R/semi_pheatmap.R
In compbiomed/celda: CEllular Latent Dirichlet Allocation
Defines functions
lo
find_coordinates
draw_dendrogram
draw_matrix
draw_colnames
draw_rownames
draw_legend
convert_annotations
draw_annotations
draw_annotation_names
draw_annotation_legend
draw_main
vplayout
heatmap_motor
generate_breaks
scale_vec_colours
scale_colours
cluster_mat
scale_rows
scale_mat
generate_annotation_colours
kmeans_pheatmap
find_gaps
is.na2
identity2
semi_pheatmap
Documented in
semi_pheatmap
# Adapted originally from the very excellent pheatmap package
# (https://cran.r-project.org/web/packages/pheatmap/index.html)
lo = function(rown, coln, nrow, ncol, cellheight = NA, cellwidth = NA, treeheight_col, treeheight_row, legend, annotation_row, annotation_col, annotation_colors, annotation_legend, annotation_names_row, annotation_names_col, main, fontsize, fontsize_row, fontsize_col, gaps_row, gaps_col, ...){
# Get height of colnames and length of rownames
if(!is.null(coln[1]) | (!is.na2(annotation_row) & annotation_names_row)){
if(!is.null(coln[1])){
t = coln
} else {
t = ""
}
tw = strwidth(t, units = 'in', cex = fontsize_col / fontsize)
if(annotation_names_row){
t = c(t, colnames(annotation_row))
tw = c(tw, strwidth(colnames(annotation_row), units = 'in'))
}
longest_coln = which.max(tw)
gp = list(fontsize = ifelse(longest_coln <= length(coln), fontsize_col, fontsize), ...)
coln_height = unit(1, "grobheight", textGrob(t[longest_coln], rot = 90, gp = do.call(gpar, gp))) + unit(10, "bigpts")
}
else{
coln_height = unit(5, "bigpts")
}
if(!is.null(rown[1])){
t = rown
tw = strwidth(t, units = 'in', cex = fontsize_row / fontsize)
if(annotation_names_col){
t = c(t, colnames(annotation_col))
tw = c(tw, strwidth(colnames(annotation_col), units = 'in'))
}
longest_rown = which.max(tw)
gp = list(fontsize = ifelse(longest_rown <= length(rown), fontsize_row, fontsize), ...)
rown_width = unit(1, "grobwidth", textGrob(t[longest_rown], gp = do.call(gpar, gp))) + unit(10, "bigpts")
}
else{
rown_width = unit(5, "bigpts")
}
gp = list(fontsize = fontsize, ...)
# Legend position
if(!is.na2(legend)){
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
textheight = unit(fontsize, "bigpts")
if(!is.na2(annotation_col)){
# Column annotation height
annot_col_height = ncol(annotation_col) * (textheight + unit(2, "bigpts")) + unit(2, "bigpts")
# Width of the correponding legend
t = c(as.vector(as.matrix(annotation_col)), colnames(annotation_col))
annot_col_legend_width = unit(1.2, "grobwidth", textGrob(t[which.max(nchar(t))], gp = gpar(...))) + unit(12, "bigpts")
if(!annotation_legend){
annot_col_legend_width = unit(0, "npc")
}
}
else{
annot_col_height = unit(0, "bigpts")
annot_col_legend_width = unit(0, "bigpts")
}
# Row annotations
if(!is.na2(annotation_row)){
# Row annotation width
annot_row_width = ncol(annotation_row) * (textheight + unit(2, "bigpts")) + unit(2, "bigpts")
# Width of the correponding legend
t = c(as.vector(as.matrix(annotation_row)), colnames(annotation_row))
annot_row_legend_width = unit(1.2, "grobwidth", textGrob(t[which.max(nchar(t))], gp = gpar(...))) + unit(12, "bigpts")
if(!annotation_legend){
annot_row_legend_width = unit(0, "npc")
}
}
else{
annot_row_width = unit(0, "bigpts")
annot_row_legend_width = unit(0, "bigpts")
}
annot_legend_width = max(annot_row_legend_width, annot_col_legend_width)
# 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)){
mat_width = unit(1, "npc") - rown_width - legend_width - treeheight_row - annot_row_width - annot_legend_width
}
else{
mat_width = unit(cellwidth * ncol, "bigpts") + length(gaps_col) * unit(0, "bigpts")
}
if(is.na(cellheight)){
mat_height = unit(1, "npc") - main_height - coln_height - treeheight_col - annot_col_height
}
else{
mat_height = unit(cellheight * nrow, "bigpts") + length(gaps_row) * unit(0, "bigpts")
}
# Produce gtable
gt = gtable(widths = unit.c(treeheight_row, annot_row_width, mat_width, rown_width, legend_width, annot_legend_width), heights = unit.c(main_height, treeheight_col, annot_col_height, mat_height, coln_height), vp = viewport(gp = do.call(gpar, gp)))
cw = convertWidth(mat_width - (length(gaps_col) * unit(0, "bigpts")), "bigpts", valueOnly = TRUE) / ncol
ch = convertHeight(mat_height - (length(gaps_row) * unit(0, "bigpts")), "bigpts", valueOnly = TRUE) / nrow
# Return minimal cell dimension in bigpts to decide if borders are drawn
mindim = min(cw, ch)
res = list(gt = gt, mindim = mindim)
return(res)
}
find_coordinates = function(n, gaps, m = 1:n){
if(length(gaps) == 0){
return(list(coord = unit(m / n, "npc"), size = unit(1 / n, "npc") ))
}
if(max(gaps) > n){
stop("Gaps do not match with matrix size")
}
size = (1 / n) * (unit(1, "npc") - length(gaps) * unit("0", "bigpts"))
gaps2 = base::apply(sapply(gaps, function(gap, x){x > gap}, m), 1, sum)
coord = m * size + (gaps2 * unit("0", "bigpts"))
return(list(coord = coord, size = size))
}
draw_dendrogram = function(hc, gaps, horizontal = TRUE){
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){
res = list(
x = c(x1, x1, x2, x2),
y = c(y1, y, y, y2)
)
return(res)
}
x = rep(NA, nrow(m) * 4)
y = rep(NA, nrow(m) * 4)
id = rep(1:nrow(m), rep(4, nrow(m)))
for(i in 1:nrow(m)){
c = draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
k = (i - 1) * 4 + 1
x[k : (k + 3)] = c$x
y[k : (k + 3)] = c$y
}
x = find_coordinates(n, gaps, x * n)$coord
y = unit(y, "npc")
if(!horizontal){
a = x
x = unit(1, "npc") - y
y = unit(1, "npc") - a
}
res = polylineGrob(x = x, y = y, id = id)
return(res)
}
draw_matrix = function(matrix, border_color, gaps_rows, gaps_cols, fmat, fontsize_number, number_color){
n = nrow(matrix)
m = ncol(matrix)
coord_x = find_coordinates(m, gaps_cols)
coord_y = find_coordinates(n, gaps_rows)
x = coord_x$coord - 0.5 * coord_x$size
y = unit(1, "npc") - (coord_y$coord - 0.5 * coord_y$size)
coord = expand.grid(y = y, x = x)
res = gList()
res[["rect"]] = rectGrob(x = coord$x, y = coord$y, width = coord_x$size, height = coord_y$size, gp = gpar(fill = matrix, col = border_color))
if(attr(fmat, "draw")){
res[["text"]] = textGrob(x = coord$x, y = coord$y, label = fmat, gp = gpar(col = number_color, fontsize = fontsize_number))
}
res = gTree(children = res)
return(res)
}
draw_colnames = function(coln, gaps, ...){
coord = find_coordinates(length(coln), gaps)
x = coord$coord - 0.5 * coord$size
res = textGrob(coln, x = x, y = unit(1, "npc") - unit(3, "bigpts"), vjust = 0.5, hjust = 0, rot = 270, gp = gpar(...))
return(res)
}
draw_rownames = function(rown, gaps, ...){
coord = find_coordinates(length(rown), gaps)
y = unit(1, "npc") - (coord$coord - 0.5 * coord$size)
res = textGrob(rown, x = unit(3, "bigpts"), y = y, vjust = 0.5, hjust = 0, gp = gpar(...))
return(res)
}
draw_legend = function(color, breaks, legend, ...){
height = min(unit(1, "npc"), unit(150, "bigpts"))
legend_pos = (legend - min(breaks)) / (max(breaks) - min(breaks))
legend_pos = height * legend_pos + (unit(1, "npc") - height)
breaks = (breaks - min(breaks)) / (max(breaks) - min(breaks))
breaks = height * breaks + (unit(1, "npc") - height)
h = breaks[-1] - breaks[-length(breaks)]
rect = rectGrob(x = 0, y = breaks[-length(breaks)], width = unit(10, "bigpts"), height = h, hjust = 0, vjust = 0, gp = gpar(fill = color, col = "#FFFFFF00"))
text = textGrob(names(legend), x = unit(14, "bigpts"), y = legend_pos, hjust = 0, gp = gpar(...))
