R/apHeatMap.R
In apratap/rbundle:
require(grid)
require(RColorBrewer)
require(memoise)
library("flashClust")
lo = function(rown, coln, nrow, ncol, cellheight = NA, cellwidth = NA,
treeheight_col, treeheight_row, legend, annotation, annotation_colors, annotation_legend,
main, fontsize, fontsize_row, fontsize_col, ...){
# Get height of colnames and length of rownames
if(!is.null(coln[1])){
longest_coln = which.max(strwidth(coln, units = 'in'))
gp = list(fontsize = fontsize_col, ...)
coln_height = unit(1, "grobheight", textGrob(coln[longest_coln], rot = 90, gp = do.call(gpar, gp))) + unit(5, "bigpts")
}
else{
coln_height = unit(5, "bigpts")
}
if(!is.null(rown[1])){
longest_rown = which.max(strwidth(rown, units = 'in'))
gp = list(fontsize = fontsize_row, ...)
rown_width = unit(1, "grobwidth", textGrob(rown[longest_rown], gp = do.call(gpar, gp))) + unit(10, "bigpts")
}
else{
rown_width = unit(5, "bigpts")
}
gp = list(fontsize = fontsize, ...)
# Legend position
if(!is.na(legend[1])){
longest_break = which.max(nchar(names(legend)))
longest_break = unit(1.1, "grobwidth", textGrob(as.character(names(legend))[longest_break], gp = do.call(gpar, gp)))
title_length = unit(1.1, "grobwidth", textGrob("Scale", gp = gpar(fontface = "bold", ...)))
legend_width = unit(12, "bigpts") + longest_break * 1.2
legend_width = max(title_length, legend_width)
}
else{
legend_width = unit(0, "bigpts")
}
# Set main title height
if(is.na(main)){
main_height = unit(0, "npc")
}
else{
main_height = unit(1.5, "grobheight", textGrob(main, gp = gpar(fontsize = 1.3 * fontsize, ...)))
}
# Column annotations
if(!is.na(annotation[[1]][1])){
# Column annotation height
annot_height = unit(ncol(annotation) * (8 + 2) + 2, "bigpts")
# Width of the correponding legend
longest_ann = which.max(nchar(as.matrix(annotation)))
annot_legend_width = unit(1.2, "grobwidth", textGrob(as.matrix(annotation)[longest_ann], gp = gpar(...))) + unit(12, "bigpts")
if(!annotation_legend){
annot_legend_width = unit(0, "npc")
}
}
else{
annot_height = unit(0, "bigpts")
annot_legend_width = unit(0, "bigpts")
}
# Tree height
treeheight_col = unit(treeheight_col, "bigpts") + unit(5, "bigpts")
treeheight_row = unit(treeheight_row, "bigpts") + unit(5, "bigpts")
# Set cell sizes
if(is.na(cellwidth)){
matwidth = unit(1, "npc") - rown_width - legend_width - treeheight_row - annot_legend_width
}
else{
matwidth = unit(cellwidth * ncol, "bigpts")
}
if(is.na(cellheight)){
matheight = unit(1, "npc") - main_height - coln_height - treeheight_col - annot_height
}
else{
matheight = unit(cellheight * nrow, "bigpts")
}
# Produce layout()
pushViewport(viewport(layout = grid.layout(nrow = 5, ncol = 5, widths = unit.c(treeheight_row, matwidth, rown_width, legend_width, annot_legend_width), heights = unit.c(main_height, treeheight_col, annot_height, matheight, coln_height)), gp = do.call(gpar, gp)))
# Get cell dimensions
pushViewport(vplayout(4, 2))
cellwidth = convertWidth(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / ncol
cellheight = convertHeight(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / nrow
upViewport()
# Return minimal cell dimension in bigpts to decide if borders are drawn
mindim = min(cellwidth, cellheight)
return(mindim)
}
draw_dendrogram = function(hc, horizontal = T){
h = hc$height / max(hc$height) / 1.05
m = hc$merge
o = hc$order
n = length(o)
m[m > 0] = n + m[m > 0]
m[m < 0] = abs(m[m < 0])
dist = matrix(0, nrow = 2 * n - 1, ncol = 2, dimnames = list(NULL, c("x", "y")))
dist[1:n, 1] = 1 / n / 2 + (1 / n) * (match(1:n, o) - 1)
for(i in 1:nrow(m)){
dist[n + i, 1] = (dist[m[i, 1], 1] + dist[m[i, 2], 1]) / 2
dist[n + i, 2] = h[i]
}
draw_connection = function(x1, x2, y1, y2, y){
grid.lines(x = c(x1, x1), y = c(y1, y))
grid.lines(x = c(x2, x2), y = c(y2, y))
grid.lines(x = c(x1, x2), y = c(y, y))
}
if(horizontal){
for(i in 1:nrow(m)){
draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
}
}
else{
gr = rectGrob()
pushViewport(viewport(height = unit(1, "grobwidth", gr), width = unit(1, "grobheight", gr), angle = 90))
dist[, 1] = 1 - dist[, 1]
for(i in 1:nrow(m)){
draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
}
upViewport()
}
}
draw_matrix = function(matrix, border_color, fmat, fontsize_number,vp){
######
## modified to use raster to draw imagemaps
######
#n = nrow(matrix)
#m = ncol(matrix)
#x = (1:m)/m - 1/2/m
#y = 1 - ((1:n)/n - 1/2/n)
grid.raster(matrix,height=unit(1, "npc"),width=unit(1, "npc"),vp=vp,interpolate=FALSE)
# for(i in 1:m){
#grid.rect(x = x[i], y = y[1:n], width = 1/m, height = 1/n, gp = gpar(fill = matrix[,i], col = border_color))
# if(attr(fmat, "draw")){
# grid.text(x = x[i], y = y[1:n], label = fmat[, i], gp = gpar(col = "grey30", fontsize = fontsize_number))
# }
# }
}
draw_colnames = function(coln, ...){
m = length(coln)
x = (1:m)/m - 1/2/m
grid.text(coln, x = x, y = unit(0.90, "npc"), vjust = 0.5, hjust = 0, rot = 270, gp = gpar(...))
}
draw_rownames = function(rown, ...){
n = length(rown)
y = 1 - ((1:n)/n - 1/2/n)
grid.text(rown, x = unit(0.04, "npc"), y = y, vjust = 0.5, hjust = 0, gp = gpar(...))
}
draw_legend = function(color, breaks, legend, ...){
height = min(unit(1, "npc"), unit(150, "bigpts"))
pushViewport(viewport(x = 0, y = unit(1, "npc"), just = c(0, 1), height = height))
legend_pos = (legend - min(breaks)) / (max(breaks) - min(breaks))
breaks = (breaks - min(breaks)) / (max(breaks) - min(breaks))
h = breaks[-1] - breaks[-length(breaks)]
grid.rect(x = 0, y = breaks[-length(breaks)], width = unit(10, "bigpts"), height = h, hjust = 0, vjust = 0, gp = gpar(fill = color, col = "#FFFFFF00"))
grid.text(names(legend), x = unit(12, "bigpts"), y = legend_pos, hjust = 0, gp = gpar(...))
upViewport()
}
convert_annotations = function(annotation, annotation_colors){
new = annotation
for(i in 1:ncol(annotation)){
a = annotation[, i]
b = annotation_colors[[colnames(annotation)[i]]]
if(is.character(a) | is.factor(a)){
a = as.character(a)
if(length(setdiff(a, names(b))) > 0){
stop(sprintf("Factor levels on variable %s do not match with annotation_colors", colnames(annotation)[i]))
}
new[, i] = b[a]
}
else{
a = cut(a, breaks = 100)
new[, i] = colorRampPalette(b)(100)[a]
}
}
return(as.matrix(new))
}
draw_annotations = function(converted_annotations, border_color){
n = ncol(converted_annotations)
m = nrow(converted_annotations)
x = (1:m)/m - 1/2/m
y = cumsum(rep(8, n)) - 4 + cumsum(rep(2, n))
for(i in 1:m){
grid.rect(x = x[i], unit(y[1:n], "bigpts"), width = 1/m, height = unit(8, "bigpts"), gp = gpar(fill = converted_annotations[i, ], col = border_color))
}
}
draw_annotation_legend = function(annotation, annotation_colors, border_color, ...){
y = unit(1, "npc")
text_height = unit(1, "grobheight", textGrob("FGH", gp = gpar(...)))
