R/signatures.R
In jokergoo/cola: A Framework for Consensus Partitioning
Defines functions
find_best_km
Ftest
pamr
samr
one_vs_others
ttest
compare_to_subgroup
knee_finder
elbow_finder
Documented in
find_best_km
# == title
# Get signature rows
#
# == param
# -object A `ConsensusPartition-class` object.
# -k Number of subgroups.
# -col Colors for the main heatmap.
# -silhouette_cutoff Cutoff for silhouette scores. Samples with values
# less than it are not used for finding signature rows. For selecting a
# proper silhouette cutoff, please refer to https://www.stat.berkeley.edu/~s133/Cluster2a.html#tth_tAb1.
# -fdr_cutoff Cutoff for FDR of the difference test between subgroups.
# -top_signatures Top signatures with most significant fdr. Note since fdr might be same for multiple rows,
# the final number of signatures might not be exactly the same as the one that has been set.
# -group_diff Cutoff for the maximal difference between group means.
# -scale_rows Whether apply row scaling when making the heatmap.
# -.scale_mean Internally used.
# -.scale_sd Internally used.
# -row_km Number of groups for performing k-means clustering on rows. By default it is automatically selected.
# -diff_method Methods to get rows which are significantly different between subgroups, see 'Details' section.
# -anno A data frame of annotations for the original matrix columns.
# By default it uses the annotations specified in `consensus_partition` or `run_all_consensus_partition_methods`.
# -anno_col A list of colors (color is defined as a named vector) for the annotations. If ``anno`` is a data frame,
# ``anno_col`` should be a named list where names correspond to the column names in ``anno``.
# -internal Used internally.
# -show_row_dend Whether show row dendrogram.
# -show_column_names Whether show column names in the heatmap.
# -column_names_gp Graphics parameters for column names.
# -use_raster Internally used.
# -plot Whether to make the plot.
# -verbose Whether to print messages.
# -seed Random seed.
# -left_annotation Annotation put on the left of the heatmap. It should be a `ComplexHeatmap::HeatmapAnnotation-class` object.
# The number of items should be the same as the number of the original matrix rows. The subsetting to the significant
# rows are automatically performed on the annotation object.
# -right_annotation Annotation put on the right of the heatmap. Same format as ``left_annotation``.
# -simplify Only used internally.
# -prefix Only used internally.
# -enforce The analysis is cached by default, so that the analysis with the same input will be automatically extracted
# without rerunning them. Set ``enforce`` to ``TRUE`` to enforce the funtion to re-perform the analysis.
# -hash Userd internally.
# -from_hc Is the `ConsensusPartition-class` object a node of a `HierarchicalPartition` object?
# -... Other arguments.
#
# == details
# Basically the function applies statistical test for the difference in subgroups for every
# row. There are following methods which test significance of the difference:
#
# -ttest First it looks for the subgroup with highest mean value, compare to each of the
# other subgroups with t-test and take the maximum p-value. Second it looks
# for the subgroup with lowest mean value, compare to each of the other subgroups
# again with t-test and take the maximum p-values. Later for these two list of p-values
# take the minimal p-value as the final p-value.
# -samr/pamr use SAM (from samr package)/PAM (from pamr package) method to find significantly different rows between subgroups.
# -Ftest use F-test to find significantly different rows between subgroups.
# -one_vs_others For each subgroup i in each row, it uses t-test to compare samples in current
# subgroup to all other samples, denoted as p_i. The p-value for current row is selected as min(p_i).
# -uniquely_high_in_one_group The signatures are defined as, if they are uniquely up-regulated in subgroup A, then it must fit following criterions:
# 1. in a two-group t-test of A ~ other_merged_groups, the statistic must be > 0 (high in group A) and p-value must be significant,
# and 2. for other groups (excluding A), t-test in every pair of groups should not be significant.
#
# ``diff_method`` can also be a self-defined function. The function needs two arguments which are the matrix for the analysis
# and the predicted classes. The function should returns a vector of FDR from the difference test.
#
# == return
# A data frame with more than two columns:
#
# -``which_row``: row index corresponding to the original matrix.
# -``fdr``: the FDR.
# -``km``: the k-means groups if ``row_km`` is set.
# -other_columns: the mean value (depending rows are scaled or not) in each subgroup.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola)
# res = golub_cola["ATC", "skmeans"]
# tb = get_signatures(res, k = 3)
# head(tb)
# get_signatures(res, k = 3, top_signatures = 100)
setMethod(f = "get_signatures",
signature = "ConsensusPartition",
definition = function(object, k,
col = if(scale_rows) c("green", "white", "red") else c("blue", "white", "red"),
silhouette_cutoff = 0.5,
fdr_cutoff = cola_opt$fdr_cutoff,
top_signatures = NULL,
group_diff = cola_opt$group_diff,
scale_rows = object@scale_rows, .scale_mean = NULL, .scale_sd = NULL,
row_km = NULL,
diff_method = c("Ftest", "ttest", "samr", "pamr", "one_vs_others", "uniquely_high_in_one_group"),
anno = get_anno(object),
anno_col = get_anno_col(object),
internal = FALSE,
show_row_dend = FALSE,
show_column_names = FALSE,
column_names_gp = gpar(fontsize = 8),
use_raster = TRUE,
plot = TRUE, verbose = TRUE, seed = 888,
left_annotation = NULL, right_annotation = NULL,
simplify = FALSE, prefix = "", enforce = FALSE, hash = NULL, from_hc = FALSE,
...) {
if(from_hc) {
group_diff = object@.env$group_diff
fdr_cutoff = object@.env$fdr_cutoff
.scale_mean = object@.env$global_row_mean
.scale_sd = object@.env$global_row_sd
k = attr(object, "best_k")
} else {
if(missing(k)) stop_wrap("k needs to be provided.")
}
dotdot = list(...)
p_cutoff = NULL
if("p_cutoff" %in% names(dotdot)) {
p_cutoff = dotdot$p_cutoff
}
from_down_sampling = FALSE
if(inherits(object, "DownSamplingConsensusPartition")) {
from_down_sampling = TRUE
}
class_df = get_classes(object, k)
class_ids = class_df$class
if(is.null(p_cutoff)) {
data = get_matrix(object, include_all_rows = TRUE)
} else {
data = object@.env$data[, object@full_column_index, drop = FALSE]
}
if(!from_down_sampling) {
l = class_df$silhouette >= silhouette_cutoff
} else {
l = class_df$p <= p_cutoff
}
data2 = data[, l, drop = FALSE]
class = class_df$class[l]
column_used_index = which(l)
tb = table(class)
l = as.character(class) %in% names(which(tb <= 1))
data2 = data2[, !l, drop = FALSE]
class = class[!l]
column_used_index = column_used_index[!l]
column_used_logical = rep(FALSE, ncol(data))
column_used_logical[column_used_index] = TRUE
has_ambiguous = sum(!column_used_logical)
n_sample_used = length(class)
if(!from_down_sampling) {
if(verbose) qqcat("@{prefix}* @{n_sample_used}/@{nrow(class_df)} samples (in @{length(unique(class))} classes) remain after filtering by silhouette (>= @{silhouette_cutoff}).\n")
} else {
if(verbose) qqcat("@{prefix}* @{n_sample_used}/@{nrow(class_df)} samples (in @{length(unique(class))} classes) remain after filtering by p-value (<= @{p_cutoff}).\n")
}
tb = table(class)
if(sum(tb > 1) <= 1) {
if(plot) {
grid.newpage()
fontsize = convertUnit(unit(0.1, "npc"), "char", valueOnly = TRUE)*get.gpar("fontsize")$fontsize
grid.text("not enough samples", gp = gpar(fontsize = fontsize))
}
if(verbose) qqcat("@{prefix}* Not enough samples.\n")
return(invisible(data.frame(which_row = integer(0))))
}
if(length(unique(class)) <= 1) {
if(plot) {
grid.newpage()
fontsize = convertUnit(unit(0.1, "npc"), "char", valueOnly = TRUE)*get.gpar("fontsize")$fontsize
grid.text("not enough classes", gp = gpar(fontsize = fontsize))
}
if(verbose) qqcat("@{prefix}* Not enough classes.\n")
return(invisible(data.frame(which_row = integer(0))))
}
do_row_clustering = TRUE
if(inherits(diff_method, "function")) {
if(verbose) qqcat("@{prefix}* calculate row difference between subgroups by user-defined function.\n")
diff_method_fun = diff_method
diff_method = digest(diff_method)
} else {
diff_method = match.arg(diff_method)
}
if(!is.null(top_signatures)) {
if(diff_method %in% c("samr", "pamr")) {
if(verbose) qqcat("`top_signatures` is ignored when `diff_method` is set to samr/pamr.\n")
}
}
if(is.null(hash)) {
hash = digest(list(used_samples = which(l),
class = class,
n_group = k,
diff_method = diff_method,
.scale_mean = .scale_mean,
.scale_sd = .scale_sd,
column_index = object@column_index,
group_diff = group_diff,
fdr_cutoff = fdr_cutoff,
top_signatures = top_signatures,
seed = seed),
algo = "md5")
} else {
if(is.null(object@.env[[paste0("signature_fdr_", hash)]])) {
if(verbose) qqcat("@{prefix} Warning: cannot find cache with hash: @{hash}, generate a new hash.")
