R/dimension_reduction.R
In jokergoo/cola: A Framework for Consensus Partitioning
# == title
# Visualize column after dimension reduction
#
# == description
# Visualize samples (the matrix columns) after dimension reduction
#
# == param
# -object A `ConsensusPartition-class` object.
# -k Number of subgroups.
# -top_n Top n rows to use. By default it uses all rows in the original matrix.
# -method Which method to reduce the dimension of the data. ``MDS`` uses `stats::cmdscale`,
# ``PCA`` uses `stats::prcomp`. ``t-SNE`` uses `Rtsne::Rtsne`. ``UMAP`` uses
# `umap::umap`.
# -color_by If annotation table is set, an annotation name can be set here.
# -control A list of parameters for `Rtsne::Rtsne` or `umap::umap`.
# -internal Internally used.
# -nr If number of matrix rows is larger than this value, random ``nr`` rows are used.
# -silhouette_cutoff Cutoff of silhouette score. Data points with values less
# than it will be mapped with cross symbols.
# -remove Whether to remove columns which have less silhouette scores than
# the cutoff.
# -scale_rows Whether to perform scaling on matrix rows.
# -verbose Whether print messages.
# -... Pass to `dimension_reduction,matrix-method`.
#
# == value
# Locations of the points.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola)
# dimension_reduction(golub_cola["ATC", "skmeans"], k = 3)
setMethod(f = "dimension_reduction",
signature = "ConsensusPartition",
definition = function(object, k, top_n = NULL,
method = c("PCA", "MDS", "t-SNE", "UMAP"),
control = list(), color_by = NULL,
internal = FALSE, nr = 5000,
silhouette_cutoff = 0.5, remove = FALSE,
scale_rows = object@scale_rows, verbose = TRUE, ...) {
if(missing(k)) stop_wrap("k needs to be provided.")
cl = as.list(match.call())
# default value
if(! "method" %in% names(cl)) {
method = NULL
if(is.null(method)) {
oe = try(loadNamespace("umap"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("umap package is not installed.\n")
} else {
if(verbose) cat("use UMAP\n")
method = "UMAP"
}
}
if(is.null(method)) {
oe = try(loadNamespace("Rtsne"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("Rtsne package is not installed.\n")
} else {
if(verbose) cat("use t-SNE\n")
method = "t-SNE"
}
}
if(is.null(method)) {
if(verbose) cat("use PCA\n")
method = "PCA"
}
}
method = match.arg(method)
data = object@.env$data[object@row_index, object@column_index, drop = FALSE]
if(!is.null(top_n)) {
top_n = min(c(top_n, nrow(data)))
all_value = object@top_value_list
ind = order(all_value, decreasing = TRUE)[1:top_n]
if(length(ind) > nr) ind = sample(ind, nr)
data = data[ind, , drop = FALSE]
} else {
top_n = nrow(data)
if(nrow(data) > nr) data = data[sample(1:nrow(data), nr), , drop = FALSE]
}
class_df = get_classes(object, k)
l = class_df$silhouette >= silhouette_cutoff
op = par(c("mar", "xpd"))
par(mar = c(4.1, 4.1, 4.1, 6), xpd = NA)
on.exit(par(op))
class_level = sort(as.character(unique(class_df$class)))
n_class = length(class_level)
if(is.null(color_by)) {
col = cola_opt$color_set_2[as.character(class_df$class)]
} else {
if(!color_by %in% colnames(object@anno)) {
stop_wrap("`color_by` should only contain the annotation names.")
}
col = object@anno_col[[color_by]][ object@anno[, color_by] ]
}
if(remove) {
loc = dimension_reduction(data[, l], pch = 16, col = col[l],
cex = 1, main = qq("@{method} on @{top_n} rows with highest @{object@top_value_method} scores@{ifelse(scale_rows, ', rows are scaled', '')}\n@{sum(l)}/@{length(l)} confident samples (silhouette > @{silhouette_cutoff})"),
method = method, control = control, scale_rows = scale_rows, nr = nr, internal = internal, verbose = verbose, ...)
if(!internal) {
if(is.null(color_by)) {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = c(paste0("group", class_level), "ambiguous"),
pch = c(rep(16, n_class), 0),
col = c(cola_opt$color_set_2[class_level], "white"), xjust = 0, yjust = 0.5,
title = "Class", title.adj = 0.1, bty = "n",
text.col = c(rep("black", n_class), "white"))
} else {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = names(object@anno_col[[color_by]]),
pch = 16,
col = object@anno_col[[color_by]], xjust = 0, yjust = 0.5,
title = color_by, title.adj = 0.1, bty = "n")
}
}
} else {
loc = dimension_reduction(data, pch = ifelse(l, 16, 4), col = col,
cex = 1, main = qq("@{method} on @{top_n} rows with highest @{object@top_value_method} scores@{ifelse(scale_rows, ', rows are scaled', '')}\n@{sum(l)}/@{length(l)} confident samples (silhouette > @{silhouette_cutoff})"),
method = method, control = control, scale_rows = scale_rows, nr = nr, internal = internal, verbose = verbose, ...)