for(i in names(annotation_colors)){
grid.text(i, x = 0, y = y, vjust = 1, hjust = 0, gp = gpar(fontface = "bold", ...))
y = y - 1.5 * text_height
if(is.character(annotation[, i]) | is.factor(annotation[, i])){
for(j in 1:length(annotation_colors[[i]])){
grid.rect(x = unit(0, "npc"), y = y, hjust = 0, vjust = 1, height = text_height, width = text_height, gp = gpar(col = border_color, fill = annotation_colors[[i]][j]))
grid.text(names(annotation_colors[[i]])[j], x = text_height * 1.3, y = y, hjust = 0, vjust = 1, gp = gpar(...))
y = y - 1.5 * text_height
}
}
else{
yy = y - 4 * text_height + seq(0, 1, 0.02) * 4 * text_height
h = 4 * text_height * 0.02
grid.rect(x = unit(0, "npc"), y = yy, hjust = 0, vjust = 1, height = h, width = text_height, gp = gpar(col = "#FFFFFF00", fill = colorRampPalette(annotation_colors[[i]])(50)))
txt = rev(range(grid.pretty(range(annotation[, i], na.rm = TRUE))))
yy = y - c(0, 3) * text_height
grid.text(txt, x = text_height * 1.3, y = yy, hjust = 0, vjust = 1, gp = gpar(...))
y = y - 4.5 * text_height
}
y = y - 1.5 * text_height
}
}
draw_main = function(text, ...){
grid.text(text, gp = gpar(fontface = "bold", ...))
}
vplayout = function(x, y){
return(viewport(layout.pos.row = x, layout.pos.col = y))
}
heatmap_motor = function(matrix, border_color, cellwidth, cellheight, tree_col, tree_row,
treeheight_col, treeheight_row, filename, width, height, breaks, color, legend,
annotation, annotation_colors, annotation_legend, main, fontsize, fontsize_row,
fontsize_col, fmat, fontsize_number, useRaster, drawRowD,
explicit_rownames=NULL, ...){
grid.newpage()
# Set layout
mindim = lo(coln = colnames(matrix), rown = rownames(matrix), nrow = nrow(matrix),
ncol = ncol(matrix), cellwidth = cellwidth, cellheight = cellheight,
treeheight_col = treeheight_col, treeheight_row = treeheight_row,
legend = legend, annotation = annotation, annotation_colors = annotation_colors,
annotation_legend = annotation_legend, main = main, fontsize = fontsize, fontsize_row = fontsize_row,
fontsize_col = fontsize_col, ...)
if(!is.na(filename)){
pushViewport(vplayout(1:5, 1:5))
if(is.na(height)){
height = convertHeight(unit(0:1, "npc"), "inches", valueOnly = T)[2]
}
if(is.na(width)){
width = convertWidth(unit(0:1, "npc"), "inches", valueOnly = T)[2]
}
# Get file type
r = regexpr("\\.[a-zA-Z]*$", filename)
if(r == -1) stop("Improper filename")
ending = substr(filename, r + 1, r + attr(r, "match.length"))
f = switch(ending,
pdf = function(x, ...) pdf(x, ...),
png = function(x, ...) png(x, units = "in", res = 300, ...),
jpeg = function(x, ...) jpeg(x, units = "in", res = 300, ...),
jpg = function(x, ...) jpeg(x, units = "in", res = 300, ...),
tiff = function(x, ...) tiff(x, units = "in", res = 300, compression = "lzw", ...),
bmp = function(x, ...) bmp(x, units = "in", res = 300, ...),
stop("File type should be: pdf, png, bmp, jpg, tiff")
)
# print(sprintf("height:%f width:%f", height, width))
f(filename, height = height, width = width)
heatmap_motor(matrix, cellwidth = cellwidth, cellheight = cellheight,
border_color = border_color, tree_col = tree_col, tree_row = tree_row,
treeheight_col = treeheight_col, treeheight_row = treeheight_row, breaks = breaks,
color = color, legend = legend, annotation = annotation, annotation_colors = annotation_colors,
annotation_legend = annotation_legend, filename = NA, main = main, fontsize = fontsize,
fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat,
fontsize_number = fontsize_number, useRaster = useRaster, drawRowD = drawRowD,
explicit_rownames=NULL, ...)
dev.off()
upViewport()
return()
}
# Omit border color if cell size is too small
if(mindim < 3) border_color = NA
# Draw title
if(!is.na(main)){
pushViewport(vplayout(1, 2))
draw_main(main, fontsize = 1.3 * fontsize, ...)
upViewport()
}
# Draw tree for the columns
if(!is.na(tree_col[[1]][1]) & treeheight_col != 0){
pushViewport(vplayout(2, 2))
draw_dendrogram(tree_col, horizontal = T)
upViewport()
}
# Draw tree for the rows
if( drawRowD == TRUE | nrow(matrix) <= 200 ){
if(!is.na(tree_row[[1]][1]) & treeheight_row != 0 ){
pushViewport(vplayout(4, 1))
draw_dendrogram(tree_row, horizontal = F)
upViewport()
}
}
# Draw matrix
vp = vplayout(4, 2)
draw_matrix(matrix, border_color, fmat, fontsize_number,vp=vp)
pushViewport(vp)
upViewport()
#Draw colnames
if(length(colnames(matrix)) != 0){
pushViewport(vplayout(5, 2))
pars = list(colnames(matrix), fontsize = fontsize_col, ...)
do.call(draw_colnames, pars)
upViewport()
}
#Draw rownames
if(length(rownames(matrix)) != 0 ){
if(is.null(explicit_rownames) == 'TRUE'){
explicit_rownames = rownames(matrix)
}
pushViewport(vplayout(4, 3))
pars = list(explicit_rownames, fontsize = fontsize_row, ...)
do.call(draw_rownames, pars)
upViewport()
}
# Draw annotation tracks
if(!is.na(annotation[[1]][1])){
pushViewport(vplayout(3, 2))
converted_annotation = convert_annotations(annotation, annotation_colors)
draw_annotations(converted_annotation, border_color)
upViewport()
}
# Draw annotation legend
if(!is.na(annotation[[1]][1]) & annotation_legend){
if( length(rownames(matrix)) <= 70 ) {
pushViewport(vplayout(4:5, 5))
}
else{
pushViewport(vplayout(3:5, 5))
}
draw_annotation_legend(annotation, annotation_colors, border_color, fontsize = fontsize, ...)
upViewport()
}
# Draw legend IF not drawing the ROW names
if(!is.na(legend[1])){
length(colnames(matrix))
if(length(rownames(matrix)) <= 70 ){
# pushViewport(vplayout(4:5, 4))
# DO NOTHING
}
else{
pushViewport(vplayout(3:5, 4))
draw_legend(color, breaks, legend, fontsize = fontsize, ...)
upViewport()
}
}
}
generate_breaks = function(x, n, center = F){
if(center){
m = max(abs(c(min(x, na.rm = T), max(x, na.rm = T))))
res = seq(-m, m, length.out = n + 1)
}
else{
res = seq(min(x, na.rm = T), max(x, na.rm = T), length.out = n + 1)
}
return(res)
}
scale_vec_colours = function(x, col = rainbow(10), breaks = NA){
return(col[as.numeric(cut(x, breaks = breaks, include.lowest = T))])
}
scale_colours = function(mat, col = rainbow(10), breaks = NA){
mat = as.matrix(mat)
return(matrix(scale_vec_colours(as.vector(mat), col = col, breaks = breaks), nrow(mat), ncol(mat), dimnames = list(rownames(mat), colnames(mat))))
}
cluster_mat = function(mat, distance, method, cor_method){
if(!(method %in% c("ward", "single", "complete", "average", "mcquitty", "median", "centroid"))){
stop("clustering method has to one form the list: 'ward', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.")