hash = digest(list(used_samples = which(l),
class = class,
n_group = k,
diff_method = diff_method,
.scale_mean = .scale_mean,
.scale_sd = .scale_sd,
column_index = object@column_index,
group_diff = group_diff,
fdr_cutoff = fdr_cutoff,
top_signatures = top_signatures,
seed = seed),
algo = "md5")
}
}
nm = paste0("signature_fdr_", hash)
if(verbose) qqcat("@{prefix}* cache hash: @{hash} (seed @{seed}).\n")
if(enforce) object@.env[[nm]] = NULL
find_signature = TRUE
if(!is.null(object@.env[[nm]])) {
if(diff_method == "samr") {
if(object@.env[[nm]]$diff_method == "samr" &&
object@.env[[nm]]$n_sample_used == n_sample_used &&
abs(object@.env[[nm]]$fdr_cutoff - fdr_cutoff) < 1e-10) {
fdr = object@.env[[nm]]$fdr
find_signature = FALSE
}
} else if(diff_method == "pamr") {
if(object@.env[[nm]]$diff_method == "pamr" &&
object@.env[[nm]]$n_sample_used == n_sample_used &&
abs(object@.env[[nm]]$fdr_cutoff - fdr_cutoff) < 1e-10) {
fdr = object@.env[[nm]]$fdr
find_signature = FALSE
}
} else {
if(object@.env[[nm]]$diff_method == diff_method &&
object@.env[[nm]]$n_sample_used == n_sample_used) {
fdr = object@.env[[nm]]$fdr
find_signature = FALSE
}
}
}
if(verbose) qqcat("@{prefix}* calculating row difference between subgroups by @{diff_method}.\n")
if(find_signature) {
if(diff_method == "ttest") {
fdr = ttest(data2, class)
} else if(diff_method == "samr") {
fdr = samr(data2, class, fdr.output = fdr_cutoff)
} else if(diff_method == "Ftest") {
fdr = Ftest(data2, class)
} else if(diff_method == "pamr") {
fdr = pamr(data2, class, fdr.ouput = fdr_cutoff)
} else if(diff_method == "one_vs_others") {
fdr = one_vs_others(data2, class)
} else {
fdr = diff_method_fun(data2, class)
}
} else {
if(verbose) qqcat("@{prefix} - row difference is extracted from cache.\n")
}
if(!is.null(top_signatures)) {
fdr_cutoff = fdr[order(fdr)[min(length(fdr), top_signatures)]]
}
if(scale_rows && !is.null(object@.env[[nm]]$row_order_scaled)) {
row_order = object@.env[[nm]]$row_order_scaled
if(verbose) qqcat("@{prefix} - row order for the scaled matrix is extracted from cache.\n")
do_row_clustering = FALSE
} else if(!scale_rows && !is.null(object@.env[[nm]]$row_order_unscaled)) {
row_order = object@.env[[nm]]$row_order_unscaled
if(verbose) qqcat("@{prefix} - row order for the unscaled matrix is extracted from cache.\n")
do_row_clustering = FALSE
}
object@.env[[nm]]$diff_method = diff_method
object@.env[[nm]]$fdr_cutoff = fdr_cutoff
object@.env[[nm]]$fdr = fdr
object@.env[[nm]]$n_sample_used = n_sample_used
object@.env[[nm]]$group_diff = group_diff
object@.env[[nm]]$.scale_mean = .scale_mean
object@.env[[nm]]$.scale_sd = .scale_sd
# filter by fdr
fdr[is.na(fdr)] = 1
p_value = attr(fdr, "p_value")
l_fdr = fdr <= fdr_cutoff
mat = data[l_fdr, , drop = FALSE]
fdr2 = fdr[l_fdr]
if(!is.null(p_value)) {
p_value = p_value[l_fdr]
}
if(!is.null(.scale_mean)) {
.scale_mean = .scale_mean[l_fdr]
}
if(!is.null(.scale_sd)) {
.scale_sd = .scale_sd[l_fdr]
}
if(!is.null(left_annotation)) left_annotation = left_annotation[l_fdr, ]
if(!is.null(right_annotation)) right_annotation = right_annotation[l_fdr, ]
returned_df = data.frame(which_row = which(l_fdr), fdr = fdr2)
if(!is.null(p_value)) {
returned_df$p_value = p_value
}
rownames(returned_df) = rownames(object)[l_fdr]
attr(returned_df, "sample_used") = column_used_logical
attr(returned_df, "hash") = hash
# filter by group_diff
mat1 = mat[, column_used_logical, drop = FALSE]
if(nrow(mat) == 1) {
group_mean = rbind(tapply(mat1, class, mean))
} else {
group_mean = do.call("cbind", tapply(seq_len(ncol(mat1)), class, function(ind) {
rowMeans(mat1[, ind, drop = FALSE])
}))
}
colnames(group_mean) = paste0("mean_", colnames(group_mean))
returned_df = cbind(returned_df, group_mean)
returned_df$group_diff = apply(group_mean, 1, function(x) max(x) - min(x))
if(scale_rows) {
mfoo = mat[, column_used_logical, drop = FALSE]
if(!is.null(.scale_mean) && !is.null(.scale_sd)) {
mat1_scaled = (mfoo - .scale_mean)/.scale_sd
} else {
mat1_scaled = (mfoo - rowMeans(mfoo))/rowSds(mfoo)
}
if(nrow(mat) == 1) {
group_mean_scaled = rbind(tapply(mat1_scaled, class, mean))
} else {
group_mean_scaled = do.call("cbind", tapply(seq_len(ncol(mat1_scaled)), class, function(ind) {
rowMeans(mat1_scaled[, ind, drop = FALSE])
}))
}
colnames(group_mean_scaled) = paste0("scaled_mean_", colnames(group_mean_scaled))
returned_df = cbind(returned_df, group_mean_scaled)
returned_df$group_diff_scaled = apply(group_mean_scaled, 1, function(x) max(x) - min(x))
}
if(group_diff > 0) {
if(scale_rows) {
l_diff = returned_df$group_diff_scaled >= group_diff
} else {
l_diff = returned_df$group_diff >= group_diff
}
mat = mat[l_diff, , drop = FALSE]
mat1 = mat1[l_diff, , drop = FALSE]
returned_df = returned_df[l_diff, , drop = FALSE]
}
returned_obj = returned_df
rownames(returned_obj) = NULL
attr(returned_obj, "sample_used") = column_used_logical
attr(returned_obj, "hash") = hash
if(simplify && !plot) {
return(returned_df)
}
## add k-means
row_km_fit = NULL
if(!internal) {
if(nrow(mat1) > 10) {
do_kmeans = TRUE
if(scale_rows) {
mat_for_km = t(scale(t(mat1)))
row_km_fit = object@.env[[nm]]$row_km_fit_scaled
} else {
mat_for_km = mat1
row_km_fit = object@.env[[nm]]$row_km_fit_unscaled
}
if(nrow(mat_for_km) > 5000) {
set.seed(seed)
mat_for_km2 = mat_for_km[sample(nrow(mat_for_km), 5000), , drop = FALSE]
} else {
mat_for_km2 = mat_for_km
}
if(!is.null(row_km_fit)) {
if(is.null(row_km) || identical(as.integer(row_km), length(row_km_fit$size))) {
returned_obj$km = apply(pdist(row_km_fit$centers, mat_for_km, as.integer(1)), 2, which.min)
do_kmeans = FALSE
if(verbose) qqcat("@{prefix}* use k-means partition that are already calculated in previous runs.\n")
}
}
if(do_kmeans) {
set.seed(seed)
if(is.null(row_km)) {
row_km = guess_best_km(mat_for_km2)
if(length(unique(class)) == 1) row_km = 1
if(length(unique(class)) == 2) row_km = min(row_km, 2)
}
if(row_km > 1) {
row_km_fit = kmeans(mat_for_km2, centers = row_km)
returned_obj$km = apply(pdist(row_km_fit$centers, mat_for_km, as.integer(1)), 2, which.min)
if(scale_rows) {
object@.env[[nm]]$row_km_fit_scaled = row_km_fit
} else {
object@.env[[nm]]$row_km_fit_unscaled = row_km_fit
}
}
if(verbose) qqcat("@{prefix}* split rows into @{row_km} groups by k-means clustering.\n")
}
}
}
if(verbose) {
if(is.null(top_signatures)) {
qqcat("@{prefix}* @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) under fdr < @{fdr_cutoff}, group_diff > @{group_diff}.\n")
} else {
qqcat("@{prefix}* @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with most significant fdr, group_diff > @{group_diff}.\n")
}
}
if(nrow(mat) == 0) {
if(plot) {
grid.newpage()
fontsize = convertUnit(unit(0.1, "npc"), "char", valueOnly = TRUE)*get.gpar("fontsize")$fontsize
grid.text("no sigatures", gp = gpar(fontsize = fontsize))
}
return(invisible(data.frame(which_row = integer(0))))
}
if(!plot) {
return(invisible(returned_obj))
}
set.seed(seed)
more_than_2k = FALSE
if(!is.null(object@.env[[nm]]$row_index)) {
if(verbose) qqcat("@{prefix} - use the signatures that are already generated in previous runs.\n")
row_index = object@.env[[nm]]$row_index
mat1 = mat[row_index, column_used_logical, drop = FALSE]
mat2 = mat[row_index, !column_used_logical, drop = FALSE]
more_than_2k = TRUE
if(!is.null(left_annotation)) left_annotation = left_annotation[row_index, ]
if(!is.null(right_annotation)) right_annotation = right_annotation[row_index, ]
} else if(nrow(mat) > 2000) {
more_than_2k = TRUE
row_index = sample(1:nrow(mat), 2000)
object@.env[[nm]]$row_index = row_index
# mat1 = mat[order(fdr2)[1:top_k_genes], column_used_logical, drop = FALSE]
# mat2 = mat[order(fdr2)[1:top_k_genes], !column_used_logical, drop = FALSE]
mat1 = mat[row_index, column_used_logical, drop = FALSE]
mat2 = mat[row_index, !column_used_logical, drop = FALSE]
# group2 = group2[order(fdr2)[1:top_k_genes]]
if(verbose) qqcat("@{prefix} - randomly sample 2000 signatures.\n")
if(!is.null(left_annotation)) left_annotation = left_annotation[row_index, ]
if(!is.null(right_annotation)) right_annotation = right_annotation[row_index, ]
} else {
row_index = seq_len(nrow(mat))
mat1 = mat[, column_used_logical, drop = FALSE]
mat2 = mat[, !column_used_logical, drop = FALSE]
}
base_mean = rowMeans(mat1)
if(nrow(mat) == 1) {
group_mean = matrix(tapply(mat1, class, mean), nrow = 1)
} else {
group_mean = do.call("cbind", tapply(seq_len(ncol(mat1)), class, function(ind) {
rowMeans(mat1[, ind, drop = FALSE])
}))
}
rel_diff = (rowMaxs(group_mean) - rowMins(group_mean))/base_mean/2
if(is.null(anno)) {
bottom_anno1 = NULL
} else {
if(is.atomic(anno)) {
anno_nm = deparse(substitute(anno))
anno = data.frame(anno)
colnames(anno) = anno_nm
if(!is.null(anno_col)) {
if(is.atomic(anno_col)) {
anno_col = list(anno_col)
