE) | .invalid(labCol)) {
labCol <- rep("",ncol(x))
} #else if(is.character(labCol)){
#labCol <- labCol[colInd]
#}
## ------------------------------------------------------------------------
## scale
## center to 0 and scale to 1 in row or col but not both! ##
## ------------------------------------------------------------------------
flush.console()
x <- .scale.data(x,scale,na.rm)
## ------------------------------------------------------------------------
## labels for observations/clusters/
## ------------------------------------------------------------------------
## margin for labels
margin.for.labRow0 <- max(nchar(labRow, keepNA = FALSE)) * 0.75 + 0.2
margin.for.labCol0 <- max(nchar(labCol, keepNA = FALSE)) * 0.75 + 0.2 ## Change compared to GMD to work with R 3.3+ that changed the default value of keepNA to "NA"
if (.invalid(margin.for.labRow)) {
margin.for.labRow <- margin.for.labRow0
}
#} else {
# message('heatmap.3 | From GMD 0.3.3, please use relative values for margin.for.labRow.')
# margin.for.labRow <- margin.for.labRow0*margin.for.labRow
#}
if (margin.for.labRow < 2) {
margin.for.labRow <- 2
}
# if (.invalid(margin.for.labCol)){
margin.for.labCol <- margin.for.labCol0
# }
# else {
# # message('heatmap.3 | From GMD 0.3.3, please use relative values for margin.for.labCol.')
# margin.for.labCol <- margin.for.labCol0*margin.for.labCol
# }
if (margin.for.labCol < 2) {
margin.for.labCol <- 2
}
## group unique labels - row ## ##??check
if (!.invalid(labRow.by.group) & !identical(labRow.by.group,FALSE)) {
group.value <- unique(labRow)
group.index <- sapply(group.value, function(x,y) min(which(y==x)), y=labRow)
labRow <- rep("",length(labRow))
labRow[group.index] <- group.value
}
## group unique labels - col ## ##??check
if (!.invalid(labCol.by.group) & !identical(labCol.by.group,FALSE)) {
group.value <- unique(labCol)
group.index <- sapply(group.value, function(x,y) min(which(y==x)), y=labCol)
labCol <- rep("",length(labCol))
labCol[group.index] <- group.value
}
## ------------------------------------------------------------------------
## color breaks
## ------------------------------------------------------------------------
flush.console()
## set breaks for binning x into colors ##
if(.invalid(breaks)) {
breaks <- 16
} else {
flog.debug(paste("inferCNV::heatmap.cnv, breaks parameter set to: [", paste(breaks, collapse=","), "]", sep=""))
}
## get x.range according to the value of x.center ##
if (!.invalid(x.center)) { ## enhanced
if (is.numeric(x.center)) {
x.range.old <- range(x,na.rm=TRUE)
if (length(breaks) > 1) {
# important, use specified breakpoint info here if set by user
x.range.old = range(breaks)
}
dist.to.x.center <- max(abs(x.range.old-x.center))
x.range <- c(x.center-dist.to.x.center,x.center+dist.to.x.center)
if (length(breaks) > 1) {
# re-set the breaks according to the new x.range
breaks=seq(x.range[1], x.range[2], length=16)
flog.debug(paste("inferCNV::heatmap.cnv, resetting breaks to adjusted x.range: [",
paste(breaks, collapse=","), "]", sep=""))
}
} else {
stop("`x.center' should be numeric.")