}
if(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) & class(distance) != "dist"){
print(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) | class(distance) != "dist")
stop("distance has to be a dissimilarity structure as produced by dist or one measure form the list: 'correlation', 'euclidean', 'maximum', 'manhattan', 'canberra', 'binary', 'minkowski'")
}
if(distance[1] == "correlation"){
d = as.dist(1 - cor(t(mat),method=cor_method))
}
else{
if(class(distance) == "dist"){
d = distance
}
else{
d = dist(mat, method = distance)
}
}
return(flashClust(d, method = method)) #hclust replaced by flashClust from WCGNA (much faster than hclust)
}
#for faster rendering caching the computationally expensive functions
memoised_cluster_mat <- memoise(function(mat, distance, method, cor_method) cluster_mat(mat, distance, method,cor_method))
scale_rows = function(x){
m = apply(x, 1, mean, na.rm = T)
s = apply(x, 1, sd, na.rm = T)
return((x - m) / s)
}
scale_mat = function(mat, scale){
if(!(scale %in% c("none", "row", "column"))){
stop("scale argument shoud take values: 'none', 'row' or 'column'")
}
mat = switch(scale, none = mat, row = scale_rows(mat), column = t(scale_rows(t(mat))))
return(mat)
}
generate_annotation_colours = function(annotation, annotation_colors, drop){
if(is.na(annotation_colors)[[1]][1]){
annotation_colors = list()
}
count = 0
for(i in 1:ncol(annotation)){
if(is.character(annotation[, i]) | is.factor(annotation[, i])){
if (is.factor(annotation[, i]) & !drop){
count = count + length(levels(annotation[, i]))
}
else{
count = count + length(unique(annotation[, i]))
}
}
}
factor_colors = hsv((seq(0, 1, length.out = count + 1)[-1] +
0.2)%%1, 0.7, 0.95)
set.seed(3453)
for(i in 1:ncol(annotation)){
if(!(colnames(annotation)[i] %in% names(annotation_colors))){
if(is.character(annotation[, i]) | is.factor(annotation[, i])){
n = length(unique(annotation[, i]))
if (is.factor(annotation[, i]) & !drop){
n = length(levels(annotation[, i]))
}
ind = sample(1:length(factor_colors), n)
annotation_colors[[colnames(annotation)[i]]] = factor_colors[ind]
l = levels(as.factor(annotation[, i]))
l = l[l %in% unique(annotation[, i])]
if (is.factor(annotation[, i]) & !drop){
l = levels(annotation[, i])
}
names(annotation_colors[[colnames(annotation)[i]]]) = l
factor_colors = factor_colors[-ind]
}
else{
r = runif(1)
annotation_colors[[colnames(annotation)[i]]] = hsv(r, c(0.1, 1), 1)
}
}
}
return(annotation_colors)
}
kmeans_pheatmap = function(mat, k = min(nrow(mat), 150), sd_limit = NA, ...){
# Filter data
if(!is.na(sd_limit)){
s = apply(mat, 1, sd)
mat = mat[s > sd_limit, ]
}
# Cluster data
set.seed(1245678)
km = kmeans(mat, k, iter.max = 100)
mat2 = km$centers
# Compose rownames
t = table(km$cluster)
rownames(mat2) = sprintf("cl%s_size_%d", names(t), t)
# Draw heatmap
pheatmap(mat2, ...)
}
#' A function to draw clustered heatmaps.
#'
#' A function to draw clustered heatmaps where one has better control over some graphical
#' parameters such as cell size, etc.
#'
#' The function also allows to aggregate the rows using kmeans clustering. This is
#' advisable if number of rows is so big that R cannot handle their hierarchical
#' clustering anymore, roughly more than 1000. Instead of showing all the rows
#' separately one can cluster the rows in advance and show only the cluster centers.
#' The number of clusters can be tuned with parameter kmeans_k.
#'
#' @param mat numeric matrix of the values to be plotted.
#' @param color vector of colors used in heatmap.
#' @param kmeans_k the number of kmeans clusters to make, if we want to agggregate the
#' rows before drawing heatmap. If NA then the rows are not aggregated.
#' @param breaks a sequence of numbers that covers the range of values in mat and is one
#' element longer than color vector. Used for mapping values to colors. Useful, if needed
#' to map certain values to certain colors, to certain values. If value is NA then the
#' breaks are calculated automatically.
#' @param border_color color of cell borders on heatmap, use NA if no border should be
#' drawn.
#' @param cellwidth individual cell width in points. If left as NA, then the values
#' depend on the size of plotting window.
#' @param cellheight individual cell height in points. If left as NA,
#' then the values depend on the size of plotting window.
#' @param scale character indicating if the values should be centered and scaled in
#' either the row direction or the column direction, or none. Corresponding values are
#' \code{"row"}, \code{"column"} and \code{"none"}
#' @param cluster_rows boolean values determining if rows should be clustered,
#' @param cluster_cols boolean values determining if columns should be clustered.
#' @param clustering_distance_rows distance measure used in clustering rows. Possible
#' values are \code{"correlation"} for Pearson correlation and all the distances
#' supported by \code{\link{dist}}, such as \code{"euclidean"}, etc. If the value is none
#' of the above it is assumed that a distance matrix is provided.
#' @param clustering_distance_cols distance measure used in clustering columns. Possible
#' values the same as for clustering_distance_rows.
#' @param clustering_method clustering method used. Accepts the same values as
#' \code{\link{hclust}}.
#' @param treeheight_row the height of a tree for rows, if these are clustered.
#' Default value 50 points.
#' @param treeheight_col the height of a tree for columns, if these are clustered.
#' Default value 50 points.
#' @param legend logical to determine if legend should be drawn or not.
#' @param legend_breaks vector of breakpoints for the legend.
#' @param legend_labels vector of labels for the \code{legend_breaks}.
#' @param annotation data frame that specifies the annotations shown on top of the
#' columns. Each row defines the features for a specific column. The columns in the data
#' and rows in the annotation are matched using corresponding row and column names. Note
#' that color schemes takes into account if variable is continuous or discrete.
#' @param annotation_colors list for specifying annotation track colors manually. It is
#' possible to define the colors for only some of the features. Check examples for
#' details.
#' @param annotation_legend boolean value showing if the legend for annotation tracks
#' should be drawn.
#' @param drop_levels logical to determine if unused levels are also shown in the legend
#' @param show_rownames boolean specifying if column names are be shown.
#' @param show_colnames boolean specifying if column names are be shown.
#' @param main the title of the plot
#' @param fontsize base fontsize for the plot
#' @param fontsize_row fontsize for rownames (Default: fontsize)
#' @param fontsize_col fontsize for colnames (Default: fontsize)
#' @param display_numbers logical determining if the numeric values are also printed to
#' the cells.
#' @param number_format format strings (C printf style) of the numbers shown in cells.
#' For example "\code{\%.2f}" shows 2 decimal places and "\code{\%.1e}" shows exponential
#' notation (see more in \code{\link{sprintf}}).
#' @param fontsize_number fontsize of the numbers displayed in cells
#' @param filename file path where to save the picture. Filetype is decided by
#' the extension in the path. Currently following formats are supported: png, pdf, tiff,
#' bmp, jpeg. Even if the plot does not fit into the plotting window, the file size is
#' calculated so that the plot would fit there, unless specified otherwise.
#' @param width manual option for determining the output file width in inches.
#' @param height manual option for determining the output file height in inches.
#' @param \dots graphical parameters for the text used in plot. Parameters passed to
#' \code{\link{grid.text}}, see \code{\link{gpar}}.