names(anno_col) = anno_nm
}
}
} else if(ncol(anno) == 1) {
if(!is.null(anno_col)) {
if(is.atomic(anno_col)) {
anno_col = list(anno_col)
names(anno_col) = colnames(anno)
}
}
}
if(is.null(anno_col)) {
bottom_anno1 = HeatmapAnnotation(df = anno[column_used_logical, , drop = FALSE],
show_annotation_name = !has_ambiguous & !internal, annotation_name_side = "right")
} else {
bottom_anno1 = HeatmapAnnotation(df = anno[column_used_logical, , drop = FALSE], col = anno_col,
show_annotation_name = !has_ambiguous & !internal, annotation_name_side = "right")
}
}
if(scale_rows) {
scaled_mean = base_mean
scaled_sd = rowSds(mat1)
scaled_mat1 = t(scale(t(mat1)))
scaled_mat2 = mat2
if(has_ambiguous) {
for(i in seq_len(nrow(mat2))) {
scaled_mat2[i, ] = (scaled_mat2[i, ] - scaled_mean[i])/scaled_sd[i]
}
}
use_mat1 = scaled_mat1
use_mat2 = scaled_mat2
use_mat1[is.infinite(use_mat1)] = 0
use_mat1[is.na(use_mat1)] = 0
use_mat2[is.infinite(use_mat2)] = 0
use_mat2[is.na(use_mat2)] = 0
mat_range = quantile(abs(scaled_mat1), 0.95, na.rm = TRUE)
col_fun = colorRamp2(c(-mat_range, 0, mat_range), col)
heatmap_name = "z-score"
} else {
use_mat1 = mat1
use_mat2 = mat2
mat_range = quantile(mat1, c(0.05, 0.95))
col_fun = colorRamp2(c(mat_range[1], mean(mat_range), mat_range[2]), col)
heatmap_name = "Value"
}
if(has_ambiguous) {
class2 = class_df$class[!column_used_logical]
if(is.null(anno)) {
bottom_anno2 = NULL
} else {
anno_col = lapply(bottom_anno1@anno_list, function(anno) {
if(is.null(anno@color_mapping)) {
return(NULL)
} else {
if(anno@color_mapping@type == "discrete") {
if(anno@name %in% names(object@anno_col)) {
object@anno_col[[anno@name]]
} else {
anno@color_mapping@colors
}
} else {
anno@color_mapping@col_fun
}
}
})
names(anno_col) = names(bottom_anno1@anno_list)
anno_col = anno_col[!sapply(anno_col, is.null)]
if(!is.null(object@anno_col)) {
nmd = setdiff(names(object@anno_col), names(anno_col))
if(length(nmd)) {
anno_col[nmd] = object@anno_col[nmd]
}
}
bottom_anno2 = HeatmapAnnotation(df = anno[!column_used_logical, , drop = FALSE], col = anno_col,
show_annotation_name = !internal, annotation_name_side = "right")
}
}
if(is.null(p_cutoff)) {
silhouette_range = range(class_df$silhouette)
silhouette_range[2] = 1
} else {
p_range = c(0, 1)
}
if(verbose) qqcat("@{prefix}* making heatmaps for signatures.\n")
row_split = NULL
if(!internal) {
if(scale_rows) {
row_km_fit = object@.env[[nm]]$row_km_fit_scaled
} else {
row_km_fit = object@.env[[nm]]$row_km_fit_unscaled
}
if(!is.null(row_km_fit)) {
row_split = factor(returned_obj$km[row_index], levels = sort(unique(returned_obj$km[row_index])))
}
}
# group2 = factor(group2, levels = sort(unique(group2)))
# ht_list = Heatmap(group2, name = "Group", show_row_names = FALSE, width = unit(5, "mm"), col = cola_opt$color_set_2)
ht_list = NULL
membership_mat = get_membership(object, k)
prop_col_fun = colorRamp2(c(0, 1), c("white", "red"))
if(from_down_sampling) {
class_df_logp = class_df$p
class_df_logp[class_df_logp == 0] = 0.001
class_df_logp = -log10(class_df_logp)
p_range = range(class_df_logp)
p_range[1] = 0
}
if(internal) {
ha1 = HeatmapAnnotation(Prob = membership_mat[column_used_logical, ],
Class = class_df$class[column_used_logical],
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !has_ambiguous & !internal,
annotation_name_side = "right",
show_legend = TRUE)
} else {
if(simplify) {
if(!from_down_sampling) {
ha1 = HeatmapAnnotation(
Class = class_df$class[column_used_logical],
silhouette = anno_barplot(class_df$silhouette[column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[column_used_logical] >= silhouette_cutoff, "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = !has_ambiguous & !internal,
annotation_name_side = "right",
show_legend = TRUE)
} else {
ha1 = HeatmapAnnotation(
Class = class_df$class[column_used_logical],
p_prediction = anno_barplot(class_df_logp[column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[column_used_logical] >= -log10(p_cutoff), "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = !has_ambiguous & !internal,
annotation_name_side = "right",
show_legend = TRUE)
}
} else {
if(!from_down_sampling) {
ha1 = HeatmapAnnotation(Prob = membership_mat[column_used_logical, ],
Class = class_df$class[column_used_logical],
silhouette = anno_barplot(class_df$silhouette[column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[column_used_logical] >= silhouette_cutoff, "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !has_ambiguous & !internal & c(TRUE, TRUE, FALSE),
annotation_name_side = "right",
show_legend = TRUE)
} else {
ha1 = HeatmapAnnotation(
Class = class_df$class[column_used_logical],
p_prediction = anno_barplot(class_df_logp[column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[column_used_logical] >= -log10(p_cutoff), "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !has_ambiguous & !internal & c(TRUE, FALSE),
annotation_name_side = "right",
show_legend = TRUE)
}
}
}
ht_list = ht_list + Heatmap(use_mat1, name = heatmap_name, col = col_fun,
top_annotation = ha1, row_split = row_split,
cluster_columns = TRUE, cluster_column_slices = FALSE, cluster_row_slices = FALSE,
column_split = factor(class_df$class[column_used_logical], levels = sort(unique(class_df$class[column_used_logical]))),
show_column_dend = FALSE,
show_row_names = FALSE, show_row_dend = show_row_dend, column_title = {if(internal) NULL else qq("@{ncol(use_mat1)} confident samples")},
use_raster = use_raster, raster_by_magick = requireNamespace("magick", quietly = TRUE),
bottom_annotation = bottom_anno1, show_column_names = show_column_names, column_names_gp = column_names_gp,
left_annotation = left_annotation, right_annotation = {if(has_ambiguous) NULL else right_annotation})
all_value_positive = !any(data < 0)
if(scale_rows && all_value_positive && !simplify) {
ht_list = ht_list + Heatmap(base_mean, show_row_names = FALSE, name = "base_mean", width = unit(5, "mm"), show_column_names = !internal, column_names_gp = column_names_gp) +
Heatmap(rel_diff, col = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red")),
show_row_names = FALSE, show_column_names = !internal, column_names_gp = column_names_gp, name = "rel_diff", width = unit(5, "mm"))
}
if(has_ambiguous) {
if(internal) {
ha2 = HeatmapAnnotation(Prob = membership_mat[!column_used_logical, ,drop = FALSE],
Class = class_df$class[!column_used_logical],
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !internal,
annotation_name_side = "right",
show_legend = FALSE)
} else {
if(simplify) {
if(!from_down_sampling) {
ha2 = HeatmapAnnotation(
Class = class_df$class[!column_used_logical],
silhouette2 = anno_barplot(class_df$silhouette[!column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "grey", "grey"),
col = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = c(TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
} else {
ha2 = HeatmapAnnotation(
Class = class_df$class[!column_used_logical],
p_prediction2 = anno_barplot(class_df_logp[!column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[!column_used_logical] >= -log10(p_cutoff), "grey", "grey"),
col = ifelse(class_df_logp[!column_used_logical] <= -log10(p_cutoff), "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = c(TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
}
} else {
if(!from_down_sampling) {
ha2 = HeatmapAnnotation(Prob = membership_mat[!column_used_logical, ,drop = FALSE],
Class = class_df$class[!column_used_logical],
silhouette2 = anno_barplot(class_df$silhouette[!column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "grey", "grey"),
col = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = c(TRUE, TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
} else {
ha2 = HeatmapAnnotation(
Class = class_df$class[!column_used_logical],
p_prediction2 = anno_barplot(class_df_logp[!column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[!column_used_logical] >= -log10(p_cutoff), "grey", "grey"),
col = ifelse(class_df_logp[!column_used_logical] >= -log10(p_cutoff), "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = c(TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
}
}
}
ht_list = ht_list + Heatmap(use_mat2, name = paste0(heatmap_name, 2), col = col_fun,
top_annotation = ha2,
cluster_columns = TRUE, show_column_dend = FALSE,
show_row_names = FALSE, show_row_dend = FALSE, show_heatmap_legend = FALSE,
use_raster = use_raster, raster_by_magick = requireNamespace("magick", quietly = TRUE),
bottom_annotation = bottom_anno2, show_column_names = show_column_names, column_names_gp = column_names_gp,
right_annotation = right_annotation)
}
if(has_ambiguous) {
lgd = Legend(title = "Status (barplots)", labels = c("confident", "ambiguous"), legend_gp = gpar(fill = c("black", "grey")))