res = grobTree(rect, text)
return(res)
}
convert_annotations = function(annotation, annotation_colors){
new = annotation
for(i in 1:ncol(annotation)){
a = annotation[, i]
b = annotation_colors[[colnames(annotation)[i]]]
if(is.character(a) | is.factor(a)){
a = as.character(a)
if(length(setdiff(setdiff(a, NA), names(b))) > 0){
stop(sprintf("Factor levels on variable %s do not match with annotation_colors", colnames(annotation)[i]))
}
new[, i] = b[a]
}
else{
a = cut(a, breaks = 100)
new[, i] = colorRampPalette(b)(100)[a]
}
}
return(as.matrix(new))
}
draw_annotations = function(converted_annotations, border_color, gaps, fontsize, horizontal){
n = ncol(converted_annotations)
m = nrow(converted_annotations)
coord_x = find_coordinates(m, gaps)
x = coord_x$coord - 0.5 * coord_x$size
# y = cumsum(rep(fontsize, n)) - 4 + cumsum(rep(2, n))
y = cumsum(rep(fontsize, n)) + cumsum(rep(2, n)) - fontsize / 2 + 1
y = unit(y, "bigpts")
if(horizontal){
coord = expand.grid(x = x, y = y)
res = rectGrob(x = coord$x, y = coord$y, width = coord_x$size, height = unit(fontsize, "bigpts"), gp = gpar(fill = converted_annotations, col = border_color))
}
else{
a = x
x = unit(1, "npc") - y
y = unit(1, "npc") - a
coord = expand.grid(y = y, x = x)
res = rectGrob(x = coord$x, y = coord$y, width = unit(fontsize, "bigpts"), height = coord_x$size, gp = gpar(fill = converted_annotations, col = border_color))
}
return(res)
}
draw_annotation_names = function(annotations, fontsize, horizontal){
n = ncol(annotations)
x = unit(3, "bigpts")
y = cumsum(rep(fontsize, n)) + cumsum(rep(2, n)) - fontsize / 2 + 1
y = unit(y, "bigpts")
if(horizontal){
res = textGrob(colnames(annotations), x = x, y = y, hjust = 0, gp = gpar(fontsize = fontsize, fontface = 2))
}
else{
a = x
x = unit(1, "npc") - y
y = unit(1, "npc") - a
res = textGrob(colnames(annotations), x = x, y = y, vjust = 0.5, hjust = 0, rot = 270, gp = gpar(fontsize = fontsize, fontface = 2))
}
return(res)
}
draw_annotation_legend = function(annotation, annotation_colors, border_color, ...){
y = unit(1, "npc")
text_height = unit(1, "grobheight", textGrob("FGH", gp = gpar(...)))
res = gList()
for(i in names(annotation)){
res[[i]] = textGrob(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]])){
n = length(annotation_colors[[i]])
yy = y - (1:n - 1) * 2 * text_height
res[[paste(i, "r")]] = rectGrob(x = unit(0, "npc"), y = yy, hjust = 0, vjust = 1, height = 2 * text_height, width = 2 * text_height, gp = gpar(col = border_color, fill = annotation_colors[[i]]))
res[[paste(i, "t")]] = textGrob(names(annotation_colors[[i]]), x = text_height * 2.4, y = yy - text_height, hjust = 0, vjust = 0.5, gp = gpar(...))
y = y - n * 2 * text_height
}
else{
yy = y - 8 * text_height + seq(0, 1, 0.25)[-1] * 8 * text_height
h = 8 * text_height * 0.25
res[[paste(i, "r")]] = rectGrob(x = unit(0, "npc"), y = yy, hjust = 0, vjust = 1, height = h, width = 2 * text_height, gp = gpar(col = NA, fill = colorRampPalette(annotation_colors[[i]])(4)))
res[[paste(i, "r2")]] = rectGrob(x = unit(0, "npc"), y = y, hjust = 0, vjust = 1, height = 8 * text_height, width = 2 * text_height, gp = gpar(col = border_color, fill = NA))
txt = rev(range(grid.pretty(range(annotation[[i]], na.rm = TRUE))))
yy = y - c(1, 7) * text_height
res[[paste(i, "t")]] = textGrob(txt, x = text_height * 2.4, y = yy, hjust = 0, vjust = 0.5, gp = gpar(...))
y = y - 8 * text_height
}
y = y - 1.5 * text_height
}
res = gTree(children = res)
return(res)
}
draw_main = function(text, ...){
res = textGrob(text, gp = gpar(fontface = "bold", ...))
return(res)
}
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_row, annotation_col, annotation_colors, annotation_legend, annotation_names_row, annotation_names_col, main, fontsize, fontsize_row, fontsize_col, fmat, fontsize_number, number_color, gaps_col, gaps_row, labels_row, labels_col, ...){
# Set layout
lo = lo(coln = labels_col, rown = labels_row, nrow = nrow(matrix), ncol = ncol(matrix), cellwidth = cellwidth, cellheight = cellheight, treeheight_col = treeheight_col, treeheight_row = treeheight_row, legend = legend, annotation_col = annotation_col, annotation_row = annotation_row, annotation_colors = annotation_colors, annotation_legend = annotation_legend, annotation_names_row = annotation_names_row, annotation_names_col = annotation_names_col, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, gaps_row = gaps_row, gaps_col = gaps_col, ...)
res = lo$gt
mindim = lo$mindim
if(!is.na(filename)){
if(is.na(height)){
height = convertHeight(gtable_height(res), "inches", valueOnly = TRUE)
}
if(is.na(width)){
width = convertWidth(gtable_width(res), "inches", valueOnly = TRUE)
}
# Get file type
r = regexpr("\\.[a-zA-Z]*$", 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))
# gt = 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_col = annotation_col, annotation_row = annotation_row, 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, number_color = number_color, labels_row = labels_row, labels_col = labels_col, gaps_col = gaps_col, gaps_row = gaps_row, ...)
f(filename, height = height, width = width)
gt = 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_col = annotation_col, annotation_row = annotation_row, annotation_colors = annotation_colors, annotation_legend = annotation_legend, annotation_names_row = annotation_names_row, annotation_names_col = annotation_names_col, filename = NA, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat, fontsize_number = fontsize_number, number_color = number_color, labels_row = labels_row, labels_col = labels_col, gaps_col = gaps_col, gaps_row = gaps_row, ...)
grid.draw(gt)
dev.off()
return(gt)
}
# Omit border color if cell size is too small
if(mindim < 3) border_color = NA
# Draw title
if(!is.na(main)){
elem = draw_main(main, fontsize = 1.3 * fontsize, ...)
res = gtable_add_grob(res, elem, t = 1, l = 3, name = "main", clip = "off")
}
# Draw tree for the columns
if(!is.na2(tree_col) & treeheight_col != 0){
elem = draw_dendrogram(tree_col, gaps_col, horizontal = TRUE)
res = gtable_add_grob(res, elem, t = 2, l = 3, name = "col_tree")
}
# Draw tree for the rows
if(!is.na2(tree_row) & treeheight_row != 0){
elem = draw_dendrogram(tree_row, gaps_row, horizontal = FALSE)
res = gtable_add_grob(res, elem, t = 4, l = 1, name = "row_tree")
}
# Draw matrix
elem = draw_matrix(matrix, border_color, gaps_row, gaps_col, fmat, fontsize_number, number_color)
res = gtable_add_grob(res, elem, t = 4, l = 3, clip = "off", name = "matrix")
# Draw colnames
if(length(labels_col) != 0){
pars = list(labels_col, gaps = gaps_col, fontsize = fontsize_col, ...)
elem = do.call(draw_colnames, pars)
res = gtable_add_grob(res, elem, t = 5, l = 3, clip = "off", name = "col_names")
}
# Draw rownames
if(length(labels_row) != 0){
pars = list(labels_row, gaps = gaps_row, fontsize = fontsize_row, ...)
elem = do.call(draw_rownames, pars)
res = gtable_add_grob(res, elem, t = 4, l = 4, clip = "off", name = "row_names")
}
# Draw annotation tracks on cols
if(!is.na2(annotation_col)){
# Draw tracks
converted_annotation = convert_annotations(annotation_col, annotation_colors)
elem = draw_annotations(converted_annotation, border_color, gaps_col, fontsize, horizontal = TRUE)
res = gtable_add_grob(res, elem, t = 3, l = 3, clip = "off", name = "col_annotation")
# Draw names
if(annotation_names_col){
elem = draw_annotation_names(annotation_col, fontsize, horizontal = TRUE)
res = gtable_add_grob(res, elem, t = 3, l = 4, clip = "off", name = "col_annotation_names")
}
}
# Draw annotation tracks on rows
if(!is.na2(annotation_row)){
# Draw tracks
converted_annotation = convert_annotations(annotation_row, annotation_colors)
elem = draw_annotations(converted_annotation, border_color, gaps_row, fontsize, horizontal = FALSE)
res = gtable_add_grob(res, elem, t = 4, l = 2, clip = "off", name = "row_annotation")
# Draw names
if(annotation_names_row){
elem = draw_annotation_names(annotation_row, fontsize, horizontal = FALSE)
res = gtable_add_grob(res, elem, t = 5, l = 2, clip = "off", name = "row_annotation_names")
}
}
# Draw annotation legend
annotation = c(annotation_col[length(annotation_col):1], annotation_row[length(annotation_row):1])
annotation = annotation[unlist(lapply(annotation, function(x) !is.na2(x)))]
if(length(annotation) > 0 & annotation_legend){
elem = draw_annotation_legend(annotation, annotation_colors, border_color, fontsize = fontsize, ...)
t = ifelse(is.null(labels_row), 4, 3)
res = gtable_add_grob(res, elem, t = t, l = 6, b = 5, clip = "off", name = "annotation_legend")
}
# Draw legend
if(!is.na2(legend)){
elem = draw_legend(color, breaks, legend, fontsize = fontsize, ...)
t = ifelse(is.null(labels_row), 4, 3)
res = gtable_add_grob(res, elem, t = t, l = 5, b = 5, clip = "off", name = "legend")
}
return(res)
}
generate_breaks = function(x, n, center = FALSE){
if(center){
m = max(abs(c(min(x, na.rm = TRUE), max(x, na.rm = TRUE))))
res = seq(-m, m, length.out = n + 1)
}
else{
res = seq(min(x, na.rm = TRUE), max(x, na.rm = TRUE), 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 = TRUE))])
}
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))))
}
## changed the original cluster_mat() in the pheatmap.r
cluster_mat <- function(mat, labels, distance, method){
# this funciton is going to change the cluster_mat() in pheatmap
if(!(method %in% c("ward.D", "ward.D2", "ward", "single", "complete", "average", "mcquitty", "median", "centroid"))){
stop("clustering method has to one form the list: 'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.")