}
} else {
x.range <- range(x,na.rm=TRUE)
if (length(breaks) > 1) {
# important, use specified breakpoint info here if set by user
x.range = range(breaks)
}
}
flog.debug( paste("inferCNV::heatmap.cnv x range set to: ",
paste(x.range, collapse=",")), sep="" )
## set breaks for centering colors to the value of x.center ##
if(length(breaks)==1) {
breaks <-
seq(min(min(x,na.rm=TRUE), x.range[1]),
max(max(x,na.rm=TRUE), x.range[2]),
length.out=breaks)
}
## count of breaks and colors ##
nbr <- length(breaks)
ncolor <- length(breaks) - 1
## generate colors ##
colors <- color.FUN(ncolor)
## set up breaks and force values outside the range into the endmost bins ##
min.breaks <- min(breaks)
max.breaks <- max(breaks)
x[] <- ifelse(x<min.breaks, min.breaks, x)
x[] <- ifelse(x>max.breaks, max.breaks, x)
## ------------------------------------------------------------------------
## Plotting
## ------------------------------------------------------------------------
if (if.plot){
ir <- length(plot.row.individuals.list)
ic <- length(plot.col.individuals.list)
cr <- length(plot.row.clustering.list)
cc <- length(plot.col.clustering.list)
flush.console()
if(mapratio<=1){
sr <- 12
sc <- sr*mapratio
} else {
sc <- 12
sr <- sc/mapratio
}
###############################
## calculate the plot layout ##
###############################
## 1) for heatmap
lmat <- matrix(1, nrow=sr, ncol=sc)
lwid <- c(rep(mapsize/sc, sc))
lhei <- c(rep(mapsize/mapratio/sr, sr))
## 2) row.clusters
if (plot.row.partition | plot.row.clusters){
lmat <- cbind(max(lmat, na.rm=TRUE)+1, lmat)
lwid <- c(0.3, lwid)
} else {
lmat <- cbind(NA, lmat)
lwid <- c(0.02, lwid)
}
## 3) col.clusters
if (plot.col.partition | plot.col.clusters){
lmat <- rbind(c(NA, rep(max(lmat, na.rm=TRUE)+1,sc)), lmat)
lhei <- c(0.3/mapratio, lhei)
} else {
lmat <- rbind(NA, lmat)
lhei <- c(0.02/mapratio, lhei)
}
if(!.invalid(RowIndividualColors)) { ## 4) add middle column to layout for vertical sidebar ##??check
# if(!is.character(RowIndividualColors) || length(RowIndividualColors) !=nr)
if(!is.character(RowIndividualColors) || dim(RowIndividualColors)[1] !=nr) {
stop("'RowIndividualColors' must be a character vector of length nrow(x)")
}
lmat <- cbind(c(rep(NA,nrow(lmat)-sr),rep(max(lmat,na.rm=TRUE)+1,sr)), lmat)
lwid <- c(0.2, lwid)
lmat <- cbind(c(rep(NA,nrow(lmat)-sr),rep(max(lmat,na.rm=TRUE)+1,sr)), lmat) # not really useful as this if is only true in plot_cnv_observations which forces layout_lmat and layout_lhei
lwid <- c(0.2, lwid) # layout_lwid is however not forced, so need to actually update it here
} else {
lmat <- cbind(NA, lmat)
lwid <- c(0.02, lwid)
}
if(!.invalid(ColIndividualColors)) { ## 5) add middle row to layout for horizontal sidebar ##??check
if(!is.character(ColIndividualColors) || length(ColIndividualColors) !=nc) {
stop("'ColIndividualColors' must be a character vector of length ncol(x)")
}
lmat <- rbind(c(rep(NA,ncol(lmat)-sc), rep(max(lmat,na.rm=TRUE)+1,sc)), lmat)
lhei <- c(0.2/mapratio, lhei)
} else {
lmat <- rbind(NA, lmat)
lhei <- c(0.02/mapratio, lhei)
}
## 6) for row-dend
lmat <- cbind(c(rep(NA, nrow(lmat)-sr),
rep(max(lmat, na.rm=TRUE)+1, sr)),
lmat
)
lwid <- c(keysize,lwid)
## 7) for col-dend, 8) for key
lmat <- rbind(c(
max(lmat, na.rm=TRUE)+2,
rep(NA, ncol(lmat)-sc-1),
rep(max(lmat, na.rm=TRUE)+1, sc)
),
lmat
)
lhei <- c(keysize/mapratio,lhei)
## text.box##
## numbered 999 ##
## 9) for RowPlot (from bottom)
if(.invalid(text.box)){
text.box <- "made by\nFunction: heatmap.3\nPackage: GMD\nin R"
}
if(plot.row.individuals) { ## enhanced: add right column to layout for plots
lmat <- cbind(lmat,
c(rep((1+max(lmat, na.rm=TRUE)), nrow(lmat)-sr),# text
rep((ir:1)+max(lmat, na.rm=TRUE)+(1), each=floor(sr/ir)), rep(NA,sr%%ir)
)