#'
#' @return
#' Invisibly a list of components
#' \itemize{
#' \item \code{tree_row} the clustering of rows as \code{\link{hclust}} object
#' \item \code{tree_col} the clustering of columns as \code{\link{hclust}} object
#' \item \code{kmeans} the kmeans clustering of rows if parameter \code{kmeans_k} was
#' specified
#' }
#'
#' @author Raivo Kolde <rkolde@@gmail.com>
#' @examples
#' # Generate some data
#' test = matrix(rnorm(200), 20, 10)
#' test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
#' test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
#' test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
#' colnames(test) = paste("Test", 1:10, sep = "")
#' rownames(test) = paste("Gene", 1:20, sep = "")
#'
#' # Draw heatmaps
#' pheatmap(test)
#' pheatmap(test, kmeans_k = 2)
#' pheatmap(test, scale = "row", clustering_distance_rows = "correlation")
#' pheatmap(test, color = colorRampPalette(c("navy", "white", "firebrick3"))(50))
#' pheatmap(test, cluster_row = FALSE)
#' pheatmap(test, legend = FALSE)
#' pheatmap(test, display_numbers = TRUE)
#' pheatmap(test, display_numbers = TRUE, number_format = "%.1e")
#' pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0",
#' "1e-4", "1e-3", "1e-2", "1e-1", "1"))
#' pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")
#' pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")
#'
#'
#' # Generate column annotations
#' annotation = data.frame(Var1 = factor(1:10 %% 2 == 0,
#' labels = c("Class1", "Class2")), Var2 = 1:10)
#' annotation$Var1 = factor(annotation$Var1, levels = c("Class1", "Class2", "Class3"))
#' rownames(annotation) = paste("Test", 1:10, sep = "")
#'
#' pheatmap(test, annotation = annotation)
#' pheatmap(test, annotation = annotation, annotation_legend = FALSE)
#' pheatmap(test, annotation = annotation, annotation_legend = FALSE, drop_levels = FALSE)
#'
#' # Specify colors
#' Var1 = c("navy", "darkgreen")
#' names(Var1) = c("Class1", "Class2")
#' Var2 = c("lightgreen", "navy")
#'
#' ann_colors = list(Var1 = Var1, Var2 = Var2)
#'
#' pheatmap(test, annotation = annotation, annotation_colors = ann_colors, main = "Example")
#'
#' # Specifying clustering from distance matrix
#' drows = dist(test, method = "minkowski")
#' dcols = dist(t(test), method = "minkowski")
#' pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)
#'
#' @export
memoised_pheatmap = function(mat, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
kmeans_k = NA, breaks = NA, border_color = "grey60", cellwidth = NA,
cellheight = NA, scale = "none", cluster_rows = TRUE, cluster_cols = TRUE,
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
clustering_method = "complete", treeheight_row = ifelse(cluster_rows, 50, 0),
treeheight_col = ifelse(cluster_cols, 50, 0), legend = TRUE, legend_breaks = NA,
legend_labels = NA, annotation = NA, annotation_colors = NA, annotation_legend = TRUE,
drop_levels = TRUE, show_rownames = T, show_colnames = T, main = NA, fontsize = 10,
fontsize_row = fontsize, fontsize_col = fontsize, display_numbers = F, number_format = "%.2f",
fontsize_number = 0.8 * fontsize, filename = NA, width = NA, height = NA,
useRaster=FALSE, drawRowD=TRUE, cor_method = "pearson",
explicit_rownames = NULL, ...){
#time at which process started
start_time = proc.time()
# Preprocess matrix
mat = as.matrix(mat)
if(scale != "none"){
mat = scale_mat(mat, scale)
if(is.na(breaks)){
breaks = generate_breaks(mat, length(color), center = T)
}
}
# Kmeans
if(!is.na(kmeans_k)){
# Cluster data
km = kmeans(mat, kmeans_k, iter.max = 100)
mat = km$centers
# Compose rownames
t = table(km$cluster)
rownames(mat) = sprintf("cl%s_size_%d", names(t), t)
}
else{
km = NA
}
# Do clustering
if(cluster_rows){
tree_row = memoised_cluster_mat(mat, distance = clustering_distance_rows, method = clustering_method, cor_method=cor_method)
#tree_row = cluster_mat(mat, distance = clustering_distance_rows, method = clustering_method)
mat = mat[tree_row$order, , drop = FALSE]
if(is.null(explicit_rownames) == FALSE){
explicit_rownames = explicit_rownames[tree_row$order]
}
}
else{
tree_row = NA
treeheight_row = 0
}
if(cluster_cols){
tree_col = memoised_cluster_mat(t(mat), distance = clustering_distance_cols, method = clustering_method, cor_method=cor_method)
#tree_col = cluster_mat(t(mat), distance = clustering_distance_cols, method = clustering_method)
mat = mat[, tree_col$order, drop = FALSE]
}
else{
tree_col = NA
treeheight_col = 0
}
# Format numbers to be displayed in cells
if(display_numbers){
fmat = matrix(sprintf(number_format, mat), nrow = nrow(mat), ncol = ncol(mat))
attr(fmat, "draw") = TRUE
}
else{
fmat = matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
attr(fmat, "draw") = FALSE
}
# Colors and scales
if(!is.na(legend_breaks[1]) & !is.na(legend_labels[1])){
if(length(legend_breaks) != length(legend_labels)){
stop("Lengths of legend_breaks and legend_labels must be the same")
}
}
if(is.na(breaks[1])){
breaks = generate_breaks(as.vector(mat), length(color))
}
if (legend & is.na(legend_breaks[1])) {
legend = grid.pretty(range(as.vector(breaks)))
names(legend) = legend
}
else if(legend & !is.na(legend_breaks[1])){
legend = legend_breaks[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
if(!is.na(legend_labels[1])){
legend_labels = legend_labels[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
names(legend) = legend_labels
}
else{
names(legend) = legend
}
}
else {
legend = NA
}
mat = scale_colours(mat, col = color, breaks = breaks)
# Preparing annotation colors
if(!is.na(annotation[[1]][1])){
annotation = annotation[colnames(mat), , drop = F]
annotation_colors = generate_annotation_colours(annotation, annotation_colors, drop = drop_levels)
}
if(!show_rownames){
rownames(mat) = NULL
}
if(!show_colnames){
colnames(mat) = NULL
}
# Draw heatmap
heatmap_motor(mat, border_color = border_color, cellwidth = cellwidth, cellheight = cellheight,
treeheight_col = treeheight_col, treeheight_row = treeheight_row, tree_col = tree_col,
tree_row = tree_row, filename = filename, width = width, height = height, breaks = breaks,
color = color, legend = legend, annotation = annotation, annotation_colors = annotation_colors,
annotation_legend = annotation_legend, main = main, fontsize = fontsize,
fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat,
fontsize_number = fontsize_number, useRaster=useRaster, drawRowD=drawRowD,
explicit_rownames = explicit_rownames, ...)
#end time
end_time = proc.time()
total_time = end_time - start_time
invisible(list(tree_row = tree_row, tree_col = tree_col, kmeans = km, time=total_time))
}
#######################
#Main Wrapper function
#######################
#' @export
apHeatMap <- function(m, annotation = NA ,
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
cor_method="spearman",
clustering_method = "average",
scale = FALSE,...){
if(nrow(m) <= 2){
memoised_pheatmap(m, cluster_rows=FALSE,
scale="none",
annotation = annotation,
drawRowD = FALSE,
border_color = NA,...)
}
else{
#do the clustering and heatmap
#scaling genes across experiments
if(scale == "TRUE"){
m <- t(scale(t(m)))
}
memoised_pheatmap(m,
scale="none",
annotation = annotation,
clustering_distance_rows = clustering_distance_rows,
clustering_distance_cols = clustering_distance_cols,
clustering_method = clustering_method,
border_color = NA,
drawRowD = FALSE,
cor_method=cor_method,...)