if(!internal) {
if(any(!l)) {
if(is.null(color_by)) {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = c(paste0("group", class_level), "ambiguous"),
pch = c(rep(16, n_class), 4),
col = c(cola_opt$color_set_2[class_level], "black"), xjust = 0, yjust = 0.5,
title = "Class", title.adj = 0.1, bty = "n")
} else {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = names(object@anno_col[[color_by]]),
pch = 16,
col = object@anno_col[[color_by]], xjust = 0, yjust = 0.5,
title = color_by, title.adj = 0.1, bty = "n")
}
} else {
if(is.null(color_by)) {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = c(paste0("group", class_level), "ambiguous"),
pch = c(rep(16, n_class), 0),
col = c(cola_opt$color_set_2[class_level], "white"), xjust = 0, yjust = 0.5,
title = "Class", title.adj = 0.1, bty = "n",
text.col = c(rep("black", n_class), "white"))
} else {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = names(object@anno_col[[color_by]]),
pch = 16,
col = object@anno_col[[color_by]], xjust = 0, yjust = 0.5,
title = color_by, title.adj = 0.1, bty = "n")
}
}
}
}
return(invisible(loc))
})
# == title
# Visualize columns after dimension reduction
#
# == param
# -object A numeric matrix.
# -method Which method to reduce the dimension of the data. ``MDS`` uses `stats::cmdscale`,
# ``PCA`` uses `stats::prcomp`. ``t-SNE`` uses `Rtsne::Rtsne`. ``UMAP`` uses
# `umap::umap`.
# -pc Which two principle components to visualize
# -control A list of parameters for `Rtsne::Rtsne` or `umap::umap`.
# -pch Ahape of points.
# -col Color of points.
# -cex Aize of points.
# -main Title of the plot.
# -scale_rows Whether perform scaling on matrix rows.
# -nr If number of matrix rows is larger than this value, random ``nr`` rows are used.
# -internal Internally used.
# -verbose Whether print messages.
#
# == value
# Locations of the points.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
setMethod(f = "dimension_reduction",
signature = "matrix",
definition = function(object,
pch = 16, col = "black", cex = 1, main = NULL,
method = c("PCA", "MDS", "t-SNE", "UMAP"),
pc = NULL, control = list(),
scale_rows = FALSE, nr = 5000,
internal = FALSE, verbose = TRUE) {
data = object
if(nrow(data) > nr) {
data = data[sample(1:nrow(data), nr), , drop = FALSE]
}
cl = as.list(match.call())
# default value
if(! "method" %in% names(cl)) {
method = NULL
if(is.null(method)) {
oe = try(loadNamespace("umap"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("umap package is not installed.\n")
} else {
if(verbose) cat("use UMAP\n")
method = "UMAP"
}
}
if(is.null(method)) {
oe = try(loadNamespace("Rtsne"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("Rtsne package is not installed.\n")
} else {
if(verbose) cat("use t-SNE\n")
method = "t-SNE"
}
}
if(is.null(method)) {
if(verbose) cat("use PCA\n")
method = "PCA"
}
}
method = match.arg(method)[1]
if(is.null(main)) {
main = qq("@{method} on a matrix with @{nrow(object)} rows @{ifelse(scale_rows, ', rows are scaled', '')}")
}
if(is.null(pc)) {
if(method == "PCA") {
pc = 1:2
} else if(method %in% c("UMAP", "t-SNE")) {
pc = seq_len(min(c(10, ncol(data))))
}
} else if(length(pc) == 1) {
pc = 1:pc
}
if(method %in% c("UMAP", "t-SNE")) {
pc = 1:max(pc)
}
if(method %in% c("UMAP", "t-SNE")) {
main = paste0(main, qq(", with @{length(pc)} PCs"))
}
if(scale_rows) data = t(scale(t(data)))
l = apply(data, 1, function(x) any(is.na(x)))
data = data[!l, ]
if(internal) {
omar = par("mar")
par(mar = c(4.1, 4.1, 1, 1))
on.exit(par(mar = omar))
main = NULL
}
if(length(col) == 1) col = rep(col, ncol(data))