)
lwid <- c(lwid,sidesize)
} else {
lmat <- cbind(lmat,c(rep(NA,nrow(lmat))))
lwid <- c(lwid,0.01)
}
## 10) for ColPlot from right
if(plot.col.individuals) { ## enhanced: add bottom row to layout for plots
lmat <- rbind(lmat,
c(rep((1+max(lmat, na.rm=TRUE)), ncol(lmat)-sc-1),# text
rep((seq_len(ic))+max(lmat, na.rm=TRUE)+(1), each=floor(sc/ic)), rep(NA,sc%%ic),
##NA # change to numeric if text.box
999
)
)
lhei <- c(lhei,sidesize/mapratio)
} else {
lmat <- rbind(lmat,c(rep(NA,ncol(lmat))))
lhei <- c(lhei,0.01/mapratio)
}
## 11) for RowPlot (from bottom)
if(plot.row.clusters) { ## enhanced: add right column to layout for plots
lmat <- cbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)), nrow(lmat)-sr-1), # text
rep((kr:1)+max(lmat[lmat!=999], na.rm=TRUE)+(1), each=floor(sr/kr)), rep(NA,sr%%kr),
##NA
999
)
)
lwid <- c(lwid,sidesize)
} else {
lmat <- cbind(lmat,c(rep(NA,nrow(lmat))))
lwid <- c(lwid,0.01)
}
## 12) for ColPlot from right
if(plot.col.clusters) { ## enhanced: add bottom row to layout for plots
lmat <- rbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)), ncol(lmat)-sc-2),# text
rep((seq_len(kc))+max(lmat[lmat!=999], na.rm=TRUE)+(1), each=floor(sc/kc)), rep(NA,sc%%kc),
##NA,NA # change to numeric if text.box
999,999
)
)
lhei <- c(lhei,sidesize/mapratio)
} else {
lmat <- rbind(lmat, c(rep(NA, ncol(lmat))))
lhei <- c(lhei,0.01/mapratio)
}
## 13) for RowPlot (from bottom)
if(plot.row.clustering) { ## enhanced: add right column to layout for plots
lmat <- cbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)), nrow(lmat)-sr-2), # text
rep(c((cr:1)+max(lmat[lmat!=999], na.rm=TRUE)+(1)), each=floor(sr/cr)), rep(NA,sr%%cr),
##NA,NA
999, 999
)
)
lwid <- c(lwid,sidesize)
} else {
lmat <- cbind(lmat,c(rep(NA,nrow(lmat))))
lwid <- c(lwid,0.01)
}
## 14) for ColPlot from right
if(plot.col.clustering) { ## enhanced: add bottom row to layout for plots
lmat <- rbind(lmat,
c(rep((1+max(lmat[lmat!=999], na.rm=TRUE)),ncol(lmat)-sc-3),# text
rep((seq_len(cc))+max(lmat[lmat!=999], na.rm=TRUE)+(1), each=floor(sc/cc)), rep(NA,sc%%cc),
##NA,NA,NA # change to numeric if text.box
999, 999, 999
)
)
lhei <- c(lhei,sidesize/mapratio)
} else {
lmat <- rbind(lmat,c(rep(NA,ncol(lmat))))
lhei <- c(lhei,0.01/mapratio)
}
lmat[is.na(lmat)] <- 0
if (any(lmat==999)) {
flag.text <- TRUE
}
else {
flag.text <- FALSE
}
lmat[lmat==999] <- max(lmat[lmat!=999]) + 1
##------------------------------- Overriding the lmat settings if already given.
## Graphics `output' ##
## layout
if(!is.null(force_lmat)) {
lmat <- force_lmat
}
if(!is.null(force_lwid)) {
lwid <- force_lwid
}
if(!is.null(force_lhei)) {
lhei <- force_lhei
}
if(!force_add) {
layout(lmat, widths=lwid, heights=lhei, respect=FALSE)
}
##---------------------------------
## reverse columns
if(revC) { ## x columns reversed
iy <- nr:1
ddc <- rev(ddc)
x <- x[iy,]
if (!.invalid(cellnote)) {
cellnote <- cellnote[iy,]
}
} else {
iy <- seq_len(nr)
}
## reverse rows
if(revR){ ## x columns reversed
ix <- nc:1
ddr <- rev(ddr)
x <- x[,ix]
if (!.invalid(cellnote)) {
cellnote <- cellnote[,ix]
}
} else {
ix <- seq_len(nc)
}
## 1) draw the main carpet/heatmap
margins <- c(margin.for.labCol, 0, 0, margin.for.labRow)
mgp <- c(3, 1, 0)
par(mar=margins,mgp=mgp);outer=FALSE
x.save <- x
if(!symm || scale !="none") { ##??
x <- t(x)
if (!.invalid(cellnote)) {
cellnote <- t(cellnote)
}
}
if (!is.null(gene_position_breaks)) {
image(gene_position_breaks, seq_len(nr+1),
x,
xlim=0.5+c(0,max(gene_position_breaks)), ylim=0.5+c(0,nr),
axes=FALSE, xlab="", ylab="", col=colors, breaks=breaks, useRaster=FALSE,
...)
} else {
image(seq_len(nc), seq_len(nr),
x,
xlim=0.5+c(0,nc), ylim=0.5+c(0,nr),
axes=FALSE, xlab="", ylab="", col=colors, breaks=breaks, useRaster=useRaster,
...)