}
}
apratap/rbundle documentation built on May 17, 2019, 6:38 p.m.
R Package Documentation
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require(grid)
require(RColorBrewer)
require(memoise)
library("flashClust")
lo = function(rown, coln, nrow, ncol, cellheight = NA, cellwidth = NA,
treeheight_col, treeheight_row, legend, annotation, annotation_colors, annotation_legend,
main, fontsize, fontsize_row, fontsize_col, ...){
# Get height of colnames and length of rownames
if(!is.null(coln[1])){
longest_coln = which.max(strwidth(coln, units = 'in'))
gp = list(fontsize = fontsize_col, ...)
coln_height = unit(1, "grobheight", textGrob(coln[longest_coln], rot = 90, gp = do.call(gpar, gp))) + unit(5, "bigpts")
}
else{
coln_height = unit(5, "bigpts")
}
if(!is.null(rown[1])){
longest_rown = which.max(strwidth(rown, units = 'in'))
gp = list(fontsize = fontsize_row, ...)
rown_width = unit(1, "grobwidth", textGrob(rown[longest_rown], gp = do.call(gpar, gp))) + unit(10, "bigpts")
}
else{
rown_width = unit(5, "bigpts")
}
gp = list(fontsize = fontsize, ...)
# Legend position
if(!is.na(legend[1])){
longest_break = which.max(nchar(names(legend)))
longest_break = unit(1.1, "grobwidth", textGrob(as.character(names(legend))[longest_break], gp = do.call(gpar, gp)))
title_length = unit(1.1, "grobwidth", textGrob("Scale", gp = gpar(fontface = "bold", ...)))
legend_width = unit(12, "bigpts") + longest_break * 1.2
legend_width = max(title_length, legend_width)
}
else{
legend_width = unit(0, "bigpts")
}
# Set main title height
if(is.na(main)){
main_height = unit(0, "npc")
}
else{
main_height = unit(1.5, "grobheight", textGrob(main, gp = gpar(fontsize = 1.3 * fontsize, ...)))
}
# Column annotations
if(!is.na(annotation[[1]][1])){
# Column annotation height
annot_height = unit(ncol(annotation) * (8 + 2) + 2, "bigpts")
# Width of the correponding legend
longest_ann = which.max(nchar(as.matrix(annotation)))
annot_legend_width = unit(1.2, "grobwidth", textGrob(as.matrix(annotation)[longest_ann], gp = gpar(...))) + unit(12, "bigpts")
if(!annotation_legend){
annot_legend_width = unit(0, "npc")
}
}
else{
annot_height = unit(0, "bigpts")
annot_legend_width = unit(0, "bigpts")
}
# Tree height
treeheight_col = unit(treeheight_col, "bigpts") + unit(5, "bigpts")
treeheight_row = unit(treeheight_row, "bigpts") + unit(5, "bigpts")
# Set cell sizes
if(is.na(cellwidth)){
matwidth = unit(1, "npc") - rown_width - legend_width - treeheight_row - annot_legend_width
}
else{
matwidth = unit(cellwidth * ncol, "bigpts")
}
if(is.na(cellheight)){
matheight = unit(1, "npc") - main_height - coln_height - treeheight_col - annot_height
}
else{
matheight = unit(cellheight * nrow, "bigpts")
}
# Produce layout()
pushViewport(viewport(layout = grid.layout(nrow = 5, ncol = 5, widths = unit.c(treeheight_row, matwidth, rown_width, legend_width, annot_legend_width), heights = unit.c(main_height, treeheight_col, annot_height, matheight, coln_height)), gp = do.call(gpar, gp)))
# Get cell dimensions
pushViewport(vplayout(4, 2))
cellwidth = convertWidth(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / ncol
cellheight = convertHeight(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / nrow
upViewport()
# Return minimal cell dimension in bigpts to decide if borders are drawn
mindim = min(cellwidth, cellheight)
return(mindim)
}
draw_dendrogram = function(hc, horizontal = T){
h = hc$height / max(hc$height) / 1.05
m = hc$merge
o = hc$order
n = length(o)
m[m > 0] = n + m[m > 0]
m[m < 0] = abs(m[m < 0])
dist = matrix(0, nrow = 2 * n - 1, ncol = 2, dimnames = list(NULL, c("x", "y")))
dist[1:n, 1] = 1 / n / 2 + (1 / n) * (match(1:n, o) - 1)
for(i in 1:nrow(m)){
dist[n + i, 1] = (dist[m[i, 1], 1] + dist[m[i, 2], 1]) / 2
dist[n + i, 2] = h[i]
}
draw_connection = function(x1, x2, y1, y2, y){
grid.lines(x = c(x1, x1), y = c(y1, y))
grid.lines(x = c(x2, x2), y = c(y2, y))
grid.lines(x = c(x1, x2), y = c(y, y))
}
if(horizontal){
for(i in 1:nrow(m)){
draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
}
}
else{
gr = rectGrob()
pushViewport(viewport(height = unit(1, "grobwidth", gr), width = unit(1, "grobheight", gr), angle = 90))
dist[, 1] = 1 - dist[, 1]
for(i in 1:nrow(m)){
draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
}
upViewport()
}
}
draw_matrix = function(matrix, border_color, fmat, fontsize_number,vp){
######
## modified to use raster to draw imagemaps
######
#n = nrow(matrix)
#m = ncol(matrix)
#x = (1:m)/m - 1/2/m
#y = 1 - ((1:n)/n - 1/2/n)
grid.raster(matrix,height=unit(1, "npc"),width=unit(1, "npc"),vp=vp,interpolate=FALSE)
# for(i in 1:m){
#grid.rect(x = x[i], y = y[1:n], width = 1/m, height = 1/n, gp = gpar(fill = matrix[,i], col = border_color))
# if(attr(fmat, "draw")){
# grid.text(x = x[i], y = y[1:n], label = fmat[, i], gp = gpar(col = "grey30", fontsize = fontsize_number))
# }
# }
}
draw_colnames = function(coln, ...){
m = length(coln)
x = (1:m)/m - 1/2/m
grid.text(coln, x = x, y = unit(0.90, "npc"), vjust = 0.5, hjust = 0, rot = 270, gp = gpar(...))
}
draw_rownames = function(rown, ...){
n = length(rown)
y = 1 - ((1:n)/n - 1/2/n)
grid.text(rown, x = unit(0.04, "npc"), y = y, vjust = 0.5, hjust = 0, gp = gpar(...))
}
draw_legend = function(color, breaks, legend, ...){
height = min(unit(1, "npc"), unit(150, "bigpts"))
pushViewport(viewport(x = 0, y = unit(1, "npc"), just = c(0, 1), height = height))
legend_pos = (legend - min(breaks)) / (max(breaks) - min(breaks))
breaks = (breaks - min(breaks)) / (max(breaks) - min(breaks))
h = breaks[-1] - breaks[-length(breaks)]
grid.rect(x = 0, y = breaks[-length(breaks)], width = unit(10, "bigpts"), height = h, hjust = 0, vjust = 0, gp = gpar(fill = color, col = "#FFFFFF00"))
grid.text(names(legend), x = unit(12, "bigpts"), y = legend_pos, hjust = 0, gp = gpar(...))
upViewport()
}
convert_annotations = function(annotation, annotation_colors){
new = annotation
for(i in 1:ncol(annotation)){
a = annotation[, i]
b = annotation_colors[[colnames(annotation)[i]]]
if(is.character(a) | is.factor(a)){
a = as.character(a)
if(length(setdiff(a, names(b))) > 0){
stop(sprintf("Factor levels on variable %s do not match with annotation_colors", colnames(annotation)[i]))
}
new[, i] = b[a]
}
else{
a = cut(a, breaks = 100)
new[, i] = colorRampPalette(b)(100)[a]
}
}
return(as.matrix(new))
}
draw_annotations = function(converted_annotations, border_color){
n = ncol(converted_annotations)
m = nrow(converted_annotations)
x = (1:m)/m - 1/2/m
y = cumsum(rep(8, n)) - 4 + cumsum(rep(2, n))
for(i in 1:m){
grid.rect(x = x[i], unit(y[1:n], "bigpts"), width = 1/m, height = unit(8, "bigpts"), gp = gpar(fill = converted_annotations[i, ], col = border_color))
}
}
draw_annotation_legend = function(annotation, annotation_colors, border_color, ...){
y = unit(1, "npc")
text_height = unit(1, "grobheight", textGrob("FGH", gp = gpar(...)))