## format pch
n_col = length(unique(col))
if(length(pch) == 1) {
pch = rep(pch, ncol(data))
} else if(length(pch) == n_col) {
col2index = split(1:ncol(data), col)
pch2 = numeric(ncol(data))
for(i in seq_along(col2index)) {
pch2[col2index[[i]]] = pch[i]
}
pch = pch2
}
if(method == "MDS") {
loc = cmdscale(dist(t(data)))
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = "Coordinate 1", ylab = "Coordinate 2")
} else if(method == "PCA") {
fit = prcomp_irlba(t(data), n = max(pc))
sm = summary(fit)
prop = sm$importance[2, pc]
loc = fit$x[, pc]
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = qq("PC@{pc[1]} (@{round(prop[1]*100)}%)"), ylab = qq("PC@{pc[2]} (@{round(prop[2]*100)}%)"))
} else if(method == "t-SNE") {
check_pkg("Rtsne", bioc = FALSE)
fit = prcomp_irlba(t(data), n = max(pc))
sm = summary(fit)
loc = fit$x[, pc]
param = list(X = loc)
if(! "perplexity" %in% names(control)) {
control$perplexity = min(30, floor(ncol(data)-1)/3)
}
control$pca = FALSE
param = c(param, control)
param$pca = FALSE
fit = do.call(Rtsne::Rtsne, param)
loc = fit$Y
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = "t-SNE 1", ylab = "t-SNE 2")
} else if(method == "UMAP") {
check_pkg("umap", bioc = FALSE)
fit = prcomp_irlba(t(data), n = max(pc))
sm = summary(fit)
loc = fit$x[, pc]
param = list(d = loc)
if(!"config" %in% names(control)) {
# reset n_neighbors for small dataset
control$config = umap::umap.defaults
control$config$n_neighbors = min(umap::umap.defaults$n_neighbors, 10)
} else {
if(!"n_neighbors" %in% names(control$config)) {
# reset n_neighbors for small dataset
control$config$n_neighbors = min(umap::umap.defaults$n_neighbors, 10)
}
}
param = c(param, control)
fit = do.call(umap::umap, param)
loc = fit$layout
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = "UMAP 1", ylab = "UMAP 2")
}
return(invisible(loc))
})
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# == title
# Visualize column after dimension reduction
#
# == description
# Visualize samples (the matrix columns) after dimension reduction
#
# == param
# -object A `ConsensusPartition-class` object.
# -k Number of subgroups.
# -top_n Top n rows to use. By default it uses all rows in the original matrix.
# -method Which method to reduce the dimension of the data. ``MDS`` uses `stats::cmdscale`,
# ``PCA`` uses `stats::prcomp`. ``t-SNE`` uses `Rtsne::Rtsne`. ``UMAP`` uses
# `umap::umap`.
# -color_by If annotation table is set, an annotation name can be set here.
# -control A list of parameters for `Rtsne::Rtsne` or `umap::umap`.
# -internal Internally used.
# -nr If number of matrix rows is larger than this value, random ``nr`` rows are used.
# -silhouette_cutoff Cutoff of silhouette score. Data points with values less
# than it will be mapped with cross symbols.
# -remove Whether to remove columns which have less silhouette scores than
# the cutoff.
# -scale_rows Whether to perform scaling on matrix rows.
# -verbose Whether print messages.
# -... Pass to `dimension_reduction,matrix-method`.
#
# == value
# Locations of the points.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola)
# dimension_reduction(golub_cola["ATC", "skmeans"], k = 3)
setMethod(f = "dimension_reduction",
signature = "ConsensusPartition",
definition = function(object, k, top_n = NULL,
method = c("PCA", "MDS", "t-SNE", "UMAP"),
control = list(), color_by = NULL,
internal = FALSE, nr = 5000,
silhouette_cutoff = 0.5, remove = FALSE,
scale_rows = object@scale_rows, verbose = TRUE, ...) {
if(missing(k)) stop_wrap("k needs to be provided.")