}
flog.info(paste("Colors for breaks: ", paste(colors, collapse=","), sep=" "))
flog.info(paste("Quantiles of plotted data range:", paste(quantile(x), collapse=","), sep=" "))
## plot/color NAs
if(!.invalid(na.color) & any(is.na(x))) {
mmat <- ifelse(is.na(x),1,NA)
if (!is.null(gene_position_breaks)) {
image(gene_position_breaks, seq_len(nr+1), mmat, axes=FALSE, xlab="", ylab="",
col=na.color, add=TRUE, useRaster=FALSE)
} else {
image(seq_len(nc),seq_len(nr), mmat, axes=FALSE, xlab="", ylab="",
col=na.color, add=TRUE, useRaster=useRaster)
}
}
##
## labCol (?)
if ((dendrogram %in% c("both","col")) & sideCol==3) {
warning("Discrepancy: col dendrogram is asked; srtCol is set to 1.")
sideCol <- 1
}
if (!length(srtCol)) {
if (is.null(cexAt)) {
cexAt = seq_along(labCol)
}
axis(sideCol,cexAt,labels=labCol,las=2,line=-0.5,tick=0,cex.axis=cexCol,outer=outer)
} else {
if (sideCol==1){
if (sideCol==1) .sideCol <- par("usr")[3]-0.5*srtCol/90 else .sideCol <- par("usr")[4]+0.5*srtCol/90
text(seq_len(nc),.sideCol,labels=labCol,srt=srtCol,pos=1,xpd=TRUE,cex=cexCol)
}
}
if(!.invalid(xlab)) {
mtext(xlab,side=1, line=margins[1]-1.25)
}
## labRow (?)
if ((dendrogram %in% c("both","row")) & sideRow==2) {
warning("Discrepancy: row dendrogram is asked; sideRow is set to 4.")
sideRow <- 4
}
if (!length(srtRow)) {
axis(sideRow,iy,labels=labRow,las=2,line=-0.5,tick=0,cex.axis=cexRow,outer=outer)
} else {
if (sideRow==4) {
if (sideRow==4) .sideRow <- par("usr")[2]+0.5*srtRow/90 else .sideRow <- par("usr")[1]-0.5*srtRow/90
text(.sideRow,iy, labels=labRow, srt=srtRow, pos=1, xpd=TRUE, cex=cexRow)
}
}
if(!.invalid(ylab)) mtext(ylab,side=4,line=margins[4]-1.25)
if (!.invalid(add.expr))
eval(substitute(add.expr))
## Enhanced: add 'sep.color' colored spaces to visually separate sections
if (plot.row.partition | plot.row.clusters) { ##??
plot.row.partitionList <- get.sep(clusters=row.clusters,type="row")
} else {
plot.row.partitionList <- NULL
}
if (plot.col.partition | plot.col.clusters) { ##??
plot.col.partitionList <- get.sep(clusters=col.clusters,type="column")
} else {
plot.col.partitionList <- NULL
}
row.sepList <- sepList[[1]]
if (!.invalid(row.sepList)) {
for (i in seq_along(row.sepList)) {
i.sep <- row.sepList[[i]]
rect(
xleft=i.sep[1]+0.5,
ybottom=i.sep[2]+0.5,
xright=i.sep[3]+0.5,
ytop=i.sep[4]+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=FALSE,
border=sep.color[1]
)
}
}
col.sepList <- sepList[[2]]
if (!.invalid(col.sepList)) {
for (i in seq_along(col.sepList)) {
i.sep <- col.sepList[[i]]
rect(
xleft=i.sep[1]+0.5,
ybottom=i.sep[2]+0.5,
xright=i.sep[3]+0.5,
ytop=i.sep[4]+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=FALSE,
border=sep.color[2]
)
}
}
## show traces
min.scale <- min(breaks)
max.scale <- max(breaks)
x.scaled <- .scale.x(t(x),min.scale,max.scale)
if(.invalid(hline)) hline=median(breaks)
if(.invalid(vline)) vline=median(breaks)
if(trace %in% c("both","column")) {
for( i in colInd ) {
if(!.invalid(vline)) {
vline.vals <- .scale.x(vline,min.scale,max.scale)
abline(v=i-0.5+vline.vals, col=linecol, lty=2)
}
xv <- rep(i, nrow(x.scaled)) + x.scaled[,i] - 0.5
xv <- c(xv[1], xv)
yv <- seq_along(xv)-0.5
lines(x=xv, y=yv, lwd=1, col=tracecol, type="s")
}
}
if(trace %in% c("both","row")) {
for( i in rowInd ) {
if(!.invalid(hline)) {
hline.vals <- .scale.x(hline, min.scale, max.scale)
abline(h=i+hline, col=linecol, lty=2)
}
yv <- rep(i,ncol(x.scaled)) + x.scaled[i,] - 0.5