for(i in names(annotation_colors)){
grid.text(i, x = 0, y = y, vjust = 1, hjust = 0, gp = gpar(fontface = "bold", ...))
y = y - 1.5 * text_height
if(is.character(annotation[, i]) | is.factor(annotation[, i])){
for(j in 1:length(annotation_colors[[i]])){
grid.rect(x = unit(0, "npc"), y = y, hjust = 0, vjust = 1, height = text_height, width = text_height, gp = gpar(col = border_color, fill = annotation_colors[[i]][j]))
grid.text(names(annotation_colors[[i]])[j], x = text_height * 1.3, y = y, hjust = 0, vjust = 1, gp = gpar(...))
y = y - 1.5 * text_height
}
}
else{
yy = y - 4 * text_height + seq(0, 1, 0.02) * 4 * text_height
h = 4 * text_height * 0.02
grid.rect(x = unit(0, "npc"), y = yy, hjust = 0, vjust = 1, height = h, width = text_height, gp = gpar(col = "#FFFFFF00", fill = colorRampPalette(annotation_colors[[i]])(50)))
txt = rev(range(grid.pretty(range(annotation[, i], na.rm = TRUE))))
yy = y - c(0, 3) * text_height
grid.text(txt, x = text_height * 1.3, y = yy, hjust = 0, vjust = 1, gp = gpar(...))
y = y - 4.5 * text_height
}
y = y - 1.5 * text_height
}
}
draw_main = function(text, ...){
grid.text(text, gp = gpar(fontface = "bold", ...))
}
vplayout = function(x, y){
return(viewport(layout.pos.row = x, layout.pos.col = y))
}
heatmap_motor = function(matrix, border_color, cellwidth, cellheight, tree_col, tree_row,
treeheight_col, treeheight_row, filename, width, height, breaks, color, legend,
annotation, annotation_colors, annotation_legend, main, fontsize, fontsize_row,
fontsize_col, fmat, fontsize_number, useRaster, drawRowD,
explicit_rownames=NULL, ...){
grid.newpage()
# Set layout
mindim = lo(coln = colnames(matrix), rown = rownames(matrix), nrow = nrow(matrix),
ncol = ncol(matrix), cellwidth = cellwidth, cellheight = cellheight,
treeheight_col = treeheight_col, treeheight_row = treeheight_row,
legend = legend, annotation = annotation, annotation_colors = annotation_colors,
annotation_legend = annotation_legend, main = main, fontsize = fontsize, fontsize_row = fontsize_row,
fontsize_col = fontsize_col, ...)
if(!is.na(filename)){
pushViewport(vplayout(1:5, 1:5))
if(is.na(height)){
height = convertHeight(unit(0:1, "npc"), "inches", valueOnly = T)[2]
}
if(is.na(width)){
width = convertWidth(unit(0:1, "npc"), "inches", valueOnly = T)[2]
}
# Get file type
r = regexpr("\\.[a-zA-Z]*$", filename)
if(r == -1) stop("Improper filename")
ending = substr(filename, r + 1, r + attr(r, "match.length"))
f = switch(ending,
pdf = function(x, ...) pdf(x, ...),
png = function(x, ...) png(x, units = "in", res = 300, ...),
jpeg = function(x, ...) jpeg(x, units = "in", res = 300, ...),
jpg = function(x, ...) jpeg(x, units = "in", res = 300, ...),
tiff = function(x, ...) tiff(x, units = "in", res = 300, compression = "lzw", ...),
bmp = function(x, ...) bmp(x, units = "in", res = 300, ...),
stop("File type should be: pdf, png, bmp, jpg, tiff")
)
# print(sprintf("height:%f width:%f", height, width))
f(filename, height = height, width = width)
heatmap_motor(matrix, cellwidth = cellwidth, cellheight = cellheight,
border_color = border_color, tree_col = tree_col, tree_row = tree_row,
treeheight_col = treeheight_col, treeheight_row = treeheight_row, breaks = breaks,
color = color, legend = legend, annotation = annotation, annotation_colors = annotation_colors,
annotation_legend = annotation_legend, filename = NA, main = main, fontsize = fontsize,
fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat,
fontsize_number = fontsize_number, useRaster = useRaster, drawRowD = drawRowD,
explicit_rownames=NULL, ...)
dev.off()
upViewport()
return()
}
# Omit border color if cell size is too small
if(mindim < 3) border_color = NA
# Draw title
if(!is.na(main)){
pushViewport(vplayout(1, 2))
draw_main(main, fontsize = 1.3 * fontsize, ...)
upViewport()
}
# Draw tree for the columns
if(!is.na(tree_col[[1]][1]) & treeheight_col != 0){
pushViewport(vplayout(2, 2))
draw_dendrogram(tree_col, horizontal = T)
upViewport()
}
# Draw tree for the rows
if( drawRowD == TRUE | nrow(matrix) <= 200 ){
if(!is.na(tree_row[[1]][1]) & treeheight_row != 0 ){
pushViewport(vplayout(4, 1))
draw_dendrogram(tree_row, horizontal = F)
upViewport()
}
}
# Draw matrix
vp = vplayout(4, 2)
draw_matrix(matrix, border_color, fmat, fontsize_number,vp=vp)
pushViewport(vp)
upViewport()
#Draw colnames
if(length(colnames(matrix)) != 0){
pushViewport(vplayout(5, 2))
pars = list(colnames(matrix), fontsize = fontsize_col, ...)
do.call(draw_colnames, pars)
upViewport()
}
#Draw rownames
if(length(rownames(matrix)) != 0 ){
if(is.null(explicit_rownames) == 'TRUE'){
explicit_rownames = rownames(matrix)
}
pushViewport(vplayout(4, 3))
pars = list(explicit_rownames, fontsize = fontsize_row, ...)
do.call(draw_rownames, pars)
upViewport()
}
# Draw annotation tracks
if(!is.na(annotation[[1]][1])){
pushViewport(vplayout(3, 2))
converted_annotation = convert_annotations(annotation, annotation_colors)
draw_annotations(converted_annotation, border_color)
upViewport()
}
# Draw annotation legend
if(!is.na(annotation[[1]][1]) & annotation_legend){
if( length(rownames(matrix)) <= 70 ) {
pushViewport(vplayout(4:5, 5))
}
else{
pushViewport(vplayout(3:5, 5))
}
draw_annotation_legend(annotation, annotation_colors, border_color, fontsize = fontsize, ...)
upViewport()
}
# Draw legend IF not drawing the ROW names
if(!is.na(legend[1])){
length(colnames(matrix))
if(length(rownames(matrix)) <= 70 ){
# pushViewport(vplayout(4:5, 4))
# DO NOTHING
}
else{
pushViewport(vplayout(3:5, 4))
draw_legend(color, breaks, legend, fontsize = fontsize, ...)
upViewport()
}
}
}
generate_breaks = function(x, n, center = F){
if(center){
m = max(abs(c(min(x, na.rm = T), max(x, na.rm = T))))
res = seq(-m, m, length.out = n + 1)
}
else{
res = seq(min(x, na.rm = T), max(x, na.rm = T), length.out = n + 1)
}
return(res)
}
scale_vec_colours = function(x, col = rainbow(10), breaks = NA){
return(col[as.numeric(cut(x, breaks = breaks, include.lowest = T))])
}
scale_colours = function(mat, col = rainbow(10), breaks = NA){
mat = as.matrix(mat)
return(matrix(scale_vec_colours(as.vector(mat), col = col, breaks = breaks), nrow(mat), ncol(mat), dimnames = list(rownames(mat), colnames(mat))))
}
cluster_mat = function(mat, distance, method, cor_method){
if(!(method %in% c("ward", "single", "complete", "average", "mcquitty", "median", "centroid"))){
stop("clustering method has to one form the list: 'ward', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.")