}
class.label <- unique(labels)
nGroup <- length(class.label) # [#group]
# get "hclust" object for each group then wrap them up as group.hclust
# distance function preparation
dis <- function(mat, distance) {
if(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) & !is(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"){
# this part should be confirmed whether being wrong? ToDo: how is the correlation matrix converted to a dsit matrix
d <- stats::as.dist(1 - stats::cor(t(mat)))
}
else{
d <- stats::dist(mat, method = distance)
}
return(d)
}
# initiate the final returning value: a "hclust" object
cum.hclust <- list()
if(nGroup==1){ # matrix has only 1 group
if(length(labels)==1){
stop("only one row/column for the matrix")
}
group.hclust <- stats::hclust(dis(mat = mat, distance = distance), method = method)
cum.hclust <- group.hclust
}else { # matrix has more than 1 groups
group.hclust <- sapply(class.label, function(x) {
class.pos <- which(labels==x) # get the positions of class label
if(length(class.pos)==1){ # if only 1 row in the group return a manually made "hclust" object
sub.hclust <- as.list(1:7)
names(sub.hclust) <- c("merge","height", "order", "labels","method","call","dist.method")
class(sub.hclust) <- "hclust"
sub.hclust$merge <- matrix(c(0,0), nrow = 1)
sub.hclust$height <- 0
sub.hclust$order <- 1
return(sub.hclust)
}else if(length(class.pos)>1){ # if >1 rows return the "hclust" object
return(stats::hclust(dis(mat = mat[class.pos,], distance = distance ),method = method ))
} }
)
# the length(group.hclust) is the [#group] == nGroup , group.hclust[[i]] to get each "hclust"
# then modify the "hclust" object and get them merged into one "hclust" object
cum.hclust <- group.hclust[,nGroup] # initiate the final "hclust" object
# merge function preparation
merge_hclust <- function(hclust1, hclust2){ # "hclust" object modifying function preparation
if(hclust1$merge[1,1]==0 & hclust2$merge[1,1]==0){ # both groups have only 1 row
hclust.com <- as.list(1:7)
names(hclust.com) <- c("merge","height", "order", "labels","method","call","dist.method")
class(hclust.com) <- "hclust"
hclust.com$merge <- matrix(c(-1,-2), nrow = 1)
hclust.com$height <- 1 # check for different matrix whether 1 should be good
hclust.com$order <- c(1,2)
return(hclust.com)
}else if( hclust1$merge[1,1]!=0 & hclust2$merge[1,1]!=0) { # both group have >1 rows
row.1 <- dim(hclust1$merge)[1] # #nodes in the hclust1 group, so actually the #rows shouls be dim()[1]+1
row.2 <- dim(hclust2$merge)[1] # #nodes in the hclust2 group
hclust.com <- list()
mer <- hclust2$merge
hclust.com$merge <- (mer>0) * (mer+row.1) + (mer<0) * (mer-row.1-1) # modify the hclust2$merge matrix
hclust.com$merge <- rbind(hclust1$merge, hclust.com$merge) # combine the merge matrix from the 2 groups
hclust.com$merge <- rbind(hclust.com$merge, c(row.1, row.1+row.2))
hclust.com$height <- c(hclust1$height, hclust2$height)
hclust.com$height <- c(hclust.com$height, max(hclust.com$height)+ 1 ) # check for different matrix whether 1 should be good
hclust.com$order <- c(hclust1$order, hclust2$order+row.1+1)
class(hclust.com) <- "hclust"
}else{ # one group has only 1 row, the other group has >1 rows
if(hclust1$merge[1,1]==0){ # hclust1 has 1 row , hclust2 has >1 rows
row.2 <- dim(hclust2$merge)[1] # #nodes in the hclust2 group
hclust.com <- as.list(1:7)
names(hclust.com) <- c("merge","height", "order", "labels","method","call","dist.method")
class(hclust.com) <- "hclust"
mer <- hclust2$merge
hclust.com$merge <- (mer>0) * (mer) + (mer<0) * (mer-1)
hclust.com$merge <- rbind(hclust.com$merge, c(-1, row.2))
hclust.com$height <- c(hclust2$height, max(hclust2$height)+1) # check for different matrix whether 1 should be good
hclust.com$order <- c(1, hclust2$order+1)
}else if(hclust2$merge[1,1]==0){ # the hclust1 has >1 rows , and hclust2 has 1 row
row.1 <- dim(hclust1$merge)[1] # #nodes in the hclust1 group
hclust.com <- as.list(1:7)
names(hclust.com) <- c("merge","height", "order", "labels","method","call","dist.method")
class(hclust.com) <- "hclust"
hclust.com$merge <- hclust1$merge
hclust.com$merge <- rbind(hclust.com$merge, c(row.1, -(row.1+2)))
hclust.com$height <- c(hclust1$height, max(hclust1$height)+1)
hclust.com$order <- c(hclust1$order, max(hclust1$order)+1)
}
}
return(hclust.com)
}
# merge the "hclust" object into the final one "hclust" object
for(i in seq(nGroup-1, 1,-1)){
cum.hclust <- merge_hclust( group.hclust[,i], cum.hclust)
}
}
cum.hclust$labels <-NULL
cum.hclust$call <- NULL
cum.hclust$method <- NULL
cum.hclust$dist.method <- NULL
return(cum.hclust)
}
scale_rows = function(x){
m = base::apply(x, 1, mean, na.rm = TRUE)
s = base::apply(x, 1, stats::sd, na.rm = TRUE)
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.na2(annotation_colors)){
annotation_colors = list()
}
count = 0
for(i in 1:length(annotation)){
annotation[[i]] = annotation[[i]][!is.na(annotation[[i]])]
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 = dscale(factor(1:count), hue_pal(l = 75))
set.seed(3453)
cont_counter = 2
for(i in 1:length(annotation)){
if(!(names(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[[names(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[[names(annotation)[i]]]) = l
factor_colors = factor_colors[-ind]
}
else{
annotation_colors[[names(annotation)[i]]] = brewer_pal("seq", cont_counter)(5)[1:4]
cont_counter = cont_counter + 1
}
}
}
return(annotation_colors)
}
kmeans_pheatmap = function(mat, k = min(nrow(mat), 150), sd_limit = NA, ...){
# Filter data
if(!is.na(sd_limit)){
s = base::apply(mat, 1, stats::sd)
mat = mat[s > sd_limit, ]
}
# Cluster data
set.seed(1245678)
km = stats::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::pheatmap(mat2, ...)
}
find_gaps = function(tree, cutree_n){
v = stats::cutree(tree, cutree_n)[tree$order]
gaps = which((v[-1] - v[-length(v)]) != 0)
}
is.na2 = function(x){
if(is.list(x) | length(x) > 1){
return(FALSE)
}
if(length(x) == 0){
return(TRUE)
}
return(is.na(x))
}
identity2 = function(x, ...){
return(x)
}
#' 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 Numeric vector. A sequence of numbers that covers the range of values in the normalized `counts`. Values in the normalized `matrix` are assigned to each bin in `breaks`. Each break is assigned to a unique color from `col`. If NULL, then breaks are calculated automatically. Default NULL.
#' @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 or \code{hclust} object,
#' @param cluster_cols boolean values determining if columns should be clustered or \code{hclust} object.
#' @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 clustering_callback callback function to modify the clustering. Is
#' called with two parameters: original \code{hclust} object and the matrix
#' used for clustering. Must return a \code{hclust} object.
#' @param cutree_rows number of clusters the rows are divided into, based on the
#' hierarchical clustering (using cutree), if rows are not clustered, the
#' argument is ignored
#' @param cutree_cols similar to \code{cutree_rows}, but for columns
#' @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_row data frame that specifies the annotations shown on left
#' side of the heatmap. Each row defines the features for a specific row. The
#' rows in the data and in the annotation are matched using corresponding row
#' names. Note that color schemes takes into account if variable is continuous
#' or discrete.
#' @param annotation_col similar to annotation_row, but for columns.
#' @param annotation deprecated parameter that currently sets the annotation_col if it is missing
#' @param annotation_colors list for specifying annotation_row and
#' annotation_col 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 annotation_names_row boolean value showing if the names for row annotation
#' tracks should be drawn.
#' @param annotation_names_col boolean value showing if the names for column 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. If this is a matrix (with same dimensions as original matrix), the contents
#' of the matrix are shown instead of original values.