heatmap_legend_list = list(lgd)
} else {
heatmap_legend_list = NULL
}
if(do_row_clustering) {
if(is.null(top_signatures)) {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with fdr < @{fdr_cutoff}@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
} else {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with most significant fdr@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
}
ht_list = draw(ht_list, main_heatmap = heatmap_name, column_title = column_title,
show_heatmap_legend = !internal, show_annotation_legend = !internal,
heatmap_legend_list = heatmap_legend_list
)
row_order = row_order(ht_list)
if(!is.list(row_order)) row_order = list(row_order)
if(scale_rows) {
object@.env[[nm]]$row_order_scaled = do.call("c", row_order)
} else {
object@.env[[nm]]$row_order_unscaled = do.call("c", row_order)
}
} else {
if(verbose) qqcat("@{prefix} - use row order from cache.\n")
if(is.null(top_signatures)) {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with fdr < @{fdr_cutoff}@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
} else {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with most significant fdr@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
}
draw(ht_list, main_heatmap = heatmap_name, column_title = column_title,
show_heatmap_legend = !internal, show_annotation_legend = !internal,
cluster_rows = FALSE, row_order = row_order, heatmap_legend_list = heatmap_legend_list,
row_title = {if(length(unique(row_split)) <= 1) NULL else qq("k-means with @{length(unique(row_split))} groups")}
)
}
# the cutoff
# https://www.stat.berkeley.edu/~s133/Cluster2a.html
if(!internal) {
if(is.null(p_cutoff)) {
if(!has_ambiguous) {
decorate_annotation("silhouette", slice = length(unique(class_df$class[column_used_logical])), {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(c(silhouette_cutoff, silhouette_cutoff), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(!has_ambiguous) grid.text("Silhouette\nscore", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
if(has_ambiguous) {
decorate_annotation("silhouette2", {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(c(silhouette_cutoff, silhouette_cutoff), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(has_ambiguous) grid.text("Silhouette\nscore", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
} else {
if(!has_ambiguous) {
decorate_annotation("p_prediction", slice = length(unique(class_df$class[column_used_logical])), {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(-log10(c(p_cutoff, p_cutoff)), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(!has_ambiguous) grid.text("-log10(prediction\n p-value)", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
if(has_ambiguous) {
decorate_annotation("p_prediction2", {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(-log10(c(p_cutoff, p_cutoff)), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(has_ambiguous) grid.text("-log10(prediction\n p-value)", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
}
}
return(invisible(returned_obj))
})
# https://stackoverflow.com/questions/2018178/finding-the-best-trade-off-point-on-a-curve
elbow_finder <- function(x_values, y_values) {
# Max values to create line
max_x_x <- max(x_values)
max_x_y <- y_values[which.max(x_values)]
max_y_y <- max(y_values)
max_y_x <- x_values[which.max(y_values)]
max_df <- data.frame(x = c(max_y_x, max_x_x), y = c(max_y_y, max_x_y))
# Creating straight line between the max values
fit <- lm(max_df$y ~ max_df$x)
# Distance from point to line
distances <- c()
for(i in 1:length(x_values)) {
distances <- c(distances, abs(coef(fit)[2]*x_values[i] - y_values[i] + coef(fit)[1]) / sqrt(coef(fit)[2]^2 + 1^2))
}
# Max distance point
x_max_dist <- x_values[which.max(distances)]
y_max_dist <- y_values[which.max(distances)]
return(c(x_max_dist, y_max_dist))
}
# https://raghavan.usc.edu//papers/kneedle-simplex11.pdf
knee_finder = function(x, y) {
n = length(x)
a = (y[n] - y[1])/(x[n] - x[1])
b = y[1] - a*x[1]
d = a*x + b - y
x[which.max(d)]
}
compare_to_subgroup = function(mat, class, which = "highest") {
check_pkg("genefilter", bioc = TRUE)
od = order(class)
class = class[od]
mat = mat[, od, drop = FALSE]
class = as.numeric(factor(class))
group = apply(mat, 1, function(x) {
group_mean = tapply(x, class, mean)
if(which == "highest") {
which.max(group_mean)
} else {
which.min(group_mean)
}
})
# class and subgroup_index are all numeric
compare_to_one_subgroup = function(mat, class, subgroup_index) {
oc = setdiff(class, subgroup_index)
pmat = matrix(NA, nrow = nrow(mat), ncol = length(oc))
for(i in seq_along(oc)) {
l = class == subgroup_index | class == oc[i]
m2 = mat[, l, drop = FALSE]
fa = factor(class[l])
pmat[, i] = genefilter::rowttests(m2, fa)[, "p.value"]
}
rowMaxs(pmat, na.rm = TRUE)
}
p = tapply(seq_len(nrow(mat)), group, function(ind) {
compare_to_one_subgroup(mat[ind, , drop = FALSE], class, group[ind][1])
})
p2 = numeric(length(group))
for(i in seq_along(p)) {
p2[group == i] = p[[i]]
}
return(p2)
}
ttest = function(mat, class) {
p1 = compare_to_subgroup(mat, class, "highest")
p2 = compare_to_subgroup(mat, class, "lowest")
p = pmin(p1, p2, na.rm = TRUE)
fdr = p.adjust(p, method = "BH")
fdr[is.na(fdr)] = Inf
fdr[is.infinite(fdr)] = Inf
attr(fdr, "p_value") = p
fdr
}
one_vs_others = function(mat, class) {
check_pkg("genefilter", bioc = TRUE)
le = unique(class)
dfl = list()
for(x in le) {
fa = as.vector(class)
fa[class == x] = "a"
fa[class != x] = "b"
fa = factor(fa, levels = c("a", "b"))
dfl[[x]] = genefilter::rowttests(mat, fa)[, "p.value"]
}
df = do.call("cbind", dfl)
p = rowMins(df)
fdr = p.adjust(p, "BH")
attr(fdr, "p_value") = p
fdr
}
samr = function(mat, class, ...) {
check_pkg("samr", bioc = FALSE)
on.exit(if(sink.number()) sink(NULL))
class = as.numeric(factor(class))
n_class = length(unique(class))
tempf = tempfile()
sink(tempf)
if(n_class == 2) {
samfit = samr::SAM(mat, class, resp.type = "Two class unpaired", nperms = 1000, ...)
} else {
samfit = samr::SAM(mat, class, resp.type = "Multiclass", nperms = 1000, ...)
}
sink(NULL)
file.remove(tempf)
sig_index = NULL
if(!is.null(samfit$siggenes.table$genes.up)) {
id = samfit$siggenes.table$genes.up
if(is.null(dim(id))) id = matrix(id, nrow = 1)
sig_index = c(sig_index, id[, 2])
}
if(!is.null(samfit$siggenes.table$genes.lo)) {
id = samfit$siggenes.table$genes.lo
if(is.null(dim(id))) id = matrix(id, nrow = 1)
sig_index = c(sig_index, id[, 2])
}
sig_index = as.numeric(sig_index)
fdr = rep(1, nrow(mat))
fdr[sig_index] = 0
return(fdr)
}
pamr = function(mat, class, fdr.cutoff = 0.1, ...) {
check_pkg("pamr", bioc = FALSE)
on.exit(if(sink.number()) sink(NULL))
class = as.numeric(factor(class))
tempf = tempfile()
sink(tempf)
mydata <- list(x=mat, y=class, geneid = rownames(mat))
mydata.fit <- pamr::pamr.train(mydata)
mydata.cv <- pamr::pamr.cv(mydata.fit, mydata)
mydata.fdr <- pamr::pamr.fdr(mydata.fit, mydata)
threshold = min(mydata.fdr$results[mydata.fdr$results[,"Median FDR"] < fdr.cutoff, "Threshold"])
mydata.genelist <- pamr::pamr.listgenes(mydata.fit, mydata, threshold = threshold, fitcv=mydata.cv)
sink(NULL)
file.remove(tempf)
fdr = rep(1, nrow(mat))
fdr[rownames(mat) %in% mydata.genelist[,"id"]] = 0
return(fdr)
}
Ftest = function(mat, class) {
check_pkg("genefilter", bioc = TRUE)
rownames(mat) = NULL
p = genefilter::rowFtests(mat, factor(class))[, "p.value"]
fdr = p.adjust(p, "BH")
fdr[is.na(fdr)] = Inf
attr(fdr, "p_value") = p
return(fdr)
}
uniquely_high_in_one_group = function (mat, class) {
le = unique(class)
dfl = list()
for (x in le) {
fa = as.vector(class)
fa[class == x] = "a"
fa[class != x] = "b"
fa = factor(fa, levels = c("a", "b"))
df = genefilter::rowttests(mat, fa)
l = df[, "statistic"] < 0; l[is.na(l)] = FALSE
df[l, "p.value"] = NA
# then for the remaining subgroups, they show no difference
if(length(le) > 2) {
l = class != x
p1 = compare_to_subgroup(mat[, l, drop = FALSE], class[l], "highest")
p2 = compare_to_subgroup(mat[, l, drop = FALSE], class[l], "lowest")
p = pmin(p1, p2, na.rm = TRUE)
df[p < 0.05, "p.value"] = NA
}
dfl[[x]] = df[, "p.value"]
}
df = do.call("cbind", dfl)
p = rowMins(df, na.rm = TRUE)
fdr = p.adjust(p, "BH")
attr(fdr, "p_value") = p
fdr
}
# test_row_diff_fun = function(fun, fdr_cutoff = 0.1) {
# set.seed(100)
# x = matrix(rnorm(1000 * 20), ncol = 20)
# rownames(x) = rep(paste0("gene1", 1:1000))
# dd = sample(1:1000, size = 100)
# u = matrix(2 * rnorm(100), ncol = 10, nrow = 100)
# x[dd, 11:20] = x[dd, 11:20] + u
# row_diff = rep("no", 1000)
# row_diff[dd] = "yes"
# y = c(rep(1, 10), rep(2, 10))
# fdr = fun(x, y)
# ht = Heatmap(x, top_annotation = HeatmapAnnotation(foo = as.character(y), col = list(foo = c("1" = "blue", "2" = "red"))), show_row_names = FALSE) +