cl = as.list(match.call())
# default value
if(! "method" %in% names(cl)) {
method = NULL
if(is.null(method)) {
oe = try(loadNamespace("umap"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("umap package is not installed.\n")
} else {
if(verbose) cat("use UMAP\n")
method = "UMAP"
}
}
if(is.null(method)) {
oe = try(loadNamespace("Rtsne"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("Rtsne package is not installed.\n")
} else {
if(verbose) cat("use t-SNE\n")
method = "t-SNE"
}
}
if(is.null(method)) {
if(verbose) cat("use PCA\n")
method = "PCA"
}
}
method = match.arg(method)
data = object@.env$data[object@row_index, object@column_index, drop = FALSE]
if(!is.null(top_n)) {
top_n = min(c(top_n, nrow(data)))
all_value = object@top_value_list
ind = order(all_value, decreasing = TRUE)[1:top_n]
if(length(ind) > nr) ind = sample(ind, nr)
data = data[ind, , drop = FALSE]
} else {
top_n = nrow(data)
if(nrow(data) > nr) data = data[sample(1:nrow(data), nr), , drop = FALSE]
}
class_df = get_classes(object, k)
l = class_df$silhouette >= silhouette_cutoff
op = par(c("mar", "xpd"))
par(mar = c(4.1, 4.1, 4.1, 6), xpd = NA)
on.exit(par(op))
class_level = sort(as.character(unique(class_df$class)))
n_class = length(class_level)
if(is.null(color_by)) {
col = cola_opt$color_set_2[as.character(class_df$class)]
} else {
if(!color_by %in% colnames(object@anno)) {
stop_wrap("`color_by` should only contain the annotation names.")
}
col = object@anno_col[[color_by]][ object@anno[, color_by] ]
}
if(remove) {
loc = dimension_reduction(data[, l], pch = 16, col = col[l],
cex = 1, main = qq("@{method} on @{top_n} rows with highest @{object@top_value_method} scores@{ifelse(scale_rows, ', rows are scaled', '')}\n@{sum(l)}/@{length(l)} confident samples (silhouette > @{silhouette_cutoff})"),
method = method, control = control, scale_rows = scale_rows, nr = nr, internal = internal, verbose = verbose, ...)
if(!internal) {
if(is.null(color_by)) {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = c(paste0("group", class_level), "ambiguous"),
pch = c(rep(16, n_class), 0),
col = c(cola_opt$color_set_2[class_level], "white"), xjust = 0, yjust = 0.5,
title = "Class", title.adj = 0.1, bty = "n",
text.col = c(rep("black", n_class), "white"))
} else {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = names(object@anno_col[[color_by]]),
pch = 16,
col = object@anno_col[[color_by]], xjust = 0, yjust = 0.5,
title = color_by, title.adj = 0.1, bty = "n")
}
}
} else {
loc = dimension_reduction(data, pch = ifelse(l, 16, 4), col = col,
cex = 1, main = qq("@{method} on @{top_n} rows with highest @{object@top_value_method} scores@{ifelse(scale_rows, ', rows are scaled', '')}\n@{sum(l)}/@{length(l)} confident samples (silhouette > @{silhouette_cutoff})"),
method = method, control = control, scale_rows = scale_rows, nr = nr, internal = internal, verbose = verbose, ...)
if(!internal) {
if(any(!l)) {
if(is.null(color_by)) {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = c(paste0("group", class_level), "ambiguous"),
pch = c(rep(16, n_class), 4),
col = c(cola_opt$color_set_2[class_level], "black"), xjust = 0, yjust = 0.5,
title = "Class", title.adj = 0.1, bty = "n")
} else {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = names(object@anno_col[[color_by]]),
pch = 16,
col = object@anno_col[[color_by]], xjust = 0, yjust = 0.5,
title = color_by, title.adj = 0.1, bty = "n")
}
} else {
if(is.null(color_by)) {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = c(paste0("group", class_level), "ambiguous"),
pch = c(rep(16, n_class), 0),
col = c(cola_opt$color_set_2[class_level], "white"), xjust = 0, yjust = 0.5,
title = "Class", title.adj = 0.1, bty = "n",
text.col = c(rep("black", n_class), "white"))
} else {
legend(x = par("usr")[2], y = mean(par("usr")[3:4]), legend = names(object@anno_col[[color_by]]),
pch = 16,
col = object@anno_col[[color_by]], xjust = 0, yjust = 0.5,
title = color_by, title.adj = 0.1, bty = "n")
}
}
}
}
return(invisible(loc))
})