#' @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 number_color color of the text
#' @param fontsize_number fontsize of the numbers displayed in cells
#' @param gaps_row vector of row indices that show shere to put gaps into
#' heatmap. Used only if the rows are not clustered. See \code{cutree_row}
#' to see how to introduce gaps to clustered rows.
#' @param gaps_col similar to gaps_row, but for columns.
#' @param labels_row custom labels for rows that are used instead of rownames.
#' @param labels_col similar to labels_row, but for columns.
#' @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 silent do not draw the plot (useful when using the gtable output)
#' @param row_label row cluster labels for semi-clustering
#' @param col_label column cluster labels for semi-clustering
#' @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
#' # Create test matrix
#' 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)
#'
#' # Show text within cells
#' pheatmap(test, display_numbers = TRUE)
#' pheatmap(test, display_numbers = TRUE, number_format = "\%.1e")
#' pheatmap(test, display_numbers = matrix(ifelse(test > 5, "*", ""), nrow(test)))
#' pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0",
#' "1e-4", "1e-3", "1e-2", "1e-1", "1"))
#'
#' # Fix cell sizes and save to file with correct size
#' pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")
#' pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")
#'
#' # Generate annotations for rows and columns
#' annotation_col = data.frame(
#' CellType = factor(rep(c("CT1", "CT2"), 5)),
#' Time = 1:5
#' )
#' rownames(annotation_col) = paste("Test", 1:10, sep = "")
#'
#' annotation_row = data.frame(
#' GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6)))
#' )
#' rownames(annotation_row) = paste("Gene", 1:20, sep = "")
#'
#' # Display row and color annotations
#' pheatmap(test, annotation_col = annotation_col)
#' pheatmap(test, annotation_col = annotation_col, annotation_legend = FALSE)
#' pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row)
#'
#'
#' # Specify colors
#' ann_colors = list(
#' Time = c("white", "firebrick"),
#' CellType = c(CT1 = "#1B9E77", CT2 = "#D95F02"),
#' GeneClass = c(Path1 = "#7570B3", Path2 = "#E7298A", Path3 = "#66A61E")
#' )
#'
#' pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors, main = "Title")
#' pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row,
#' annotation_colors = ann_colors)
#' pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors[2])
#'
#' # Gaps in heatmaps
#' pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14))
#' pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14),
#' cutree_col = 2)
#'
#' # Show custom strings as row/col names
#' labels_row = c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
#' "", "", "Il10", "Il15", "Il1b")
#'
#' pheatmap(test, annotation_col = annotation_col, labels_row = labels_row)
#'
#' # Specifying clustering from distance matrix
#' drows = stats::dist(test, method = "minkowski")
#' dcols = stats::dist(t(test), method = "minkowski")
#' pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)
#'
#' # Modify ordering of the clusters using clustering callback option
#' callback = function(hc, mat){
#' sv = svd(t(mat))$v[,1]
#' dend = reorder(as.dendrogram(hc), wts = sv)
#' as.hclust(dend)
#' }
#'
#' pheatmap(test, clustering_callback = callback)
#'
#' dontrun{
#' # Same using dendsort package
#' library(dendsort)
#'
#' callback = function(hc, ...){dendsort(hc)}
#' pheatmap(test, clustering_callback = callback)
#' }
#'
#' @export
semi_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",
clustering_callback = identity2,
cutree_rows = NA,
cutree_cols = NA,
treeheight_row = ifelse(cluster_rows, 50, 0),
treeheight_col = ifelse(cluster_cols, 50, 0),
legend = TRUE, legend_breaks = NA, legend_labels = NA,
annotation_row = NA, annotation_col = NA,
annotation = NA, annotation_colors = NA,
annotation_legend = TRUE, annotation_names_row = TRUE, annotation_names_col = TRUE,
drop_levels = TRUE, show_rownames = TRUE, show_colnames = TRUE,
main = NA, fontsize = 10,
fontsize_row = fontsize, fontsize_col = fontsize,
display_numbers = FALSE,
number_format = "%.2f",
number_color = "grey30",
fontsize_number = 0.8 * fontsize,
gaps_row = NULL, gaps_col = NULL, labels_row = NULL, labels_col = NULL,
filename = NA, width = NA, height = NA, silent = FALSE,
row_label, col_label,
...){
# Set labels
if(is.null(labels_row) & !is.null(rownames(mat))){
labels_row = rownames(mat)
}
if(is.null(labels_row) & is.null(rownames(mat))){
labels_row = 1:nrow(mat)
rownames(mat) = 1:nrow(mat)
}
if(is.null(labels_col) & !is.null(colnames(mat))){
labels_col = colnames(mat)
}
if(is.null(labels_col) & is.null(colnames(mat))){
labels_col = 1:ncol(mat)
colnames(mat) = 1:ncol(mat)
}
if(is.na2(breaks)){
breaks = generate_breaks(mat, length(color), center = TRUE)
}
# Kmeans
if(!is.na(kmeans_k)){
# Cluster data
km = stats::kmeans(mat, kmeans_k, iter.max = 100)
mat = km$centers
# Compose rownames
t = table(km$cluster)
labels_row = sprintf("Cluster: %s Size: %d", names(t), t)
}
else{
km = NA
}
# Format numbers to be displayed in cells
if(is.matrix(display_numbers) | is.data.frame(display_numbers)){
if(nrow(display_numbers) != nrow(mat) | ncol(display_numbers) != ncol(mat)){
stop("If display_numbers provided as matrix, its dimensions have to match with mat")
}
display_numbers = as.matrix(display_numbers)
fmat = matrix(as.character(display_numbers), nrow = nrow(display_numbers), ncol = ncol(display_numbers))
fmat_draw = TRUE
}
else{
if(display_numbers){
fmat = matrix(sprintf(number_format, mat), nrow = nrow(mat), ncol = ncol(mat))
fmat_draw = TRUE
}
else{
fmat = matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
fmat_draw = FALSE
}
}
# Do clustering for rows
if(cluster_rows == TRUE) {
if (is.null(row_label)) {
row_label = rep(1, nrow(mat))
} else {
o = order(row_label)
mat = mat[o,,drop=FALSE]
fmat = fmat[o, , drop = FALSE]
row_label = row_label[o]
if(!is.null(annotation_row)) {
annotation_row = annotation_row[o,,drop=FALSE]
}
}
tree_row = cluster_mat(mat, row_label , distance = clustering_distance_rows, method = clustering_method)
tree_row = clustering_callback(tree_row, mat)
mat = mat[tree_row$order, , drop = FALSE]
fmat = fmat[tree_row$order, , drop = FALSE]
labels_row = labels_row[tree_row$order]
if(!is.na(cutree_rows)){
gaps_row = find_gaps(tree_row, cutree_rows)
}
else {
gaps_row = NULL
}
} else {
tree_row = NA
treeheight_row = 0
}
## Do clustering for columns
if(cluster_cols == TRUE) {
if(is.null(col_label)) {
col_label = rep(1, ncol(mat))
} else {
o = order(col_label)
mat = mat[,o,drop=FALSE]
fmat = fmat[,o,drop = FALSE]
col_label = col_label[o]
if(!is.null(annotation_col)) {
annotation_col = annotation_col[o,,drop=FALSE]
}
}
tree_col = cluster_mat(t(mat), col_label , distance = clustering_distance_cols, method = clustering_method)
tree_col = clustering_callback(tree_col, t(mat))
mat = mat[, tree_col$order, drop = FALSE]
fmat = fmat[, tree_col$order, drop = FALSE]
labels_col = labels_col[tree_col$order]
if(!is.na(cutree_cols)){
gaps_col = find_gaps(tree_col, cutree_cols)
}
else {
gaps_col = NULL
}
} else {
tree_col = NA
treeheight_col = 0
}
attr(fmat, "draw") = fmat_draw
# Colors and scales
if(!is.na2(legend_breaks) & !is.na2(legend_labels)){
if(length(legend_breaks) != length(legend_labels)){
stop("Lengths of legend_breaks and legend_labels must be the same")
}
}
if(is.na2(breaks)){
breaks = generate_breaks(as.vector(mat), length(color))
}
if (legend & is.na2(legend_breaks)) {
legend = grid.pretty(range(as.vector(breaks)))
names(legend) = legend
}
else if(legend & !is.na2(legend_breaks)){
legend = legend_breaks[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
if(!is.na2(legend_labels)){
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)
annotation = c(annotation_row, annotation_col)
annotation = annotation[unlist(lapply(annotation, function(x) !is.na2(x)))]
if(length(annotation) != 0){
annotation_colors = generate_annotation_colours(annotation, annotation_colors, drop = drop_levels)
}
else{
annotation_colors = NA
}
labels_row = rownames(mat)
labels_col = colnames(mat)
if(!show_rownames){
labels_row = NULL
}
if(!show_colnames){
labels_col = NULL
}
# Draw heatmap
gt = 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_row = annotation_row, annotation_col = annotation_col, annotation_colors = annotation_colors, annotation_legend = annotation_legend, annotation_names_row = annotation_names_row, annotation_names_col = annotation_names_col, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat, fontsize_number = fontsize_number, number_color = number_color, gaps_row = gaps_row, gaps_col = gaps_col, labels_row = labels_row, labels_col = labels_col, ...)
if(is.na(filename) & !silent){
grid.newpage()
grid.draw(gt)
}
invisible(list(tree_row = tree_row, tree_col = tree_col, gtable = gt))
}
compbiomed/celda documentation built on May 25, 2019, 3:58 a.m.