# Heatmap(row_diff, name = "diff", col = c("yes" = "red", "no" = "white"), width = unit(5, "mm")) +
# Heatmap(fdr, name = "fdr", width = unit(5, "mm"), show_row_names = FALSE)
# draw(ht, split = fdr < fdr_cutoff)
# }
# title
# Density for the signatures
#
# == param
# -object A `ConsensusPartition-class` object.
# -k number of partitions
# -... pass to `get_signatures,ConsensusPartition-method`
#
# == details
# The function makes density distributio nf of signatures in all columns.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# setMethod(f = "signature_density",
# signature = "ConsensusPartition",
# definition = function(object, k, ...) {
# cl = get_class(object, k = k)$class
# data = object@.env$data[, object@column_index, drop = FALSE]
# all_den_list = lapply(seq_len(ncol(data)), function(i) {
# x = data[, i]
# density(x)
# })
# x_range = range(unlist(lapply(all_den_list, function(x) x$x)))
# y_range = range(unlist(lapply(all_den_list, function(x) x$y)))
# x = get_signatures(object, k = k, plot = FALSE, verbose = FALSE, ...)
# gp_tb = table(x$df$group)
# n_gp = sum(gp_tb > 5)
# gp_tb = gp_tb[gp_tb > 5]
# op = par(no.readonly = TRUE)
# par(mfrow = c(n_gp + 1, 1), mar = c(2, 4, 1, 3))
# plot(NULL, type = "n", xlim = x_range, ylim = y_range, ylab = "density", xlab = NULL)
# for(i in 1:ncol(data)) {
# lines(all_den_list[[i]], col = cola_opt$color_set_2[cl[i]], lwd = 1)
# }
# mtext("all rows", side = 4, line = 1)
# gp = x$df$group
# for(j in as.numeric(names(gp_tb))) {
# gp2 = gp[gp == as.character(j)]
# all_den_list = lapply(seq_len(ncol(data)), function(i) density(data[names(gp2), i]))
# # x_range = range(unlist(lapply(all_den_list, function(x) x$x)))
# y_range = range(unlist(lapply(all_den_list, function(x) x$y)))
# plot(NULL, type = "n", xlim = x_range, ylim = y_range, ylab = "density", xlab = NULL)
# for(i in 1:ncol(data)) {
# lines(all_den_list[[i]], col = cola_opt$color_set_2[cl[i]], lwd = ifelse(cl[i] == j, 2, 0.5))
# }
# mtext(qq("subgroup @{j}/@{k}"), side = 4, line = 1)
# }
# par(op)
# })
# == title
# Compare Signatures from Different k
#
# == param
# -object A `ConsensusPartition-class` object.
# -k Number of subgroups. Value should be a vector.
# -verbose Whether to print message.
# -... Other arguments passed to `get_signatures,ConsensusPartition-method`.
#
# == details
# It plots an Euler diagram showing the overlap of signatures from different k.
#
# == example
# \donttest{
# data(golub_cola)
# res = golub_cola["ATC", "skmeans"]
# compare_signatures(res)
# }
setMethod(f = "compare_signatures",
signature = "ConsensusPartition",
definition = function(object, k = object@k, verbose = interactive(), ...) {
check_pkg("eulerr", bioc = FALSE)
sig_list = sapply(k, function(x) {
tb = get_signatures(object, k = x, verbose = verbose, ..., plot = FALSE)
if(is.null(tb)) {
return(integer(0))
} else {
return(tb$which_row)
}
})
l = sapply(sig_list, length) > 0
if(any(l) && verbose) {
qqcat("Following k have no signature found: \"@{paste(k[l], collapse=', ')}\"\n")
}
sig_list = sig_list[l]
names(sig_list) = paste(k[l], "-group", sep = "")
plot(eulerr::euler(sig_list), legend = TRUE, quantities = TRUE, main = "Signatures from different k")
})
# == title
# Find a best k for the k-means clustering
#
# == param
# -mat A matrix where k-means clustering is executed by rows.
# -max_km Maximal k to try.
#
# == details
# The best k is determined by looking for the knee/elbow of the WSS curve (within-cluster sum of square).
#
# Note this function is only for a rough and quick estimation of the best k.
#
find_best_km = function(mat, max_km = 15) {
wss = (nrow(mat)-1)*sum(apply(mat,2,var))
max_km = min(c(nrow(mat) - 1, max_km))
for (i in 2:max_km) wss[i] = sum(kmeans(mat, centers = i, iter.max = 50)$withinss)
row_km = min(elbow_finder(1:max_km, wss)[1], knee_finder(1:max_km, wss)[1])
return(row_km)
}
jokergoo/cola documentation built on Feb. 29, 2024, 1:41 a.m.
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Defines functions find_best_km Ftest pamr samr one_vs_others ttest compare_to_subgroup knee_finder elbow_finder
Documented in find_best_km
# == title
# Get signature rows
#
# == param
# -object A `ConsensusPartition-class` object.
# -k Number of subgroups.
# -col Colors for the main heatmap.
# -silhouette_cutoff Cutoff for silhouette scores. Samples with values
# less than it are not used for finding signature rows. For selecting a
# proper silhouette cutoff, please refer to https://www.stat.berkeley.edu/~s133/Cluster2a.html#tth_tAb1.
# -fdr_cutoff Cutoff for FDR of the difference test between subgroups.
# -top_signatures Top signatures with most significant fdr. Note since fdr might be same for multiple rows,
# the final number of signatures might not be exactly the same as the one that has been set.
# -group_diff Cutoff for the maximal difference between group means.
# -scale_rows Whether apply row scaling when making the heatmap.
# -.scale_mean Internally used.
# -.scale_sd Internally used.
# -row_km Number of groups for performing k-means clustering on rows. By default it is automatically selected.
# -diff_method Methods to get rows which are significantly different between subgroups, see 'Details' section.
# -anno A data frame of annotations for the original matrix columns.
# By default it uses the annotations specified in `consensus_partition` or `run_all_consensus_partition_methods`.
# -anno_col A list of colors (color is defined as a named vector) for the annotations. If ``anno`` is a data frame,
# ``anno_col`` should be a named list where names correspond to the column names in ``anno``.
# -internal Used internally.
# -show_row_dend Whether show row dendrogram.
# -show_column_names Whether show column names in the heatmap.
# -column_names_gp Graphics parameters for column names.
# -use_raster Internally used.
# -plot Whether to make the plot.
# -verbose Whether to print messages.
# -seed Random seed.
# -left_annotation Annotation put on the left of the heatmap. It should be a `ComplexHeatmap::HeatmapAnnotation-class` object.
# The number of items should be the same as the number of the original matrix rows. The subsetting to the significant
# rows are automatically performed on the annotation object.
# -right_annotation Annotation put on the right of the heatmap. Same format as ``left_annotation``.
# -simplify Only used internally.
# -prefix Only used internally.
# -enforce The analysis is cached by default, so that the analysis with the same input will be automatically extracted
# without rerunning them. Set ``enforce`` to ``TRUE`` to enforce the funtion to re-perform the analysis.
# -hash Userd internally.
# -from_hc Is the `ConsensusPartition-class` object a node of a `HierarchicalPartition` object?
# -... Other arguments.
#
# == details
# Basically the function applies statistical test for the difference in subgroups for every
# row. There are following methods which test significance of the difference:
#
# -ttest First it looks for the subgroup with highest mean value, compare to each of the
# other subgroups with t-test and take the maximum p-value. Second it looks
# for the subgroup with lowest mean value, compare to each of the other subgroups
# again with t-test and take the maximum p-values. Later for these two list of p-values
# take the minimal p-value as the final p-value.
# -samr/pamr use SAM (from samr package)/PAM (from pamr package) method to find significantly different rows between subgroups.
# -Ftest use F-test to find significantly different rows between subgroups.
# -one_vs_others For each subgroup i in each row, it uses t-test to compare samples in current
# subgroup to all other samples, denoted as p_i. The p-value for current row is selected as min(p_i).
# -uniquely_high_in_one_group The signatures are defined as, if they are uniquely up-regulated in subgroup A, then it must fit following criterions:
# 1. in a two-group t-test of A ~ other_merged_groups, the statistic must be > 0 (high in group A) and p-value must be significant,
# and 2. for other groups (excluding A), t-test in every pair of groups should not be significant.
#
# ``diff_method`` can also be a self-defined function. The function needs two arguments which are the matrix for the analysis
# and the predicted classes. The function should returns a vector of FDR from the difference test.
#
# == return
# A data frame with more than two columns:
#
# -``which_row``: row index corresponding to the original matrix.
# -``fdr``: the FDR.
# -``km``: the k-means groups if ``row_km`` is set.
# -other_columns: the mean value (depending rows are scaled or not) in each subgroup.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola)
# res = golub_cola["ATC", "skmeans"]
# tb = get_signatures(res, k = 3)
# head(tb)
# get_signatures(res, k = 3, top_signatures = 100)
setMethod(f = "get_signatures",
signature = "ConsensusPartition",
definition = function(object, k,
col = if(scale_rows) c("green", "white", "red") else c("blue", "white", "red"),
silhouette_cutoff = 0.5,
fdr_cutoff = cola_opt$fdr_cutoff,
top_signatures = NULL,
group_diff = cola_opt$group_diff,
scale_rows = object@scale_rows, .scale_mean = NULL, .scale_sd = NULL,
row_km = NULL,
diff_method = c("Ftest", "ttest", "samr", "pamr", "one_vs_others", "uniquely_high_in_one_group"),
anno = get_anno(object),
anno_col = get_anno_col(object),
internal = FALSE,
show_row_dend = FALSE,
show_column_names = FALSE,
column_names_gp = gpar(fontsize = 8),
use_raster = TRUE,
plot = TRUE, verbose = TRUE, seed = 888,
left_annotation = NULL, right_annotation = NULL,
simplify = FALSE, prefix = "", enforce = FALSE, hash = NULL, from_hc = FALSE,
...) {
if(from_hc) {
group_diff = object@.env$group_diff
fdr_cutoff = object@.env$fdr_cutoff
.scale_mean = object@.env$global_row_mean
.scale_sd = object@.env$global_row_sd
k = attr(object, "best_k")
} else {
if(missing(k)) stop_wrap("k needs to be provided.")