yv <- rev(c(yv[1],yv))
xv <- length(yv):1-0.5
lines(x=xv, y=yv, lwd=1, col=tracecol, type="s")
}
}
## cellnote
if(!.invalid(cellnote)) {
text(x=c(row(cellnote)),
y=c(col(cellnote)),
labels=c(cellnote),
col=notecol,
cex=cex.note)
}
## 2) plot.row.partition
if(plot.row.partition |plot.row.clusters) { ##row.clusters
par(mar=c(margins[1], 0.5, 0, 0.1))
row.clusters.unique <- unique(row.clusters)
row.clusters.unique <- row.clusters.unique[!is.na(row.clusters.unique)]
image(rbind(seq_len(nr)),
xlim=0.5+c(0,1), ylim=0.5+c(0,nr),
col=par("bg"),
axes=FALSE, add=force_add, useRaster=useRaster)
if (!.invalid(plot.row.partitionList)) {
for (i in seq_along(plot.row.partitionList)) {
i.sep <- plot.row.partitionList[[i]]
rect(
xleft=0+0.5,
ybottom=i.sep[2]+0.5,
xright=1+0.5,
ytop=i.sep[4]+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=color.partition.box,
border=color.partition.border
)
g <- row.clusters.unique[i]
s <- g
text(x=1,y=(i.sep[2]+0.5+i.sep[4]+0.5)/2, labels=s, col=color.partition.border,
cex=cex.partition,
srt=90
)
}
}
}
## 3) plot.col.partition
if(plot.col.partition | plot.col.clusters) {
par(mar=c(0.1,0,0,margins[4]))
col.clusters.unique <- unique(col.clusters)
col.clusters.unique <- col.clusters.unique[!is.na(col.clusters.unique)]
if (!is.null(gene_position_breaks)) {
image(cbind(seq_len(nc)),
xlim=0.5+c(0,nc), ylim=0.5+c(0,1),
col=par("bg"),
axes=FALSE, add=force_add, useRaster=useRaster)
} else {
image(cbind(seq_len(nc)),
xlim=0.5+c(0,nc), ylim=0.5+c(0,1),
col=par("bg"),
axes=FALSE, add=force_add, useRaster=useRaster)
}
if (!.invalid(plot.col.partitionList)) {
for (i in seq_along(plot.col.partitionList)) {
i.sep <- plot.col.partitionList[[i]]
rect(
xleft=i.sep[1]+0.5,
ybottom=0+0.5,
xright=i.sep[3]+0.5,
ytop=1+0.5,
lty=sep.lty,
lwd=sep.lwd,
col=color.partition.box,
border=color.partition.border
)
g <- col.clusters.unique[i]
s <- g
text(x=(i.sep[1]+0.5+i.sep[3]+0.5)/2, y=1, labels=s, col=color.partition.border,
cex=cex.partition,
srt=0
)
}
}
}
## 4) draw the side color bars - for row
if(!.invalid(RowIndividualColors)) {
par(mar=c(margins[1],0,0,0.5))
# image(rbind(1:nr),col=RowIndividualColors[rowInd, 1],axes=FALSE,add=force_add)
image(rbind(seq_len(nr)),col=RowIndividualColors[rowInd, 1], axes=FALSE, add=FALSE, useRaster=useRaster)
if (dim(RowIndividualColors)[2] > 1) {
par(mar=c(margins[1],0,0,0.5))
image(rbind(seq_len(nr)),col=RowIndividualColors[rowInd, 2], axes=FALSE, add=force_add, useRaster=useRaster)
}
}
## 5) draw the side color bars - for col
if(!.invalid(ColIndividualColors)) {
par(mar=c(0.5,0,0,margins[4]))
if (!is.null(gene_position_breaks)) {
#image(cbind(gene_position_breaks), col=ColIndividualColors[colInd], axes=FALSE, add=force_add, useRaster=FALSE)
image(gene_position_breaks, 1, cbind(seq_len(nc)), col=ColIndividualColors[colInd], axes=FALSE, add=force_add, useRaster=FALSE)
} else {
image(cbind(seq_len(nc)), col=ColIndividualColors[colInd], axes=FALSE, add=force_add, useRaster=useRaster)
}
}
## 6) row-dend
par(mar=c(margins[1], 0, 0, 0))
if(dendrogram %in% c("both","row")) {
plot(ddr,horiz=TRUE,axes=FALSE,yaxs="i",leaflab="none")
} else {
.plot.text(ylim=range(iy))
if (sideRow==2) {
.sideRow <- par("usr")[2]-0.5*srtCol/90
text(.sideRow,iy,labels=labRow,srt=srtRow,pos=1,xpd=TRUE,cex=cexRow)
}
}
par(mar=c(0,0,0,0))
plot(1, type="n", axes=FALSE, xlab="", ylab="")
legend(x=0.6,y=1.2 ,legend=annotations_legend[,1],
cex=1.2,
fill=annotations_legend[,2],