}
if(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) & class(distance) != "dist"){
print(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) | class(distance) != "dist")
stop("distance has to be a dissimilarity structure as produced by dist or one measure form the list: 'correlation', 'euclidean', 'maximum', 'manhattan', 'canberra', 'binary', 'minkowski'")
}
if(distance[1] == "correlation"){
d = as.dist(1 - cor(t(mat),method=cor_method))
}
else{
if(class(distance) == "dist"){
d = distance
}
else{
d = dist(mat, method = distance)
}
}
return(flashClust(d, method = method)) #hclust replaced by flashClust from WCGNA (much faster than hclust)
}
#for faster rendering caching the computationally expensive functions
memoised_cluster_mat <- memoise(function(mat, distance, method, cor_method) cluster_mat(mat, distance, method,cor_method))
scale_rows = function(x){
m = apply(x, 1, mean, na.rm = T)
s = apply(x, 1, sd, na.rm = T)
return((x - m) / s)
}
scale_mat = function(mat, scale){
if(!(scale %in% c("none", "row", "column"))){
stop("scale argument shoud take values: 'none', 'row' or 'column'")
}
mat = switch(scale, none = mat, row = scale_rows(mat), column = t(scale_rows(t(mat))))
return(mat)
}
generate_annotation_colours = function(annotation, annotation_colors, drop){
if(is.na(annotation_colors)[[1]][1]){
annotation_colors = list()
}
count = 0
for(i in 1:ncol(annotation)){
if(is.character(annotation[, i]) | is.factor(annotation[, i])){
if (is.factor(annotation[, i]) & !drop){
count = count + length(levels(annotation[, i]))
}
else{
count = count + length(unique(annotation[, i]))
}
}
}
factor_colors = hsv((seq(0, 1, length.out = count + 1)[-1] +
0.2)%%1, 0.7, 0.95)
set.seed(3453)
for(i in 1:ncol(annotation)){
if(!(colnames(annotation)[i] %in% names(annotation_colors))){
if(is.character(annotation[, i]) | is.factor(annotation[, i])){
n = length(unique(annotation[, i]))
if (is.factor(annotation[, i]) & !drop){
n = length(levels(annotation[, i]))
}
ind = sample(1:length(factor_colors), n)
annotation_colors[[colnames(annotation)[i]]] = factor_colors[ind]
l = levels(as.factor(annotation[, i]))
l = l[l %in% unique(annotation[, i])]
if (is.factor(annotation[, i]) & !drop){
l = levels(annotation[, i])
}
names(annotation_colors[[colnames(annotation)[i]]]) = l
factor_colors = factor_colors[-ind]
}
else{
r = runif(1)
annotation_colors[[colnames(annotation)[i]]] = hsv(r, c(0.1, 1), 1)
}
}
}
return(annotation_colors)
}
kmeans_pheatmap = function(mat, k = min(nrow(mat), 150), sd_limit = NA, ...){
# Filter data
if(!is.na(sd_limit)){
s = apply(mat, 1, sd)
mat = mat[s > sd_limit, ]
}
# Cluster data
set.seed(1245678)
km = kmeans(mat, k, iter.max = 100)
mat2 = km$centers
# Compose rownames
t = table(km$cluster)
rownames(mat2) = sprintf("cl%s_size_%d", names(t), t)
# Draw heatmap
pheatmap(mat2, ...)
}
#' A function to draw clustered heatmaps.
#'
#' A function to draw clustered heatmaps where one has better control over some graphical
#' parameters such as cell size, etc.
#'
#' The function also allows to aggregate the rows using kmeans clustering. This is
#' advisable if number of rows is so big that R cannot handle their hierarchical
#' clustering anymore, roughly more than 1000. Instead of showing all the rows
#' separately one can cluster the rows in advance and show only the cluster centers.
#' The number of clusters can be tuned with parameter kmeans_k.
#'
#' @param mat numeric matrix of the values to be plotted.
#' @param color vector of colors used in heatmap.
#' @param kmeans_k the number of kmeans clusters to make, if we want to agggregate the
#' rows before drawing heatmap. If NA then the rows are not aggregated.
#' @param breaks a sequence of numbers that covers the range of values in mat and is one
#' element longer than color vector. Used for mapping values to colors. Useful, if needed
#' to map certain values to certain colors, to certain values. If value is NA then the
#' breaks are calculated automatically.
#' @param border_color color of cell borders on heatmap, use NA if no border should be
#' drawn.
#' @param cellwidth individual cell width in points. If left as NA, then the values
#' depend on the size of plotting window.
#' @param cellheight individual cell height in points. If left as NA,
#' then the values depend on the size of plotting window.
#' @param scale character indicating if the values should be centered and scaled in
#' either the row direction or the column direction, or none. Corresponding values are
#' \code{"row"}, \code{"column"} and \code{"none"}
#' @param cluster_rows boolean values determining if rows should be clustered,
#' @param cluster_cols boolean values determining if columns should be clustered.
#' @param clustering_distance_rows distance measure used in clustering rows. Possible
#' values are \code{"correlation"} for Pearson correlation and all the distances
#' supported by \code{\link{dist}}, such as \code{"euclidean"}, etc. If the value is none
#' of the above it is assumed that a distance matrix is provided.
#' @param clustering_distance_cols distance measure used in clustering columns. Possible
#' values the same as for clustering_distance_rows.
#' @param clustering_method clustering method used. Accepts the same values as
#' \code{\link{hclust}}.
#' @param treeheight_row the height of a tree for rows, if these are clustered.
#' Default value 50 points.
#' @param treeheight_col the height of a tree for columns, if these are clustered.
#' Default value 50 points.
#' @param legend logical to determine if legend should be drawn or not.
#' @param legend_breaks vector of breakpoints for the legend.
#' @param legend_labels vector of labels for the \code{legend_breaks}.
#' @param annotation data frame that specifies the annotations shown on top of the
#' columns. Each row defines the features for a specific column. The columns in the data
#' and rows in the annotation are matched using corresponding row and column names. Note
#' that color schemes takes into account if variable is continuous or discrete.
#' @param annotation_colors list for specifying annotation track colors manually. It is
#' possible to define the colors for only some of the features. Check examples for
#' details.
#' @param annotation_legend boolean value showing if the legend for annotation tracks
#' should be drawn.
#' @param drop_levels logical to determine if unused levels are also shown in the legend
#' @param show_rownames boolean specifying if column names are be shown.
#' @param show_colnames boolean specifying if column names are be shown.
#' @param main the title of the plot
#' @param fontsize base fontsize for the plot
#' @param fontsize_row fontsize for rownames (Default: fontsize)
#' @param fontsize_col fontsize for colnames (Default: fontsize)
#' @param display_numbers logical determining if the numeric values are also printed to
#' the cells.
#' @param number_format format strings (C printf style) of the numbers shown in cells.
#' For example "\code{\%.2f}" shows 2 decimal places and "\code{\%.1e}" shows exponential
#' notation (see more in \code{\link{sprintf}}).
#' @param fontsize_number fontsize of the numbers displayed in cells
#' @param filename file path where to save the picture. Filetype is decided by
#' the extension in the path. Currently following formats are supported: png, pdf, tiff,
#' bmp, jpeg. Even if the plot does not fit into the plotting window, the file size is
#' calculated so that the plot would fit there, unless specified otherwise.
#' @param width manual option for determining the output file width in inches.
#' @param height manual option for determining the output file height in inches.
#' @param \dots graphical parameters for the text used in plot. Parameters passed to
#' \code{\link{grid.text}}, see \code{\link{gpar}}.