}
dotdot = list(...)
p_cutoff = NULL
if("p_cutoff" %in% names(dotdot)) {
p_cutoff = dotdot$p_cutoff
}
from_down_sampling = FALSE
if(inherits(object, "DownSamplingConsensusPartition")) {
from_down_sampling = TRUE
}
class_df = get_classes(object, k)
class_ids = class_df$class
if(is.null(p_cutoff)) {
data = get_matrix(object, include_all_rows = TRUE)
} else {
data = object@.env$data[, object@full_column_index, drop = FALSE]
}
if(!from_down_sampling) {
l = class_df$silhouette >= silhouette_cutoff
} else {
l = class_df$p <= p_cutoff
}
data2 = data[, l, drop = FALSE]
class = class_df$class[l]
column_used_index = which(l)
tb = table(class)
l = as.character(class) %in% names(which(tb <= 1))
data2 = data2[, !l, drop = FALSE]
class = class[!l]
column_used_index = column_used_index[!l]
column_used_logical = rep(FALSE, ncol(data))
column_used_logical[column_used_index] = TRUE
has_ambiguous = sum(!column_used_logical)
n_sample_used = length(class)
if(!from_down_sampling) {
if(verbose) qqcat("@{prefix}* @{n_sample_used}/@{nrow(class_df)} samples (in @{length(unique(class))} classes) remain after filtering by silhouette (>= @{silhouette_cutoff}).\n")
} else {
if(verbose) qqcat("@{prefix}* @{n_sample_used}/@{nrow(class_df)} samples (in @{length(unique(class))} classes) remain after filtering by p-value (<= @{p_cutoff}).\n")
}
tb = table(class)
if(sum(tb > 1) <= 1) {
if(plot) {
grid.newpage()
fontsize = convertUnit(unit(0.1, "npc"), "char", valueOnly = TRUE)*get.gpar("fontsize")$fontsize
grid.text("not enough samples", gp = gpar(fontsize = fontsize))
}
if(verbose) qqcat("@{prefix}* Not enough samples.\n")
return(invisible(data.frame(which_row = integer(0))))
}
if(length(unique(class)) <= 1) {
if(plot) {
grid.newpage()
fontsize = convertUnit(unit(0.1, "npc"), "char", valueOnly = TRUE)*get.gpar("fontsize")$fontsize
grid.text("not enough classes", gp = gpar(fontsize = fontsize))
}
if(verbose) qqcat("@{prefix}* Not enough classes.\n")
return(invisible(data.frame(which_row = integer(0))))
}
do_row_clustering = TRUE
if(inherits(diff_method, "function")) {
if(verbose) qqcat("@{prefix}* calculate row difference between subgroups by user-defined function.\n")
diff_method_fun = diff_method
diff_method = digest(diff_method)
} else {
diff_method = match.arg(diff_method)
}
if(!is.null(top_signatures)) {
if(diff_method %in% c("samr", "pamr")) {
if(verbose) qqcat("`top_signatures` is ignored when `diff_method` is set to samr/pamr.\n")
}
}
if(is.null(hash)) {
hash = digest(list(used_samples = which(l),
class = class,
n_group = k,
diff_method = diff_method,
.scale_mean = .scale_mean,
.scale_sd = .scale_sd,
column_index = object@column_index,
group_diff = group_diff,
fdr_cutoff = fdr_cutoff,
top_signatures = top_signatures,
seed = seed),
algo = "md5")
} else {
if(is.null(object@.env[[paste0("signature_fdr_", hash)]])) {
if(verbose) qqcat("@{prefix} Warning: cannot find cache with hash: @{hash}, generate a new hash.")
hash = digest(list(used_samples = which(l),
class = class,
n_group = k,
diff_method = diff_method,
.scale_mean = .scale_mean,
.scale_sd = .scale_sd,
column_index = object@column_index,
group_diff = group_diff,
fdr_cutoff = fdr_cutoff,
top_signatures = top_signatures,
seed = seed),
algo = "md5")
}
}
nm = paste0("signature_fdr_", hash)
if(verbose) qqcat("@{prefix}* cache hash: @{hash} (seed @{seed}).\n")
if(enforce) object@.env[[nm]] = NULL
find_signature = TRUE
if(!is.null(object@.env[[nm]])) {
if(diff_method == "samr") {
if(object@.env[[nm]]$diff_method == "samr" &&
object@.env[[nm]]$n_sample_used == n_sample_used &&
abs(object@.env[[nm]]$fdr_cutoff - fdr_cutoff) < 1e-10) {
fdr = object@.env[[nm]]$fdr
find_signature = FALSE
}
} else if(diff_method == "pamr") {
if(object@.env[[nm]]$diff_method == "pamr" &&
object@.env[[nm]]$n_sample_used == n_sample_used &&
abs(object@.env[[nm]]$fdr_cutoff - fdr_cutoff) < 1e-10) {
fdr = object@.env[[nm]]$fdr
find_signature = FALSE
}
} else {
if(object@.env[[nm]]$diff_method == diff_method &&
object@.env[[nm]]$n_sample_used == n_sample_used) {
fdr = object@.env[[nm]]$fdr
find_signature = FALSE
}
}
}
if(verbose) qqcat("@{prefix}* calculating row difference between subgroups by @{diff_method}.\n")
if(find_signature) {
if(diff_method == "ttest") {
fdr = ttest(data2, class)
} else if(diff_method == "samr") {
fdr = samr(data2, class, fdr.output = fdr_cutoff)
} else if(diff_method == "Ftest") {
fdr = Ftest(data2, class)
} else if(diff_method == "pamr") {
fdr = pamr(data2, class, fdr.ouput = fdr_cutoff)
} else if(diff_method == "one_vs_others") {
fdr = one_vs_others(data2, class)
} else {
fdr = diff_method_fun(data2, class)
}
} else {
if(verbose) qqcat("@{prefix} - row difference is extracted from cache.\n")
}
if(!is.null(top_signatures)) {
fdr_cutoff = fdr[order(fdr)[min(length(fdr), top_signatures)]]
}
if(scale_rows && !is.null(object@.env[[nm]]$row_order_scaled)) {
row_order = object@.env[[nm]]$row_order_scaled
if(verbose) qqcat("@{prefix} - row order for the scaled matrix is extracted from cache.\n")
do_row_clustering = FALSE
} else if(!scale_rows && !is.null(object@.env[[nm]]$row_order_unscaled)) {
row_order = object@.env[[nm]]$row_order_unscaled
if(verbose) qqcat("@{prefix} - row order for the unscaled matrix is extracted from cache.\n")
do_row_clustering = FALSE
}
object@.env[[nm]]$diff_method = diff_method
object@.env[[nm]]$fdr_cutoff = fdr_cutoff
object@.env[[nm]]$fdr = fdr
object@.env[[nm]]$n_sample_used = n_sample_used
object@.env[[nm]]$group_diff = group_diff
object@.env[[nm]]$.scale_mean = .scale_mean
object@.env[[nm]]$.scale_sd = .scale_sd
# filter by fdr
fdr[is.na(fdr)] = 1
p_value = attr(fdr, "p_value")
l_fdr = fdr <= fdr_cutoff
mat = data[l_fdr, , drop = FALSE]
fdr2 = fdr[l_fdr]
if(!is.null(p_value)) {
p_value = p_value[l_fdr]
}
if(!is.null(.scale_mean)) {
.scale_mean = .scale_mean[l_fdr]
}
if(!is.null(.scale_sd)) {
.scale_sd = .scale_sd[l_fdr]
}
if(!is.null(left_annotation)) left_annotation = left_annotation[l_fdr, ]
if(!is.null(right_annotation)) right_annotation = right_annotation[l_fdr, ]
returned_df = data.frame(which_row = which(l_fdr), fdr = fdr2)
if(!is.null(p_value)) {
returned_df$p_value = p_value
}
rownames(returned_df) = rownames(object)[l_fdr]
attr(returned_df, "sample_used") = column_used_logical
attr(returned_df, "hash") = hash
# filter by group_diff
mat1 = mat[, column_used_logical, drop = FALSE]
if(nrow(mat) == 1) {
group_mean = rbind(tapply(mat1, class, mean))
} else {
group_mean = do.call("cbind", tapply(seq_len(ncol(mat1)), class, function(ind) {
rowMeans(mat1[, ind, drop = FALSE])
}))
}
colnames(group_mean) = paste0("mean_", colnames(group_mean))
returned_df = cbind(returned_df, group_mean)
returned_df$group_diff = apply(group_mean, 1, function(x) max(x) - min(x))
if(scale_rows) {
mfoo = mat[, column_used_logical, drop = FALSE]
if(!is.null(.scale_mean) && !is.null(.scale_sd)) {
mat1_scaled = (mfoo - .scale_mean)/.scale_sd
} else {
mat1_scaled = (mfoo - rowMeans(mfoo))/rowSds(mfoo)
}
if(nrow(mat) == 1) {
group_mean_scaled = rbind(tapply(mat1_scaled, class, mean))
} else {
group_mean_scaled = do.call("cbind", tapply(seq_len(ncol(mat1_scaled)), class, function(ind) {
rowMeans(mat1_scaled[, ind, drop = FALSE])
}))
}
colnames(group_mean_scaled) = paste0("scaled_mean_", colnames(group_mean_scaled))
returned_df = cbind(returned_df, group_mean_scaled)
returned_df$group_diff_scaled = apply(group_mean_scaled, 1, function(x) max(x) - min(x))
}
if(group_diff > 0) {
if(scale_rows) {
l_diff = returned_df$group_diff_scaled >= group_diff
} else {
l_diff = returned_df$group_diff >= group_diff
}
mat = mat[l_diff, , drop = FALSE]
mat1 = mat1[l_diff, , drop = FALSE]
returned_df = returned_df[l_diff, , drop = FALSE]
}
returned_obj = returned_df
rownames(returned_obj) = NULL
attr(returned_obj, "sample_used") = column_used_logical
attr(returned_obj, "hash") = hash
if(simplify && !plot) {
return(returned_df)
}
## add k-means
row_km_fit = NULL
if(!internal) {
if(nrow(mat1) > 10) {
do_kmeans = TRUE
if(scale_rows) {
mat_for_km = t(scale(t(mat1)))
row_km_fit = object@.env[[nm]]$row_km_fit_scaled
} else {
mat_for_km = mat1
row_km_fit = object@.env[[nm]]$row_km_fit_unscaled
}
if(nrow(mat_for_km) > 5000) {
set.seed(seed)
mat_for_km2 = mat_for_km[sample(nrow(mat_for_km), 5000), , drop = FALSE]
} else {
mat_for_km2 = mat_for_km
}
if(!is.null(row_km_fit)) {
if(is.null(row_km) || identical(as.integer(row_km), length(row_km_fit$size))) {
returned_obj$km = apply(pdist(row_km_fit$centers, mat_for_km, as.integer(1)), 2, which.min)