ncol=grouping_key_coln)
## 7) col-dend and title
mar3 <- (if(!is.null(main)) mar.main else 0) +
(if(!is.null(sub)) mar.sub else 0)
par(mar=c(0,0,mar3,margins[4]))
if(dendrogram %in% c("both","column")) {
plot(ddc,axes=FALSE,xaxs="i",leaflab="none")
} else {
if(key) {
.plot.text(xlim=range(seq_len(nc)))
}
if (sideCol==3){
.sideCol <- par("usr")[3]+0.5*srtCol/90
text(seq_len(nc),.sideCol,labels=labCol,srt=srtCol,pos=1,xpd=TRUE,cex=cexCol)
}
}
## title
if (is.null(sub)) {
main.line <- 1
}
else {
main.line <- 3
}
if(!is.null(main)) {
title(main,cex.main=cex.main, adj=adj.main, mgp=mgp.main, font.main=font.main, line=main.line)
}
if(!is.null(sub)) {
title(sub,cex.main=cex.sub, adj=adj.main, mgp=mgp.main, font.main=font.sub, line=0)
}
##if(!is.null(main)) title(main,cex.main=1.5*op[["cex.main"]])
## 8) plot the color-key
if(key) {
cex.key <- 0.75
op.ori <- par()
par(mar=c(2,1.5,0.75,1)*keysize,cex=cex.key,mgp=c(0.75,0,0),tcl=-0.05)
z <- seq(x.range[1],x.range[2],length=length(colors))
flog.debug(paste("::inferCNV::heatmap.cnv colorkey z range: ", paste(z, collapse=","), sep=""))
flog.debug(paste("::inferCNV::heatmap.cnv colorkey breaks range: ", paste(breaks, collapse=","), sep=""))
flog.debug(paste("::inferCNV::heatmap.cnv colorkey colors range: ", paste(colors, collapse=","), sep=""))
image(z=matrix(z, ncol=1),
col=colors,
breaks=breaks,
xaxt="n",
yaxt="n",
xlab=key.xlab,
ylab="",
main="",
add=force_add,
useRaster=useRaster
)
par(usr=c(0,1,0,1))
lv <- pretty(breaks)
xv <- .scale.x(as.numeric(lv), x.range[1], x.range[2])
axis(1,at=xv,labels=lv,cex.axis=cex.key*1)
if(density.info=="density") {
## Experimental : also plot density of data
dens <- density(x, adjust=densadj,na.rm=TRUE)
omit <- dens$x < min(breaks) | dens$x > max(breaks)
dens$x <- dens$x[-omit]
dens$y <- dens$y[-omit]
dens$x <- .scale.x(dens$x, x.range[1], x.range[2])
lines(dens$x,dens$y / max(dens$y) * 0.95, col=denscol,lwd=1)
axis(2, at=pretty(dens$y)/max(dens$y) * 0.95, pretty(dens$y), cex.axis=cex.key*1)
##title("Color Key and Density",cex.lab=cex.key*0.25)
title(key.title, cex.main=cex.key, font.main=1)
mtext(side=2, "Density", line=0.75, cex=cex.key)
} else if(density.info=="histogram") {
h <- hist(x, plot=FALSE, breaks=breaks)
hx <- .scale.x(breaks, x.range[1], x.range[2])
hy <- c(h$counts, h$counts[length(h$counts)])
lines(hx, hy/max(hy)*0.95, lwd=1,type="s", col=denscol)
axis(2, at=pretty(hy)/max(hy)*0.95, pretty(hy), cex.axis=cex.key*1)
##title("Color Key and Histogram",cex.main=cex.key*0.25)
title(key.title, cex.main=cex.key, font.main=1)
mtext(side=2, key.ylab, line=0.75, cex=cex.key)
} else {
title(key.title, cex.main=cex.key, font.main=1)
}
par(mar=op.ori$mar, cex=op.ori$cex, mgp=op.ori$mgp, tcl=op.ori$tcl, usr=op.ori$usr)
} else{
if(!force_add){
.plot.text()
}
}
## 9)
if(plot.row.individuals) {
.plot.text("Row\nIndividuals", cex=cex.text, bg="white")
for (i in seq_len(ir)) {
##.plot.text()
tmp <- plot.row.individuals.list[[i]]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
}
}
## 10)
if(plot.col.individuals) {
.plot.text("Column\nIndividuals",cex=cex.text,bg="white",srt=90)
for (i in seq_len(ic)) {
##.plot.text()
tmp <- plot.col.individuals.list[[i]]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
}
}
## 11) for RowPlot from bottom
if (plot.row.clusters) {
.plot.text("Row\nClusters", cex=cex.text, bg="white")
tmp <- plot.row.clusters.list[[1]]
row.data <- row.data[rowInd]
for (i in unique(row.clusters)) {