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Defines functions lo find_coordinates draw_dendrogram draw_matrix draw_colnames draw_rownames draw_legend convert_annotations draw_annotations draw_annotation_names draw_annotation_legend draw_main vplayout heatmap_motor generate_breaks scale_vec_colours scale_colours cluster_mat scale_rows scale_mat generate_annotation_colours kmeans_pheatmap find_gaps is.na2 identity2 semi_pheatmap
Documented in semi_pheatmap
# Adapted originally from the very excellent pheatmap package
# (https://cran.r-project.org/web/packages/pheatmap/index.html)
lo = function(rown, coln, nrow, ncol, cellheight = NA, cellwidth = NA, treeheight_col, treeheight_row, legend, annotation_row, annotation_col, annotation_colors, annotation_legend, annotation_names_row, annotation_names_col, main, fontsize, fontsize_row, fontsize_col, gaps_row, gaps_col, ...){
# Get height of colnames and length of rownames
if(!is.null(coln[1]) | (!is.na2(annotation_row) & annotation_names_row)){
if(!is.null(coln[1])){
t = coln
} else {
t = ""
}
tw = strwidth(t, units = 'in', cex = fontsize_col / fontsize)
if(annotation_names_row){
t = c(t, colnames(annotation_row))
tw = c(tw, strwidth(colnames(annotation_row), units = 'in'))
}
longest_coln = which.max(tw)
gp = list(fontsize = ifelse(longest_coln <= length(coln), fontsize_col, fontsize), ...)
coln_height = unit(1, "grobheight", textGrob(t[longest_coln], rot = 90, gp = do.call(gpar, gp))) + unit(10, "bigpts")
}
else{
coln_height = unit(5, "bigpts")
}
if(!is.null(rown[1])){
t = rown
tw = strwidth(t, units = 'in', cex = fontsize_row / fontsize)
if(annotation_names_col){
t = c(t, colnames(annotation_col))
tw = c(tw, strwidth(colnames(annotation_col), units = 'in'))
}
longest_rown = which.max(tw)
gp = list(fontsize = ifelse(longest_rown <= length(rown), fontsize_row, fontsize), ...)
rown_width = unit(1, "grobwidth", textGrob(t[longest_rown], gp = do.call(gpar, gp))) + unit(10, "bigpts")
}
else{
rown_width = unit(5, "bigpts")
}
gp = list(fontsize = fontsize, ...)
# Legend position
if(!is.na2(legend)){
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
textheight = unit(fontsize, "bigpts")
if(!is.na2(annotation_col)){
# Column annotation height
annot_col_height = ncol(annotation_col) * (textheight + unit(2, "bigpts")) + unit(2, "bigpts")
# Width of the correponding legend
t = c(as.vector(as.matrix(annotation_col)), colnames(annotation_col))
annot_col_legend_width = unit(1.2, "grobwidth", textGrob(t[which.max(nchar(t))], gp = gpar(...))) + unit(12, "bigpts")
if(!annotation_legend){
annot_col_legend_width = unit(0, "npc")
}
}
else{
annot_col_height = unit(0, "bigpts")
annot_col_legend_width = unit(0, "bigpts")
}
# Row annotations
if(!is.na2(annotation_row)){
# Row annotation width
annot_row_width = ncol(annotation_row) * (textheight + unit(2, "bigpts")) + unit(2, "bigpts")
# Width of the correponding legend
t = c(as.vector(as.matrix(annotation_row)), colnames(annotation_row))
annot_row_legend_width = unit(1.2, "grobwidth", textGrob(t[which.max(nchar(t))], gp = gpar(...))) + unit(12, "bigpts")
if(!annotation_legend){
annot_row_legend_width = unit(0, "npc")
}
}
else{
annot_row_width = unit(0, "bigpts")
annot_row_legend_width = unit(0, "bigpts")
}
annot_legend_width = max(annot_row_legend_width, annot_col_legend_width)
# 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)){
mat_width = unit(1, "npc") - rown_width - legend_width - treeheight_row - annot_row_width - annot_legend_width
}
else{
mat_width = unit(cellwidth * ncol, "bigpts") + length(gaps_col) * unit(0, "bigpts")
}
if(is.na(cellheight)){
mat_height = unit(1, "npc") - main_height - coln_height - treeheight_col - annot_col_height
}
else{
mat_height = unit(cellheight * nrow, "bigpts") + length(gaps_row) * unit(0, "bigpts")
}
# Produce gtable
gt = gtable(widths = unit.c(treeheight_row, annot_row_width, mat_width, rown_width, legend_width, annot_legend_width), heights = unit.c(main_height, treeheight_col, annot_col_height, mat_height, coln_height), vp = viewport(gp = do.call(gpar, gp)))
cw = convertWidth(mat_width - (length(gaps_col) * unit(0, "bigpts")), "bigpts", valueOnly = TRUE) / ncol
ch = convertHeight(mat_height - (length(gaps_row) * unit(0, "bigpts")), "bigpts", valueOnly = TRUE) / nrow
# Return minimal cell dimension in bigpts to decide if borders are drawn
mindim = min(cw, ch)
res = list(gt = gt, mindim = mindim)
return(res)
}
find_coordinates = function(n, gaps, m = 1:n){
if(length(gaps) == 0){
return(list(coord = unit(m / n, "npc"), size = unit(1 / n, "npc") ))
}
if(max(gaps) > n){
stop("Gaps do not match with matrix size")
}
size = (1 / n) * (unit(1, "npc") - length(gaps) * unit("0", "bigpts"))
gaps2 = base::apply(sapply(gaps, function(gap, x){x > gap}, m), 1, sum)
coord = m * size + (gaps2 * unit("0", "bigpts"))
return(list(coord = coord, size = size))
}
draw_dendrogram = function(hc, gaps, horizontal = TRUE){
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){
res = list(
x = c(x1, x1, x2, x2),
y = c(y1, y, y, y2)
)
return(res)
}
x = rep(NA, nrow(m) * 4)
y = rep(NA, nrow(m) * 4)
id = rep(1:nrow(m), rep(4, nrow(m)))
for(i in 1:nrow(m)){
c = draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
k = (i - 1) * 4 + 1
x[k : (k + 3)] = c$x
y[k : (k + 3)] = c$y
}
x = find_coordinates(n, gaps, x * n)$coord
y = unit(y, "npc")
if(!horizontal){
a = x
x = unit(1, "npc") - y
y = unit(1, "npc") - a
}
res = polylineGrob(x = x, y = y, id = id)
return(res)
}
draw_matrix = function(matrix, border_color, gaps_rows, gaps_cols, fmat, fontsize_number, number_color){
n = nrow(matrix)
m = ncol(matrix)
coord_x = find_coordinates(m, gaps_cols)
coord_y = find_coordinates(n, gaps_rows)
x = coord_x$coord - 0.5 * coord_x$size
y = unit(1, "npc") - (coord_y$coord - 0.5 * coord_y$size)
coord = expand.grid(y = y, x = x)
res = gList()
res[["rect"]] = rectGrob(x = coord$x, y = coord$y, width = coord_x$size, height = coord_y$size, gp = gpar(fill = matrix, col = border_color))
if(attr(fmat, "draw")){
res[["text"]] = textGrob(x = coord$x, y = coord$y, label = fmat, gp = gpar(col = number_color, fontsize = fontsize_number))
}
res = gTree(children = res)
return(res)
}
draw_colnames = function(coln, gaps, ...){
coord = find_coordinates(length(coln), gaps)
x = coord$coord - 0.5 * coord$size
res = textGrob(coln, x = x, y = unit(1, "npc") - unit(3, "bigpts"), vjust = 0.5, hjust = 0, rot = 270, gp = gpar(...))
return(res)
}
draw_rownames = function(rown, gaps, ...){
coord = find_coordinates(length(rown), gaps)
y = unit(1, "npc") - (coord$coord - 0.5 * coord$size)
res = textGrob(rown, x = unit(3, "bigpts"), y = y, vjust = 0.5, hjust = 0, gp = gpar(...))
return(res)
}
draw_legend = function(color, breaks, legend, ...){
height = min(unit(1, "npc"), unit(150, "bigpts"))
legend_pos = (legend - min(breaks)) / (max(breaks) - min(breaks))
legend_pos = height * legend_pos + (unit(1, "npc") - height)
breaks = (breaks - min(breaks)) / (max(breaks) - min(breaks))
breaks = height * breaks + (unit(1, "npc") - height)
h = breaks[-1] - breaks[-length(breaks)]
rect = rectGrob(x = 0, y = breaks[-length(breaks)], width = unit(10, "bigpts"), height = h, hjust = 0, vjust = 0, gp = gpar(fill = color, col = "#FFFFFF00"))
text = textGrob(names(legend), x = unit(14, "bigpts"), y = legend_pos, hjust = 0, gp = gpar(...))