do_kmeans = FALSE
if(verbose) qqcat("@{prefix}* use k-means partition that are already calculated in previous runs.\n")
}
}
if(do_kmeans) {
set.seed(seed)
if(is.null(row_km)) {
row_km = guess_best_km(mat_for_km2)
if(length(unique(class)) == 1) row_km = 1
if(length(unique(class)) == 2) row_km = min(row_km, 2)
}
if(row_km > 1) {
row_km_fit = kmeans(mat_for_km2, centers = row_km)
returned_obj$km = apply(pdist(row_km_fit$centers, mat_for_km, as.integer(1)), 2, which.min)
if(scale_rows) {
object@.env[[nm]]$row_km_fit_scaled = row_km_fit
} else {
object@.env[[nm]]$row_km_fit_unscaled = row_km_fit
}
}
if(verbose) qqcat("@{prefix}* split rows into @{row_km} groups by k-means clustering.\n")
}
}
}
if(verbose) {
if(is.null(top_signatures)) {
qqcat("@{prefix}* @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) under fdr < @{fdr_cutoff}, group_diff > @{group_diff}.\n")
} else {
qqcat("@{prefix}* @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with most significant fdr, group_diff > @{group_diff}.\n")
}
}
if(nrow(mat) == 0) {
if(plot) {
grid.newpage()
fontsize = convertUnit(unit(0.1, "npc"), "char", valueOnly = TRUE)*get.gpar("fontsize")$fontsize
grid.text("no sigatures", gp = gpar(fontsize = fontsize))
}
return(invisible(data.frame(which_row = integer(0))))
}
if(!plot) {
return(invisible(returned_obj))
}
set.seed(seed)
more_than_2k = FALSE
if(!is.null(object@.env[[nm]]$row_index)) {
if(verbose) qqcat("@{prefix} - use the signatures that are already generated in previous runs.\n")
row_index = object@.env[[nm]]$row_index
mat1 = mat[row_index, column_used_logical, drop = FALSE]
mat2 = mat[row_index, !column_used_logical, drop = FALSE]
more_than_2k = TRUE
if(!is.null(left_annotation)) left_annotation = left_annotation[row_index, ]
if(!is.null(right_annotation)) right_annotation = right_annotation[row_index, ]
} else if(nrow(mat) > 2000) {
more_than_2k = TRUE
row_index = sample(1:nrow(mat), 2000)
object@.env[[nm]]$row_index = row_index
# mat1 = mat[order(fdr2)[1:top_k_genes], column_used_logical, drop = FALSE]
# mat2 = mat[order(fdr2)[1:top_k_genes], !column_used_logical, drop = FALSE]
mat1 = mat[row_index, column_used_logical, drop = FALSE]
mat2 = mat[row_index, !column_used_logical, drop = FALSE]
# group2 = group2[order(fdr2)[1:top_k_genes]]
if(verbose) qqcat("@{prefix} - randomly sample 2000 signatures.\n")
if(!is.null(left_annotation)) left_annotation = left_annotation[row_index, ]
if(!is.null(right_annotation)) right_annotation = right_annotation[row_index, ]
} else {
row_index = seq_len(nrow(mat))
mat1 = mat[, column_used_logical, drop = FALSE]
mat2 = mat[, !column_used_logical, drop = FALSE]
}
base_mean = rowMeans(mat1)
if(nrow(mat) == 1) {
group_mean = matrix(tapply(mat1, class, mean), nrow = 1)
} else {
group_mean = do.call("cbind", tapply(seq_len(ncol(mat1)), class, function(ind) {
rowMeans(mat1[, ind, drop = FALSE])
}))
}
rel_diff = (rowMaxs(group_mean) - rowMins(group_mean))/base_mean/2
if(is.null(anno)) {
bottom_anno1 = NULL
} else {
if(is.atomic(anno)) {
anno_nm = deparse(substitute(anno))
anno = data.frame(anno)
colnames(anno) = anno_nm
if(!is.null(anno_col)) {
if(is.atomic(anno_col)) {
anno_col = list(anno_col)
names(anno_col) = anno_nm
}
}
} else if(ncol(anno) == 1) {
if(!is.null(anno_col)) {
if(is.atomic(anno_col)) {
anno_col = list(anno_col)
names(anno_col) = colnames(anno)
}
}
}
if(is.null(anno_col)) {
bottom_anno1 = HeatmapAnnotation(df = anno[column_used_logical, , drop = FALSE],
show_annotation_name = !has_ambiguous & !internal, annotation_name_side = "right")
} else {
bottom_anno1 = HeatmapAnnotation(df = anno[column_used_logical, , drop = FALSE], col = anno_col,
show_annotation_name = !has_ambiguous & !internal, annotation_name_side = "right")
}
}
if(scale_rows) {
scaled_mean = base_mean
scaled_sd = rowSds(mat1)
scaled_mat1 = t(scale(t(mat1)))
scaled_mat2 = mat2
if(has_ambiguous) {
for(i in seq_len(nrow(mat2))) {
scaled_mat2[i, ] = (scaled_mat2[i, ] - scaled_mean[i])/scaled_sd[i]
}
}
use_mat1 = scaled_mat1
use_mat2 = scaled_mat2
use_mat1[is.infinite(use_mat1)] = 0
use_mat1[is.na(use_mat1)] = 0
use_mat2[is.infinite(use_mat2)] = 0
use_mat2[is.na(use_mat2)] = 0
mat_range = quantile(abs(scaled_mat1), 0.95, na.rm = TRUE)
col_fun = colorRamp2(c(-mat_range, 0, mat_range), col)
heatmap_name = "z-score"
} else {
use_mat1 = mat1
use_mat2 = mat2
mat_range = quantile(mat1, c(0.05, 0.95))
col_fun = colorRamp2(c(mat_range[1], mean(mat_range), mat_range[2]), col)
heatmap_name = "Value"
}
if(has_ambiguous) {
class2 = class_df$class[!column_used_logical]
if(is.null(anno)) {
bottom_anno2 = NULL
} else {
anno_col = lapply(bottom_anno1@anno_list, function(anno) {
if(is.null(anno@color_mapping)) {
return(NULL)
} else {
if(anno@color_mapping@type == "discrete") {
if(anno@name %in% names(object@anno_col)) {
object@anno_col[[anno@name]]
} else {
anno@color_mapping@colors
}
} else {
anno@color_mapping@col_fun
}
}
})
names(anno_col) = names(bottom_anno1@anno_list)
anno_col = anno_col[!sapply(anno_col, is.null)]
if(!is.null(object@anno_col)) {
nmd = setdiff(names(object@anno_col), names(anno_col))
if(length(nmd)) {
anno_col[nmd] = object@anno_col[nmd]
}
}
bottom_anno2 = HeatmapAnnotation(df = anno[!column_used_logical, , drop = FALSE], col = anno_col,
show_annotation_name = !internal, annotation_name_side = "right")
}
}
if(is.null(p_cutoff)) {
silhouette_range = range(class_df$silhouette)
silhouette_range[2] = 1
} else {
p_range = c(0, 1)
}
if(verbose) qqcat("@{prefix}* making heatmaps for signatures.\n")
row_split = NULL
if(!internal) {
if(scale_rows) {
row_km_fit = object@.env[[nm]]$row_km_fit_scaled
} else {
row_km_fit = object@.env[[nm]]$row_km_fit_unscaled
}
if(!is.null(row_km_fit)) {
row_split = factor(returned_obj$km[row_index], levels = sort(unique(returned_obj$km[row_index])))
}
}
# group2 = factor(group2, levels = sort(unique(group2)))
# ht_list = Heatmap(group2, name = "Group", show_row_names = FALSE, width = unit(5, "mm"), col = cola_opt$color_set_2)
ht_list = NULL
membership_mat = get_membership(object, k)
prop_col_fun = colorRamp2(c(0, 1), c("white", "red"))
if(from_down_sampling) {
class_df_logp = class_df$p
class_df_logp[class_df_logp == 0] = 0.001
class_df_logp = -log10(class_df_logp)
p_range = range(class_df_logp)
p_range[1] = 0
}
if(internal) {
ha1 = HeatmapAnnotation(Prob = membership_mat[column_used_logical, ],
Class = class_df$class[column_used_logical],
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !has_ambiguous & !internal,
annotation_name_side = "right",
show_legend = TRUE)
} else {
if(simplify) {
if(!from_down_sampling) {
ha1 = HeatmapAnnotation(
Class = class_df$class[column_used_logical],
silhouette = anno_barplot(class_df$silhouette[column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[column_used_logical] >= silhouette_cutoff, "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = !has_ambiguous & !internal,
annotation_name_side = "right",
show_legend = TRUE)
} else {
ha1 = HeatmapAnnotation(
Class = class_df$class[column_used_logical],
p_prediction = anno_barplot(class_df_logp[column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[column_used_logical] >= -log10(p_cutoff), "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = !has_ambiguous & !internal,
annotation_name_side = "right",
show_legend = TRUE)
}
} else {
if(!from_down_sampling) {
ha1 = HeatmapAnnotation(Prob = membership_mat[column_used_logical, ],
Class = class_df$class[column_used_logical],
silhouette = anno_barplot(class_df$silhouette[column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[column_used_logical] >= silhouette_cutoff, "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !has_ambiguous & !internal & c(TRUE, TRUE, FALSE),
annotation_name_side = "right",
show_legend = TRUE)
} else {
ha1 = HeatmapAnnotation(
Class = class_df$class[column_used_logical],
p_prediction = anno_barplot(class_df_logp[column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[column_used_logical] >= -log10(p_cutoff), "black", "#EEEEEE"),
col = NA),
bar_width = 1, baseline = 0, axis = !has_ambiguous, axis_param = list(side= "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !has_ambiguous & !internal & c(TRUE, FALSE),
annotation_name_side = "right",
show_legend = TRUE)
}
}
}
ht_list = ht_list + Heatmap(use_mat1, name = heatmap_name, col = col_fun,
top_annotation = ha1, row_split = row_split,
cluster_columns = TRUE, cluster_column_slices = FALSE, cluster_row_slices = FALSE,