i.x <- row.data[row.clusters==i]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
i.main <- sprintf("Row group %s (n=%s)",i,length(i.x))
title(i.main, cex.main=1, font.main=1)
}
}
## 12) for ColPlot from left
if (plot.col.clusters) {
.plot.text("Col\nClusters", cex=cex.text, bg="white", srt=90)
tmp <- plot.col.clusters.list[[1]]
col.data <- if(revC) col.data[rev(colInd)] else col.data[colInd]
for (i in unique(col.clusters)){
i.x <- col.data[col.clusters==i]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
i.main <- sprintf("Col group %s (n=%s)", i, length(i.x))
title(i.main, cex.main=1, font.main=1)
}
}
## 13)
if(plot.row.clustering) {
.plot.text("Row\nClustering",cex=cex.text,bg="white")
for (i in seq_len(cr)) {
##.plot.text()
tmp <- plot.row.clustering.list[[i]]
for(j in seq_along(tmp)){
eval(tmp[[j]])
}
}
}
## 14)
if(plot.col.clustering) {
.plot.text("Column\nClustering", cex=cex.text, bg="white", srt=90)
for (i in seq_len(cc)) {
##.plot.text()
tmp <- plot.col.clustering.list[[i]]
for(j in seq_along(tmp)) {
eval(tmp[[j]])
}
}
}
## 15) text
if (!.invalid(text.box) & if.plot.info) {
.plot.text(text.box, cex=cex.text, bg="gray75")
} else{
if (flag.text){
.plot.text()
}
}
} else {
x.save <- x
}
# ret <-
# list(x.ori=x.ori,
# x=x.save,
# rowInd=rowInd,colInd=colInd,
# row.clusters=row.clusters,col.clusters=col.clusters,
# dist.row=dist.row,dist.col=dist.col,
# hclust.row=hclust.row,hclust.col=hclust.col,
# kr=kr,kc=kc
# )
ret <-
list(x.ori=x.ori,
x=x.save,
rowInd=rowInd, colInd=colInd,
# row.clusters=row.clusters, col.clusters=col.clusters,
kr=kr, kc=kc
)
# class(ret) <- c("hclustering",class(ret))
invisible(ret)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## The official version from the package GMD is now archived
## https://cran.r-project.org/web/packages/GMD/index.html
gdist <-
function(x,
method="euclidean",
MoreArgs=NULL,
diag=FALSE,
upper=FALSE
)
{
if(method %in% c("correlation","correlation.of.observations")) {
FUN <- function(x,...) {
as.dist(1-cor(t(x),y=NULL,...),diag=diag,upper=upper)
}
if (.invalid(MoreArgs)) {
MoreArgs=list(method="pearson",use="everything")
}
} else if(method %in% c("correlation.of.variables")) {
FUN <- function(x,...) {
as.dist(1-cor(x,y=NULL,...),diag=diag,upper=upper)
}
if (.invalid(MoreArgs)) {
MoreArgs=list(method="pearson",use="everything")
}
}
COMMON_METHODS <-
c("euclidean","maximum",
"manhattan","canberra",
"binary","minkowski"
)
if(method %in% COMMON_METHODS) {
d <- dist(x=x, method=method, diag=diag, upper=upper, p=MoreArgs$p)
} else if (method %in% c("correlation","correlation.of.observations","correlation.of.variables")) {
##d <- .call.FUN(FUN,x,MoreArgs)
d <- FUN(x, method=MoreArgs$method, use=MoreArgs$use)
attr(d, "method") <- method
} else {
FUN <- match.fun(method)
MoreArgs[["diag"]] <- diag
MoreArgs[["upper"]] <- upper
d <- .call.FUN(FUN,x,MoreArgs)
## check attributes of the dist object ##
if(is.null(attr(d,"method"))) {
attr(d,"method") <- method
}
if(is.null(attr(d,"call"))) {
attr(d,"call") <- match.call()
}
if(is.null(attr(d,"Size"))) {
warning(sprintf("The `dist' object returned by %s does not contain a specified attribute of `Size'.",attr(d,"method")))
}
if(is.null(attr(d,"Labels"))) {
warning(sprintf("The `dist' object returned by %s does not contain a specified attribute of `Labels'.",attr(d,"method")))
}
}
attr(d,"Diag") <- diag
attr(d,"Upper") <- upper
class(d) <- "dist"
return(d)