# == title
# Visualize columns after dimension reduction
#
# == param
# -object A numeric matrix.
# -method Which method to reduce the dimension of the data. ``MDS`` uses `stats::cmdscale`,
# ``PCA`` uses `stats::prcomp`. ``t-SNE`` uses `Rtsne::Rtsne`. ``UMAP`` uses
# `umap::umap`.
# -pc Which two principle components to visualize
# -control A list of parameters for `Rtsne::Rtsne` or `umap::umap`.
# -pch Ahape of points.
# -col Color of points.
# -cex Aize of points.
# -main Title of the plot.
# -scale_rows Whether perform scaling on matrix rows.
# -nr If number of matrix rows is larger than this value, random ``nr`` rows are used.
# -internal Internally used.
# -verbose Whether print messages.
#
# == value
# Locations of the points.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
setMethod(f = "dimension_reduction",
signature = "matrix",
definition = function(object,
pch = 16, col = "black", cex = 1, main = NULL,
method = c("PCA", "MDS", "t-SNE", "UMAP"),
pc = NULL, control = list(),
scale_rows = FALSE, nr = 5000,
internal = FALSE, verbose = TRUE) {
data = object
if(nrow(data) > nr) {
data = data[sample(1:nrow(data), nr), , drop = FALSE]
}
cl = as.list(match.call())
# default value
if(! "method" %in% names(cl)) {
method = NULL
if(is.null(method)) {
oe = try(loadNamespace("umap"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("umap package is not installed.\n")
} else {
if(verbose) cat("use UMAP\n")
method = "UMAP"
}
}
if(is.null(method)) {
oe = try(loadNamespace("Rtsne"), silent = TRUE)
if(inherits(oe, "try-error")) {
if(verbose) cat("Rtsne package is not installed.\n")
} else {
if(verbose) cat("use t-SNE\n")
method = "t-SNE"
}
}
if(is.null(method)) {
if(verbose) cat("use PCA\n")
method = "PCA"
}
}
method = match.arg(method)[1]
if(is.null(main)) {
main = qq("@{method} on a matrix with @{nrow(object)} rows @{ifelse(scale_rows, ', rows are scaled', '')}")
}
if(is.null(pc)) {
if(method == "PCA") {
pc = 1:2
} else if(method %in% c("UMAP", "t-SNE")) {
pc = seq_len(min(c(10, ncol(data))))
}
} else if(length(pc) == 1) {
pc = 1:pc
}
if(method %in% c("UMAP", "t-SNE")) {
pc = 1:max(pc)
}
if(method %in% c("UMAP", "t-SNE")) {
main = paste0(main, qq(", with @{length(pc)} PCs"))
}
if(scale_rows) data = t(scale(t(data)))
l = apply(data, 1, function(x) any(is.na(x)))
data = data[!l, ]
if(internal) {
omar = par("mar")
par(mar = c(4.1, 4.1, 1, 1))
on.exit(par(mar = omar))
main = NULL
}
if(length(col) == 1) col = rep(col, ncol(data))
## format pch
n_col = length(unique(col))
if(length(pch) == 1) {
pch = rep(pch, ncol(data))
} else if(length(pch) == n_col) {
col2index = split(1:ncol(data), col)
pch2 = numeric(ncol(data))
for(i in seq_along(col2index)) {
pch2[col2index[[i]]] = pch[i]
}
pch = pch2
}
if(method == "MDS") {
loc = cmdscale(dist(t(data)))
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = "Coordinate 1", ylab = "Coordinate 2")
} else if(method == "PCA") {
fit = prcomp_irlba(t(data), n = max(pc))
sm = summary(fit)
prop = sm$importance[2, pc]
loc = fit$x[, pc]
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = qq("PC@{pc[1]} (@{round(prop[1]*100)}%)"), ylab = qq("PC@{pc[2]} (@{round(prop[2]*100)}%)"))
} else if(method == "t-SNE") {
check_pkg("Rtsne", bioc = FALSE)
fit = prcomp_irlba(t(data), n = max(pc))
sm = summary(fit)
loc = fit$x[, pc]
param = list(X = loc)
if(! "perplexity" %in% names(control)) {
control$perplexity = min(30, floor(ncol(data)-1)/3)
}
control$pca = FALSE
param = c(param, control)
param$pca = FALSE
fit = do.call(Rtsne::Rtsne, param)
loc = fit$Y
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = "t-SNE 1", ylab = "t-SNE 2")
} else if(method == "UMAP") {
check_pkg("umap", bioc = FALSE)
fit = prcomp_irlba(t(data), n = max(pc))
sm = summary(fit)
loc = fit$x[, pc]
param = list(d = loc)
if(!"config" %in% names(control)) {
# reset n_neighbors for small dataset
control$config = umap::umap.defaults
control$config$n_neighbors = min(umap::umap.defaults$n_neighbors, 10)
} else {
if(!"n_neighbors" %in% names(control$config)) {
# reset n_neighbors for small dataset
control$config$n_neighbors = min(umap::umap.defaults$n_neighbors, 10)
}
}
param = c(param, control)
fit = do.call(umap::umap, param)
loc = fit$layout
plot(loc, pch = pch, col = col, cex = cex, main = main, xlab = "UMAP 1", ylab = "UMAP 2")
}
return(invisible(loc))
})
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