res = grobTree(rect, text)
return(res)
}
convert_annotations = function(annotation, annotation_colors){
new = annotation
for(i in 1:ncol(annotation)){
a = annotation[, i]
b = annotation_colors[[colnames(annotation)[i]]]
if(is.character(a) | is.factor(a)){
a = as.character(a)
if(length(setdiff(setdiff(a, NA), names(b))) > 0){
stop(sprintf("Factor levels on variable %s do not match with annotation_colors", colnames(annotation)[i]))
}
new[, i] = b[a]
}
else{
a = cut(a, breaks = 100)
new[, i] = colorRampPalette(b)(100)[a]
}
}
return(as.matrix(new))
}
draw_annotations = function(converted_annotations, border_color, gaps, fontsize, horizontal){
n = ncol(converted_annotations)
m = nrow(converted_annotations)
coord_x = find_coordinates(m, gaps)
x = coord_x$coord - 0.5 * coord_x$size
# y = cumsum(rep(fontsize, n)) - 4 + cumsum(rep(2, n))
y = cumsum(rep(fontsize, n)) + cumsum(rep(2, n)) - fontsize / 2 + 1
y = unit(y, "bigpts")
if(horizontal){
coord = expand.grid(x = x, y = y)
res = rectGrob(x = coord$x, y = coord$y, width = coord_x$size, height = unit(fontsize, "bigpts"), gp = gpar(fill = converted_annotations, col = border_color))
}
else{
a = x
x = unit(1, "npc") - y
y = unit(1, "npc") - a
coord = expand.grid(y = y, x = x)
res = rectGrob(x = coord$x, y = coord$y, width = unit(fontsize, "bigpts"), height = coord_x$size, gp = gpar(fill = converted_annotations, col = border_color))
}
return(res)
}
draw_annotation_names = function(annotations, fontsize, horizontal){
n = ncol(annotations)
x = unit(3, "bigpts")
y = cumsum(rep(fontsize, n)) + cumsum(rep(2, n)) - fontsize / 2 + 1
y = unit(y, "bigpts")
if(horizontal){
res = textGrob(colnames(annotations), x = x, y = y, hjust = 0, gp = gpar(fontsize = fontsize, fontface = 2))
}
else{
a = x
x = unit(1, "npc") - y
y = unit(1, "npc") - a
res = textGrob(colnames(annotations), x = x, y = y, vjust = 0.5, hjust = 0, rot = 270, gp = gpar(fontsize = fontsize, fontface = 2))
}
return(res)
}
draw_annotation_legend = function(annotation, annotation_colors, border_color, ...){
y = unit(1, "npc")
text_height = unit(1, "grobheight", textGrob("FGH", gp = gpar(...)))
res = gList()
for(i in names(annotation)){
res[[i]] = textGrob(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]])){
n = length(annotation_colors[[i]])
yy = y - (1:n - 1) * 2 * text_height
res[[paste(i, "r")]] = rectGrob(x = unit(0, "npc"), y = yy, hjust = 0, vjust = 1, height = 2 * text_height, width = 2 * text_height, gp = gpar(col = border_color, fill = annotation_colors[[i]]))
res[[paste(i, "t")]] = textGrob(names(annotation_colors[[i]]), x = text_height * 2.4, y = yy - text_height, hjust = 0, vjust = 0.5, gp = gpar(...))
y = y - n * 2 * text_height
}
else{
yy = y - 8 * text_height + seq(0, 1, 0.25)[-1] * 8 * text_height
h = 8 * text_height * 0.25
res[[paste(i, "r")]] = rectGrob(x = unit(0, "npc"), y = yy, hjust = 0, vjust = 1, height = h, width = 2 * text_height, gp = gpar(col = NA, fill = colorRampPalette(annotation_colors[[i]])(4)))
res[[paste(i, "r2")]] = rectGrob(x = unit(0, "npc"), y = y, hjust = 0, vjust = 1, height = 8 * text_height, width = 2 * text_height, gp = gpar(col = border_color, fill = NA))
txt = rev(range(grid.pretty(range(annotation[[i]], na.rm = TRUE))))
yy = y - c(1, 7) * text_height
res[[paste(i, "t")]] = textGrob(txt, x = text_height * 2.4, y = yy, hjust = 0, vjust = 0.5, gp = gpar(...))
y = y - 8 * text_height
}
y = y - 1.5 * text_height
}
res = gTree(children = res)
return(res)
}
draw_main = function(text, ...){
res = textGrob(text, gp = gpar(fontface = "bold", ...))
return(res)
}
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_row, annotation_col, annotation_colors, annotation_legend, annotation_names_row, annotation_names_col, main, fontsize, fontsize_row, fontsize_col, fmat, fontsize_number, number_color, gaps_col, gaps_row, labels_row, labels_col, ...){
# Set layout
lo = lo(coln = labels_col, rown = labels_row, nrow = nrow(matrix), ncol = ncol(matrix), cellwidth = cellwidth, cellheight = cellheight, treeheight_col = treeheight_col, treeheight_row = treeheight_row, legend = legend, annotation_col = annotation_col, annotation_row = annotation_row, annotation_colors = annotation_colors, annotation_legend = annotation_legend, annotation_names_row = annotation_names_row, annotation_names_col = annotation_names_col, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, gaps_row = gaps_row, gaps_col = gaps_col, ...)
res = lo$gt
mindim = lo$mindim
if(!is.na(filename)){
if(is.na(height)){
height = convertHeight(gtable_height(res), "inches", valueOnly = TRUE)
}
if(is.na(width)){
width = convertWidth(gtable_width(res), "inches", valueOnly = TRUE)
}
# Get file type
r = regexpr("\\.[a-zA-Z]*$", 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))
# gt = 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_col = annotation_col, annotation_row = annotation_row, 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, number_color = number_color, labels_row = labels_row, labels_col = labels_col, gaps_col = gaps_col, gaps_row = gaps_row, ...)
f(filename, height = height, width = width)
gt = 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_col = annotation_col, annotation_row = annotation_row, annotation_colors = annotation_colors, annotation_legend = annotation_legend, annotation_names_row = annotation_names_row, annotation_names_col = annotation_names_col, filename = NA, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat, fontsize_number = fontsize_number, number_color = number_color, labels_row = labels_row, labels_col = labels_col, gaps_col = gaps_col, gaps_row = gaps_row, ...)
grid.draw(gt)
dev.off()
return(gt)
}
# Omit border color if cell size is too small
if(mindim < 3) border_color = NA
# Draw title
if(!is.na(main)){
elem = draw_main(main, fontsize = 1.3 * fontsize, ...)
res = gtable_add_grob(res, elem, t = 1, l = 3, name = "main", clip = "off")
}
# Draw tree for the columns
if(!is.na2(tree_col) & treeheight_col != 0){
elem = draw_dendrogram(tree_col, gaps_col, horizontal = TRUE)
res = gtable_add_grob(res, elem, t = 2, l = 3, name = "col_tree")
}
# Draw tree for the rows
if(!is.na2(tree_row) & treeheight_row != 0){
elem = draw_dendrogram(tree_row, gaps_row, horizontal = FALSE)
res = gtable_add_grob(res, elem, t = 4, l = 1, name = "row_tree")
}
# Draw matrix
elem = draw_matrix(matrix, border_color, gaps_row, gaps_col, fmat, fontsize_number, number_color)
res = gtable_add_grob(res, elem, t = 4, l = 3, clip = "off", name = "matrix")
# Draw colnames
if(length(labels_col) != 0){
pars = list(labels_col, gaps = gaps_col, fontsize = fontsize_col, ...)
elem = do.call(draw_colnames, pars)
res = gtable_add_grob(res, elem, t = 5, l = 3, clip = "off", name = "col_names")
}
# Draw rownames
if(length(labels_row) != 0){
pars = list(labels_row, gaps = gaps_row, fontsize = fontsize_row, ...)
elem = do.call(draw_rownames, pars)
res = gtable_add_grob(res, elem, t = 4, l = 4, clip = "off", name = "row_names")
}
# Draw annotation tracks on cols
if(!is.na2(annotation_col)){
# Draw tracks
converted_annotation = convert_annotations(annotation_col, annotation_colors)
elem = draw_annotations(converted_annotation, border_color, gaps_col, fontsize, horizontal = TRUE)
res = gtable_add_grob(res, elem, t = 3, l = 3, clip = "off", name = "col_annotation")
# Draw names
if(annotation_names_col){
elem = draw_annotation_names(annotation_col, fontsize, horizontal = TRUE)
res = gtable_add_grob(res, elem, t = 3, l = 4, clip = "off", name = "col_annotation_names")
}
}
# Draw annotation tracks on rows
if(!is.na2(annotation_row)){
# Draw tracks
converted_annotation = convert_annotations(annotation_row, annotation_colors)
elem = draw_annotations(converted_annotation, border_color, gaps_row, fontsize, horizontal = FALSE)
res = gtable_add_grob(res, elem, t = 4, l = 2, clip = "off", name = "row_annotation")
# Draw names
if(annotation_names_row){
elem = draw_annotation_names(annotation_row, fontsize, horizontal = FALSE)
res = gtable_add_grob(res, elem, t = 5, l = 2, clip = "off", name = "row_annotation_names")
}
}
# Draw annotation legend
annotation = c(annotation_col[length(annotation_col):1], annotation_row[length(annotation_row):1])
annotation = annotation[unlist(lapply(annotation, function(x) !is.na2(x)))]
if(length(annotation) > 0 & annotation_legend){
elem = draw_annotation_legend(annotation, annotation_colors, border_color, fontsize = fontsize, ...)
t = ifelse(is.null(labels_row), 4, 3)
res = gtable_add_grob(res, elem, t = t, l = 6, b = 5, clip = "off", name = "annotation_legend")
}
# Draw legend
if(!is.na2(legend)){
elem = draw_legend(color, breaks, legend, fontsize = fontsize, ...)
t = ifelse(is.null(labels_row), 4, 3)
res = gtable_add_grob(res, elem, t = t, l = 5, b = 5, clip = "off", name = "legend")
}
return(res)
}
generate_breaks = function(x, n, center = FALSE){
if(center){
m = max(abs(c(min(x, na.rm = TRUE), max(x, na.rm = TRUE))))
res = seq(-m, m, length.out = n + 1)
}
else{
res = seq(min(x, na.rm = TRUE), max(x, na.rm = TRUE), 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 = TRUE))])
}
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))))
}
## changed the original cluster_mat() in the pheatmap.r
cluster_mat <- function(mat, labels, distance, method){
# this funciton is going to change the cluster_mat() in pheatmap
if(!(method %in% c("ward.D", "ward.D2", "ward", "single", "complete", "average", "mcquitty", "median", "centroid"))){
stop("clustering method has to one form the list: 'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.")