#'
#' @return
#' Invisibly a list of components
#' \itemize{
#' \item \code{tree_row} the clustering of rows as \code{\link{hclust}} object
#' \item \code{tree_col} the clustering of columns as \code{\link{hclust}} object
#' \item \code{kmeans} the kmeans clustering of rows if parameter \code{kmeans_k} was
#' specified
#' }
#'
#' @author Raivo Kolde <rkolde@@gmail.com>
#' @examples
#' # Generate some data
#' test = matrix(rnorm(200), 20, 10)
#' test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
#' test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
#' test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
#' colnames(test) = paste("Test", 1:10, sep = "")
#' rownames(test) = paste("Gene", 1:20, sep = "")
#'
#' # Draw heatmaps
#' pheatmap(test)
#' pheatmap(test, kmeans_k = 2)
#' pheatmap(test, scale = "row", clustering_distance_rows = "correlation")
#' pheatmap(test, color = colorRampPalette(c("navy", "white", "firebrick3"))(50))
#' pheatmap(test, cluster_row = FALSE)
#' pheatmap(test, legend = FALSE)
#' pheatmap(test, display_numbers = TRUE)
#' pheatmap(test, display_numbers = TRUE, number_format = "%.1e")
#' pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0",
#' "1e-4", "1e-3", "1e-2", "1e-1", "1"))
#' pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")
#' pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")
#'
#'
#' # Generate column annotations
#' annotation = data.frame(Var1 = factor(1:10 %% 2 == 0,
#' labels = c("Class1", "Class2")), Var2 = 1:10)
#' annotation$Var1 = factor(annotation$Var1, levels = c("Class1", "Class2", "Class3"))
#' rownames(annotation) = paste("Test", 1:10, sep = "")
#'
#' pheatmap(test, annotation = annotation)
#' pheatmap(test, annotation = annotation, annotation_legend = FALSE)
#' pheatmap(test, annotation = annotation, annotation_legend = FALSE, drop_levels = FALSE)
#'
#' # Specify colors
#' Var1 = c("navy", "darkgreen")
#' names(Var1) = c("Class1", "Class2")
#' Var2 = c("lightgreen", "navy")
#'
#' ann_colors = list(Var1 = Var1, Var2 = Var2)
#'
#' pheatmap(test, annotation = annotation, annotation_colors = ann_colors, main = "Example")
#'
#' # Specifying clustering from distance matrix
#' drows = dist(test, method = "minkowski")
#' dcols = dist(t(test), method = "minkowski")
#' pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)
#'
#' @export
memoised_pheatmap = function(mat, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
kmeans_k = NA, breaks = NA, border_color = "grey60", cellwidth = NA,
cellheight = NA, scale = "none", cluster_rows = TRUE, cluster_cols = TRUE,
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
clustering_method = "complete", treeheight_row = ifelse(cluster_rows, 50, 0),
treeheight_col = ifelse(cluster_cols, 50, 0), legend = TRUE, legend_breaks = NA,
legend_labels = NA, annotation = NA, annotation_colors = NA, annotation_legend = TRUE,
drop_levels = TRUE, show_rownames = T, show_colnames = T, main = NA, fontsize = 10,
fontsize_row = fontsize, fontsize_col = fontsize, display_numbers = F, number_format = "%.2f",
fontsize_number = 0.8 * fontsize, filename = NA, width = NA, height = NA,
useRaster=FALSE, drawRowD=TRUE, cor_method = "pearson",
explicit_rownames = NULL, ...){
#time at which process started
start_time = proc.time()
# Preprocess matrix
mat = as.matrix(mat)
if(scale != "none"){
mat = scale_mat(mat, scale)
if(is.na(breaks)){
breaks = generate_breaks(mat, length(color), center = T)
}
}
# Kmeans
if(!is.na(kmeans_k)){
# Cluster data
km = kmeans(mat, kmeans_k, iter.max = 100)
mat = km$centers
# Compose rownames
t = table(km$cluster)
rownames(mat) = sprintf("cl%s_size_%d", names(t), t)
}
else{
km = NA
}
# Do clustering
if(cluster_rows){
tree_row = memoised_cluster_mat(mat, distance = clustering_distance_rows, method = clustering_method, cor_method=cor_method)
#tree_row = cluster_mat(mat, distance = clustering_distance_rows, method = clustering_method)
mat = mat[tree_row$order, , drop = FALSE]
if(is.null(explicit_rownames) == FALSE){
explicit_rownames = explicit_rownames[tree_row$order]
}
}
else{
tree_row = NA
treeheight_row = 0
}
if(cluster_cols){
tree_col = memoised_cluster_mat(t(mat), distance = clustering_distance_cols, method = clustering_method, cor_method=cor_method)
#tree_col = cluster_mat(t(mat), distance = clustering_distance_cols, method = clustering_method)
mat = mat[, tree_col$order, drop = FALSE]
}
else{
tree_col = NA
treeheight_col = 0
}
# Format numbers to be displayed in cells
if(display_numbers){
fmat = matrix(sprintf(number_format, mat), nrow = nrow(mat), ncol = ncol(mat))
attr(fmat, "draw") = TRUE
}
else{
fmat = matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
attr(fmat, "draw") = FALSE
}
# Colors and scales
if(!is.na(legend_breaks[1]) & !is.na(legend_labels[1])){
if(length(legend_breaks) != length(legend_labels)){
stop("Lengths of legend_breaks and legend_labels must be the same")
}
}
if(is.na(breaks[1])){
breaks = generate_breaks(as.vector(mat), length(color))
}
if (legend & is.na(legend_breaks[1])) {
legend = grid.pretty(range(as.vector(breaks)))
names(legend) = legend
}
else if(legend & !is.na(legend_breaks[1])){
legend = legend_breaks[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
if(!is.na(legend_labels[1])){
legend_labels = legend_labels[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
names(legend) = legend_labels
}
else{
names(legend) = legend
}
}
else {
legend = NA
}
mat = scale_colours(mat, col = color, breaks = breaks)
# Preparing annotation colors
if(!is.na(annotation[[1]][1])){
annotation = annotation[colnames(mat), , drop = F]
annotation_colors = generate_annotation_colours(annotation, annotation_colors, drop = drop_levels)
}
if(!show_rownames){
rownames(mat) = NULL
}
if(!show_colnames){
colnames(mat) = NULL
}
# Draw heatmap
heatmap_motor(mat, border_color = border_color, cellwidth = cellwidth, cellheight = cellheight,
treeheight_col = treeheight_col, treeheight_row = treeheight_row, tree_col = tree_col,
tree_row = tree_row, filename = filename, width = width, height = height, breaks = breaks,
color = color, legend = legend, annotation = annotation, annotation_colors = annotation_colors,
annotation_legend = annotation_legend, main = main, fontsize = fontsize,
fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat,
fontsize_number = fontsize_number, useRaster=useRaster, drawRowD=drawRowD,
explicit_rownames = explicit_rownames, ...)
#end time
end_time = proc.time()
total_time = end_time - start_time
invisible(list(tree_row = tree_row, tree_col = tree_col, kmeans = km, time=total_time))
}
#######################
#Main Wrapper function
#######################
#' @export
apHeatMap <- function(m, annotation = NA ,
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
cor_method="spearman",
clustering_method = "average",
scale = FALSE,...){
if(nrow(m) <= 2){
memoised_pheatmap(m, cluster_rows=FALSE,
scale="none",
annotation = annotation,
drawRowD = FALSE,
border_color = NA,...)
}
else{
#do the clustering and heatmap
#scaling genes across experiments
if(scale == "TRUE"){
m <- t(scale(t(m)))
}
memoised_pheatmap(m,
scale="none",
annotation = annotation,
clustering_distance_rows = clustering_distance_rows,
clustering_distance_cols = clustering_distance_cols,
clustering_method = clustering_method,
border_color = NA,
drawRowD = FALSE,
cor_method=cor_method,...)
}
}
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