}
## Please note this code is from the library GMD
## All credit for this code goes to GMD's authors.
## I do not recommend using this version of the code, which
## has been poorly modified for our use but recommend using
## the official version from the package GMD
## https://cran.r-project.org/web/packages/GMD/index.html
## Get row or column lines of separation for \code{heatmap.3} according to clusters
## param clusters a numerical vector, indicating the cluster labels of observations.
## param type string, one of the following: \code{c("row","column","both")}
get.sep <-
function(clusters,type=c("row","column","both"))
{
## if(!is.numeric(clusters) | !(.is.grouped(clusters))){
## stop("`clusters' should be a grouped numeric vector.")
## }
tmp.whichna <- which(is.na(clusters))
tmp.which <- which(!duplicated(clusters))
tmp.sep <- data.frame(start=tmp.which, end=c(tmp.which[-1], length(clusters)+1)-1)
tmp.sep2 <- tmp.sep[tmp.sep$start<=tmp.sep$end,]
## lines of separation
sepList <- list()
if (type=="row") {
xmax <- length(clusters)
for(i.s in seq_len(nrow(tmp.sep2))) {
sepList[[i.s]] <- c(0, tmp.sep2[i.s, 1]-1, xmax, tmp.sep2[i.s, 2])
}
} else if (type=="column") {
ymax <- length(clusters)
for(i.s in seq_len(nrow(tmp.sep2))) {
sepList[[i.s]] <- c(tmp.sep2[i.s, 1]-1, 0, tmp.sep2[i.s, 2], ymax)
}
} else if (type=="both") {
for(i.s in seq_len(nrow(tmp.sep2))) {
sepList[[i.s]] <- c(tmp.sep2[i.s, 1]-1, tmp.sep2[i.s, 1] - 1, tmp.sep2[i.s, 2], tmp.sep2[i.s, 2])
}
}
sepList
}
#####################################################
## Custom infercnv functions related to visualization
depress_low_signal_midpt_ratio <- function(infercnv_obj, expr_mean, midpt_ratio=0.2, slope=20) {
expr_bounds = get_average_bounds(infercnv_obj)
delta_mean = max(expr_mean - expr_bounds[1], expr_bounds[2] - expr_mean)
delta_midpt = delta_mean * midpt_ratio
infercnv_obj <- depress_log_signal_midpt_val(infercnv_obj, expr_mean, delta_midpt, slope)
return(infercnv_obj)
}
depress_log_signal_midpt_val <- function(infercnv_obj, expr_mean, delta_midpt, slope=20) {
infercnv_obj@expr.data <- .apply_logistic_val_adj(infercnv_obj@expr.data, expr_mean, delta_midpt, slope)
return(infercnv_obj)
}
.apply_logistic_val_adj <- function(vals_matrix, expr_mean, delta_midpt, slope) {
adjust_value <- function(x) {
newval = x
val = abs(x - expr_mean)
p = .logistic(val, delta_midpt, slope)
if (x > expr_mean) {
newval = expr_mean + p*val
} else if (x < expr_mean) {
newval = expr_mean - p*val
}
return(newval)
}
vals_matrix <- apply(vals_matrix, seq_len(2), adjust_value)
return(vals_matrix)
}
# method that returns an artificial dendrogram with only 1 branch
# since at least 2 elements need to be present for building one normally
# but we need to be able to merge this lone branch to the bigger dendrogram for plotting
.single_element_dendrogram <- function(unique_label) {
dfake = list()
dfake[[1]] = 1L
attr(dfake, "class") = "dendrogram"
attr(dfake, "height") = 1
attr(dfake, "members") = 1L
attr(dfake[[1]], "class") = "dendrogram"
attr(dfake[[1]], "leaf") = TRUE
attr(dfake[[1]], "label") = unique_label
attr(dfake[[1]], "height") = 0
return(dfake)
}
.pairwise_dendrogram <- function(labels) {
dfake = list()
dfake[[1]] = 1L
dfake[[2]] = 1L
attr(dfake, "class") = "dendrogram"
attr(dfake, "height") = 1
attr(dfake, "members") = 2L
attr(dfake[[1]], "class") = "dendrogram"
attr(dfake[[1]], "leaf") = TRUE
attr(dfake[[1]], "label") = labels[1]
attr(dfake[[1]], "height") = 0
attr(dfake[[2]], "class") = "dendrogram"
attr(dfake[[2]], "leaf") = TRUE
attr(dfake[[2]], "label") = labels[2]
attr(dfake[[2]], "height") = 0
return(dfake)
}
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