}
class.label <- unique(labels)
nGroup <- length(class.label) # [#group]
# get "hclust" object for each group then wrap them up as group.hclust
# distance function preparation
dis <- function(mat, distance) {
if(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) & !is(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"){
# this part should be confirmed whether being wrong? ToDo: how is the correlation matrix converted to a dsit matrix
d <- stats::as.dist(1 - stats::cor(t(mat)))
}
else{
d <- stats::dist(mat, method = distance)
}
return(d)
}
# initiate the final returning value: a "hclust" object
cum.hclust <- list()
if(nGroup==1){ # matrix has only 1 group
if(length(labels)==1){
stop("only one row/column for the matrix")
}
group.hclust <- stats::hclust(dis(mat = mat, distance = distance), method = method)
cum.hclust <- group.hclust
}else { # matrix has more than 1 groups
group.hclust <- sapply(class.label, function(x) {
class.pos <- which(labels==x) # get the positions of class label
if(length(class.pos)==1){ # if only 1 row in the group return a manually made "hclust" object
sub.hclust <- as.list(1:7)
names(sub.hclust) <- c("merge","height", "order", "labels","method","call","dist.method")
class(sub.hclust) <- "hclust"
sub.hclust$merge <- matrix(c(0,0), nrow = 1)
sub.hclust$height <- 0
sub.hclust$order <- 1
return(sub.hclust)
}else if(length(class.pos)>1){ # if >1 rows return the "hclust" object
return(stats::hclust(dis(mat = mat[class.pos,], distance = distance ),method = method ))
} }
)
# the length(group.hclust) is the [#group] == nGroup , group.hclust[[i]] to get each "hclust"
# then modify the "hclust" object and get them merged into one "hclust" object
cum.hclust <- group.hclust[,nGroup] # initiate the final "hclust" object
# merge function preparation
merge_hclust <- function(hclust1, hclust2){ # "hclust" object modifying function preparation
if(hclust1$merge[1,1]==0 & hclust2$merge[1,1]==0){ # both groups have only 1 row
hclust.com <- as.list(1:7)
names(hclust.com) <- c("merge","height", "order", "labels","method","call","dist.method")
class(hclust.com) <- "hclust"
hclust.com$merge <- matrix(c(-1,-2), nrow = 1)
hclust.com$height <- 1 # check for different matrix whether 1 should be good
hclust.com$order <- c(1,2)
return(hclust.com)
}else if( hclust1$merge[1,1]!=0 & hclust2$merge[1,1]!=0) { # both group have >1 rows
row.1 <- dim(hclust1$merge)[1] # #nodes in the hclust1 group, so actually the #rows shouls be dim()[1]+1
row.2 <- dim(hclust2$merge)[1] # #nodes in the hclust2 group
hclust.com <- list()
mer <- hclust2$merge
hclust.com$merge <- (mer>0) * (mer+row.1) + (mer<0) * (mer-row.1-1) # modify the hclust2$merge matrix
hclust.com$merge <- rbind(hclust1$merge, hclust.com$merge) # combine the merge matrix from the 2 groups
hclust.com$merge <- rbind(hclust.com$merge, c(row.1, row.1+row.2))
hclust.com$height <- c(hclust1$height, hclust2$height)
hclust.com$height <- c(hclust.com$height, max(hclust.com$height)+ 1 ) # check for different matrix whether 1 should be good
hclust.com$order <- c(hclust1$order, hclust2$order+row.1+1)
class(hclust.com) <- "hclust"
}else{ # one group has only 1 row, the other group has >1 rows
if(hclust1$merge[1,1]==0){ # hclust1 has 1 row , hclust2 has >1 rows
row.2 <- dim(hclust2$merge)[1] # #nodes in the hclust2 group
hclust.com <- as.list(1:7)
names(hclust.com) <- c("merge","height", "order", "labels","method","call","dist.method")
class(hclust.com) <- "hclust"
mer <- hclust2$merge
hclust.com$merge <- (mer>0) * (mer) + (mer<0) * (mer-1)
hclust.com$merge <- rbind(hclust.com$merge, c(-1, row.2))
hclust.com$height <- c(hclust2$height, max(hclust2$height)+1) # check for different matrix whether 1 should be good
hclust.com$order <- c(1, hclust2$order+1)
}else if(hclust2$merge[1,1]==0){ # the hclust1 has >1 rows , and hclust2 has 1 row
row.1 <- dim(hclust1$merge)[1] # #nodes in the hclust1 group
hclust.com <- as.list(1:7)
names(hclust.com) <- c("merge","height", "order", "labels","method","call","dist.method")
class(hclust.com) <- "hclust"
hclust.com$merge <- hclust1$merge
hclust.com$merge <- rbind(hclust.com$merge, c(row.1, -(row.1+2)))
hclust.com$height <- c(hclust1$height, max(hclust1$height)+1)
hclust.com$order <- c(hclust1$order, max(hclust1$order)+1)
}
}
return(hclust.com)
}
# merge the "hclust" object into the final one "hclust" object
for(i in seq(nGroup-1, 1,-1)){
cum.hclust <- merge_hclust( group.hclust[,i], cum.hclust)
}
}
cum.hclust$labels <-NULL
cum.hclust$call <- NULL
cum.hclust$method <- NULL
cum.hclust$dist.method <- NULL
return(cum.hclust)
}
scale_rows = function(x){
m = base::apply(x, 1, mean, na.rm = TRUE)
s = base::apply(x, 1, stats::sd, na.rm = TRUE)
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.na2(annotation_colors)){
annotation_colors = list()
}
count = 0
for(i in 1:length(annotation)){
annotation[[i]] = annotation[[i]][!is.na(annotation[[i]])]
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 = dscale(factor(1:count), hue_pal(l = 75))
set.seed(3453)
cont_counter = 2
for(i in 1:length(annotation)){
if(!(names(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[[names(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[[names(annotation)[i]]]) = l
factor_colors = factor_colors[-ind]
}
else{
annotation_colors[[names(annotation)[i]]] = brewer_pal("seq", cont_counter)(5)[1:4]
cont_counter = cont_counter + 1
}
}
}
return(annotation_colors)
}
kmeans_pheatmap = function(mat, k = min(nrow(mat), 150), sd_limit = NA, ...){
# Filter data
if(!is.na(sd_limit)){
s = base::apply(mat, 1, stats::sd)
mat = mat[s > sd_limit, ]
}
# Cluster data
set.seed(1245678)
km = stats::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::pheatmap(mat2, ...)
}
find_gaps = function(tree, cutree_n){
v = stats::cutree(tree, cutree_n)[tree$order]
gaps = which((v[-1] - v[-length(v)]) != 0)
}
is.na2 = function(x){
if(is.list(x) | length(x) > 1){
return(FALSE)
}
if(length(x) == 0){
return(TRUE)
}
return(is.na(x))
}
identity2 = function(x, ...){
return(x)
}
#' 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 Numeric vector. A sequence of numbers that covers the range of values in the normalized `counts`. Values in the normalized `matrix` are assigned to each bin in `breaks`. Each break is assigned to a unique color from `col`. If NULL, then breaks are calculated automatically. Default NULL.
#' @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 or \code{hclust} object,
#' @param cluster_cols boolean values determining if columns should be clustered or \code{hclust} object.
#' @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 clustering_callback callback function to modify the clustering. Is
#' called with two parameters: original \code{hclust} object and the matrix
#' used for clustering. Must return a \code{hclust} object.
#' @param cutree_rows number of clusters the rows are divided into, based on the
#' hierarchical clustering (using cutree), if rows are not clustered, the
#' argument is ignored
#' @param cutree_cols similar to \code{cutree_rows}, but for columns
#' @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_row data frame that specifies the annotations shown on left
#' side of the heatmap. Each row defines the features for a specific row. The
#' rows in the data and in the annotation are matched using corresponding row
#' names. Note that color schemes takes into account if variable is continuous
#' or discrete.
#' @param annotation_col similar to annotation_row, but for columns.
#' @param annotation deprecated parameter that currently sets the annotation_col if it is missing
#' @param annotation_colors list for specifying annotation_row and
#' annotation_col 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 annotation_names_row boolean value showing if the names for row annotation
#' tracks should be drawn.
#' @param annotation_names_col boolean value showing if the names for column 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. If this is a matrix (with same dimensions as original matrix), the contents
#' of the matrix are shown instead of original values.
#' @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 number_color color of the text
#' @param fontsize_number fontsize of the numbers displayed in cells
#' @param gaps_row vector of row indices that show shere to put gaps into
#' heatmap. Used only if the rows are not clustered. See \code{cutree_row}
#' to see how to introduce gaps to clustered rows.
#' @param gaps_col similar to gaps_row, but for columns.
#' @param labels_row custom labels for rows that are used instead of rownames.
#' @param labels_col similar to labels_row, but for columns.
#' @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 silent do not draw the plot (useful when using the gtable output)
#' @param row_label row cluster labels for semi-clustering
#' @param col_label column cluster labels for semi-clustering
#' @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
#' # Create test matrix
#' 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)
#'
#' # Show text within cells
#' pheatmap(test, display_numbers = TRUE)
#' pheatmap(test, display_numbers = TRUE, number_format = "\%.1e")
#' pheatmap(test, display_numbers = matrix(ifelse(test > 5, "*", ""), nrow(test)))
#' pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0",
#' "1e-4", "1e-3", "1e-2", "1e-1", "1"))
#'
#' # Fix cell sizes and save to file with correct size
#' pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")
#' pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")
#'
#' # Generate annotations for rows and columns
#' annotation_col = data.frame(
#' CellType = factor(rep(c("CT1", "CT2"), 5)),
#' Time = 1:5
#' )
#' rownames(annotation_col) = paste("Test", 1:10, sep = "")
#'
#' annotation_row = data.frame(
#' GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6)))
#' )
#' rownames(annotation_row) = paste("Gene", 1:20, sep = "")
#'
#' # Display row and color annotations
#' pheatmap(test, annotation_col = annotation_col)
#' pheatmap(test, annotation_col = annotation_col, annotation_legend = FALSE)
#' pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row)
#'
#'
#' # Specify colors
#' ann_colors = list(
#' Time = c("white", "firebrick"),
#' CellType = c(CT1 = "#1B9E77", CT2 = "#D95F02"),
#' GeneClass = c(Path1 = "#7570B3", Path2 = "#E7298A", Path3 = "#66A61E")
#' )
#'
#' pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors, main = "Title")
#' pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row,
#' annotation_colors = ann_colors)
#' pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors[2])
#'
#' # Gaps in heatmaps
#' pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14))
#' pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14),
#' cutree_col = 2)
#'
#' # Show custom strings as row/col names
#' labels_row = c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
#' "", "", "Il10", "Il15", "Il1b")
#'
#' pheatmap(test, annotation_col = annotation_col, labels_row = labels_row)
#'
#' # Specifying clustering from distance matrix
#' drows = stats::dist(test, method = "minkowski")
#' dcols = stats::dist(t(test), method = "minkowski")
#' pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)
#'
#' # Modify ordering of the clusters using clustering callback option
#' callback = function(hc, mat){
#' sv = svd(t(mat))$v[,1]
#' dend = reorder(as.dendrogram(hc), wts = sv)
#' as.hclust(dend)
#' }
#'
#' pheatmap(test, clustering_callback = callback)
#'
#' dontrun{
#' # Same using dendsort package
#' library(dendsort)
#'
#' callback = function(hc, ...){dendsort(hc)}
#' pheatmap(test, clustering_callback = callback)
#' }
#'
#' @export
semi_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",
clustering_callback = identity2,
cutree_rows = NA,
cutree_cols = NA,
treeheight_row = ifelse(cluster_rows, 50, 0),
treeheight_col = ifelse(cluster_cols, 50, 0),
legend = TRUE, legend_breaks = NA, legend_labels = NA,
annotation_row = NA, annotation_col = NA,
annotation = NA, annotation_colors = NA,
annotation_legend = TRUE, annotation_names_row = TRUE, annotation_names_col = TRUE,
drop_levels = TRUE, show_rownames = TRUE, show_colnames = TRUE,
main = NA, fontsize = 10,
fontsize_row = fontsize, fontsize_col = fontsize,
display_numbers = FALSE,
number_format = "%.2f",
number_color = "grey30",
fontsize_number = 0.8 * fontsize,
gaps_row = NULL, gaps_col = NULL, labels_row = NULL, labels_col = NULL,
filename = NA, width = NA, height = NA, silent = FALSE,
row_label, col_label,
...){
# Set labels
if(is.null(labels_row) & !is.null(rownames(mat))){
labels_row = rownames(mat)
}
if(is.null(labels_row) & is.null(rownames(mat))){
labels_row = 1:nrow(mat)
rownames(mat) = 1:nrow(mat)
}
if(is.null(labels_col) & !is.null(colnames(mat))){
labels_col = colnames(mat)
}
if(is.null(labels_col) & is.null(colnames(mat))){
labels_col = 1:ncol(mat)
colnames(mat) = 1:ncol(mat)
}
if(is.na2(breaks)){
breaks = generate_breaks(mat, length(color), center = TRUE)
}
# Kmeans
if(!is.na(kmeans_k)){
# Cluster data
km = stats::kmeans(mat, kmeans_k, iter.max = 100)
mat = km$centers
# Compose rownames
t = table(km$cluster)
labels_row = sprintf("Cluster: %s Size: %d", names(t), t)
}
else{
km = NA
}
# Format numbers to be displayed in cells
if(is.matrix(display_numbers) | is.data.frame(display_numbers)){
if(nrow(display_numbers) != nrow(mat) | ncol(display_numbers) != ncol(mat)){
stop("If display_numbers provided as matrix, its dimensions have to match with mat")
}
display_numbers = as.matrix(display_numbers)
fmat = matrix(as.character(display_numbers), nrow = nrow(display_numbers), ncol = ncol(display_numbers))
fmat_draw = TRUE
}
else{
if(display_numbers){
fmat = matrix(sprintf(number_format, mat), nrow = nrow(mat), ncol = ncol(mat))
fmat_draw = TRUE
}
else{
fmat = matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
fmat_draw = FALSE
}
}
# Do clustering for rows
if(cluster_rows == TRUE) {
if (is.null(row_label)) {
row_label = rep(1, nrow(mat))
} else {
o = order(row_label)
mat = mat[o,,drop=FALSE]
fmat = fmat[o, , drop = FALSE]
row_label = row_label[o]
if(!is.null(annotation_row)) {
annotation_row = annotation_row[o,,drop=FALSE]
}
}
tree_row = cluster_mat(mat, row_label , distance = clustering_distance_rows, method = clustering_method)
tree_row = clustering_callback(tree_row, mat)
mat = mat[tree_row$order, , drop = FALSE]
fmat = fmat[tree_row$order, , drop = FALSE]
labels_row = labels_row[tree_row$order]
if(!is.na(cutree_rows)){
gaps_row = find_gaps(tree_row, cutree_rows)
}
else {
gaps_row = NULL
}
} else {
tree_row = NA
treeheight_row = 0
}
## Do clustering for columns
if(cluster_cols == TRUE) {
if(is.null(col_label)) {
col_label = rep(1, ncol(mat))
} else {
o = order(col_label)
mat = mat[,o,drop=FALSE]
fmat = fmat[,o,drop = FALSE]
col_label = col_label[o]
if(!is.null(annotation_col)) {
annotation_col = annotation_col[o,,drop=FALSE]
}
}
tree_col = cluster_mat(t(mat), col_label , distance = clustering_distance_cols, method = clustering_method)
tree_col = clustering_callback(tree_col, t(mat))
mat = mat[, tree_col$order, drop = FALSE]
fmat = fmat[, tree_col$order, drop = FALSE]
labels_col = labels_col[tree_col$order]
if(!is.na(cutree_cols)){
gaps_col = find_gaps(tree_col, cutree_cols)
}
else {
gaps_col = NULL
}
} else {
tree_col = NA
treeheight_col = 0
}
attr(fmat, "draw") = fmat_draw
# Colors and scales
if(!is.na2(legend_breaks) & !is.na2(legend_labels)){
if(length(legend_breaks) != length(legend_labels)){
stop("Lengths of legend_breaks and legend_labels must be the same")
}
}
if(is.na2(breaks)){
breaks = generate_breaks(as.vector(mat), length(color))
}
if (legend & is.na2(legend_breaks)) {
legend = grid.pretty(range(as.vector(breaks)))
names(legend) = legend
}
else if(legend & !is.na2(legend_breaks)){
legend = legend_breaks[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
if(!is.na2(legend_labels)){
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)
annotation = c(annotation_row, annotation_col)
annotation = annotation[unlist(lapply(annotation, function(x) !is.na2(x)))]
if(length(annotation) != 0){
annotation_colors = generate_annotation_colours(annotation, annotation_colors, drop = drop_levels)
}
else{
annotation_colors = NA
}
labels_row = rownames(mat)
labels_col = colnames(mat)
if(!show_rownames){
labels_row = NULL
}
if(!show_colnames){
labels_col = NULL
}
# Draw heatmap
gt = 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_row = annotation_row, annotation_col = annotation_col, annotation_colors = annotation_colors, annotation_legend = annotation_legend, annotation_names_row = annotation_names_row, annotation_names_col = annotation_names_col, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat, fontsize_number = fontsize_number, number_color = number_color, gaps_row = gaps_row, gaps_col = gaps_col, labels_row = labels_row, labels_col = labels_col, ...)
if(is.na(filename) & !silent){
grid.newpage()
grid.draw(gt)
}
invisible(list(tree_row = tree_row, tree_col = tree_col, gtable = gt))
}
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