column_split = factor(class_df$class[column_used_logical], levels = sort(unique(class_df$class[column_used_logical]))),
show_column_dend = FALSE,
show_row_names = FALSE, show_row_dend = show_row_dend, column_title = {if(internal) NULL else qq("@{ncol(use_mat1)} confident samples")},
use_raster = use_raster, raster_by_magick = requireNamespace("magick", quietly = TRUE),
bottom_annotation = bottom_anno1, show_column_names = show_column_names, column_names_gp = column_names_gp,
left_annotation = left_annotation, right_annotation = {if(has_ambiguous) NULL else right_annotation})
all_value_positive = !any(data < 0)
if(scale_rows && all_value_positive && !simplify) {
ht_list = ht_list + Heatmap(base_mean, show_row_names = FALSE, name = "base_mean", width = unit(5, "mm"), show_column_names = !internal, column_names_gp = column_names_gp) +
Heatmap(rel_diff, col = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red")),
show_row_names = FALSE, show_column_names = !internal, column_names_gp = column_names_gp, name = "rel_diff", width = unit(5, "mm"))
}
if(has_ambiguous) {
if(internal) {
ha2 = HeatmapAnnotation(Prob = membership_mat[!column_used_logical, ,drop = FALSE],
Class = class_df$class[!column_used_logical],
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = !internal,
annotation_name_side = "right",
show_legend = FALSE)
} else {
if(simplify) {
if(!from_down_sampling) {
ha2 = HeatmapAnnotation(
Class = class_df$class[!column_used_logical],
silhouette2 = anno_barplot(class_df$silhouette[!column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "grey", "grey"),
col = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = c(TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
} else {
ha2 = HeatmapAnnotation(
Class = class_df$class[!column_used_logical],
p_prediction2 = anno_barplot(class_df_logp[!column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[!column_used_logical] >= -log10(p_cutoff), "grey", "grey"),
col = ifelse(class_df_logp[!column_used_logical] <= -log10(p_cutoff), "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2),
show_annotation_name = c(TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
}
} else {
if(!from_down_sampling) {
ha2 = HeatmapAnnotation(Prob = membership_mat[!column_used_logical, ,drop = FALSE],
Class = class_df$class[!column_used_logical],
silhouette2 = anno_barplot(class_df$silhouette[!column_used_logical], ylim = silhouette_range,
gp = gpar(fill = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "grey", "grey"),
col = ifelse(class_df$silhouette[!column_used_logical] >= silhouette_cutoff, "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = c(TRUE, TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
} else {
ha2 = HeatmapAnnotation(
Class = class_df$class[!column_used_logical],
p_prediction2 = anno_barplot(class_df_logp[!column_used_logical], ylim = p_range,
gp = gpar(fill = ifelse(class_df_logp[!column_used_logical] >= -log10(p_cutoff), "grey", "grey"),
col = ifelse(class_df_logp[!column_used_logical] >= -log10(p_cutoff), "black", NA)),
bar_width = 1, baseline = 0, axis = TRUE, axis_param = list(side = "right"),
height = unit(15, "mm")),
col = list(Class = cola_opt$color_set_2, Prob = prop_col_fun),
show_annotation_name = c(TRUE, FALSE) & !internal,
annotation_name_side = "right",
show_legend = FALSE)
}
}
}
ht_list = ht_list + Heatmap(use_mat2, name = paste0(heatmap_name, 2), col = col_fun,
top_annotation = ha2,
cluster_columns = TRUE, show_column_dend = FALSE,
show_row_names = FALSE, show_row_dend = FALSE, show_heatmap_legend = FALSE,
use_raster = use_raster, raster_by_magick = requireNamespace("magick", quietly = TRUE),
bottom_annotation = bottom_anno2, show_column_names = show_column_names, column_names_gp = column_names_gp,
right_annotation = right_annotation)
}
if(has_ambiguous) {
lgd = Legend(title = "Status (barplots)", labels = c("confident", "ambiguous"), legend_gp = gpar(fill = c("black", "grey")))
heatmap_legend_list = list(lgd)
} else {
heatmap_legend_list = NULL
}
if(do_row_clustering) {
if(is.null(top_signatures)) {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with fdr < @{fdr_cutoff}@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
} else {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with most significant fdr@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
}
ht_list = draw(ht_list, main_heatmap = heatmap_name, column_title = column_title,
show_heatmap_legend = !internal, show_annotation_legend = !internal,
heatmap_legend_list = heatmap_legend_list
)
row_order = row_order(ht_list)
if(!is.list(row_order)) row_order = list(row_order)
if(scale_rows) {
object@.env[[nm]]$row_order_scaled = do.call("c", row_order)
} else {
object@.env[[nm]]$row_order_unscaled = do.call("c", row_order)
}
} else {
if(verbose) qqcat("@{prefix} - use row order from cache.\n")
if(is.null(top_signatures)) {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with fdr < @{fdr_cutoff}@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
} else {
column_title = ifelse(internal, "", qq("@{k} subgroups, @{nrow(mat)} signatures (@{sprintf('%.1f',nrow(mat)/nrow(object)*100)}%) with most significant fdr@{ifelse(group_diff > 0, paste0(', group_diff > ', group_diff), '')}"))
}
draw(ht_list, main_heatmap = heatmap_name, column_title = column_title,
show_heatmap_legend = !internal, show_annotation_legend = !internal,
cluster_rows = FALSE, row_order = row_order, heatmap_legend_list = heatmap_legend_list,
row_title = {if(length(unique(row_split)) <= 1) NULL else qq("k-means with @{length(unique(row_split))} groups")}
)
}
# the cutoff
# https://www.stat.berkeley.edu/~s133/Cluster2a.html
if(!internal) {
if(is.null(p_cutoff)) {
if(!has_ambiguous) {
decorate_annotation("silhouette", slice = length(unique(class_df$class[column_used_logical])), {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(c(silhouette_cutoff, silhouette_cutoff), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(!has_ambiguous) grid.text("Silhouette\nscore", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
if(has_ambiguous) {
decorate_annotation("silhouette2", {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(c(silhouette_cutoff, silhouette_cutoff), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(has_ambiguous) grid.text("Silhouette\nscore", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
} else {
if(!has_ambiguous) {
decorate_annotation("p_prediction", slice = length(unique(class_df$class[column_used_logical])), {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(-log10(c(p_cutoff, p_cutoff)), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(!has_ambiguous) grid.text("-log10(prediction\n p-value)", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
if(has_ambiguous) {
decorate_annotation("p_prediction2", {
grid.rect(gp = gpar(fill = "transparent"))
grid.lines(c(0, 1), unit(-log10(c(p_cutoff, p_cutoff)), "native"), gp = gpar(lty = 2, col = "#CCCCCC"))
if(has_ambiguous) grid.text("-log10(prediction\n p-value)", x = unit(1, "npc") + unit(5, "mm"), just = "left", gp = gpar(fontsize = 10))
})
}
}
}
return(invisible(returned_obj))
})
# https://stackoverflow.com/questions/2018178/finding-the-best-trade-off-point-on-a-curve
elbow_finder <- function(x_values, y_values) {
# Max values to create line
max_x_x <- max(x_values)
max_x_y <- y_values[which.max(x_values)]
max_y_y <- max(y_values)
max_y_x <- x_values[which.max(y_values)]
max_df <- data.frame(x = c(max_y_x, max_x_x), y = c(max_y_y, max_x_y))
# Creating straight line between the max values
fit <- lm(max_df$y ~ max_df$x)
# Distance from point to line
distances <- c()
for(i in 1:length(x_values)) {
distances <- c(distances, abs(coef(fit)[2]*x_values[i] - y_values[i] + coef(fit)[1]) / sqrt(coef(fit)[2]^2 + 1^2))
}
# Max distance point
x_max_dist <- x_values[which.max(distances)]
y_max_dist <- y_values[which.max(distances)]
return(c(x_max_dist, y_max_dist))
}
# https://raghavan.usc.edu//papers/kneedle-simplex11.pdf
knee_finder = function(x, y) {
n = length(x)
a = (y[n] - y[1])/(x[n] - x[1])
b = y[1] - a*x[1]
d = a*x + b - y
x[which.max(d)]
}
compare_to_subgroup = function(mat, class, which = "highest") {
check_pkg("genefilter", bioc = TRUE)
od = order(class)
class = class[od]
mat = mat[, od, drop = FALSE]
class = as.numeric(factor(class))
group = apply(mat, 1, function(x) {
group_mean = tapply(x, class, mean)
if(which == "highest") {
which.max(group_mean)
} else {
which.min(group_mean)
}
})
# class and subgroup_index are all numeric
compare_to_one_subgroup = function(mat, class, subgroup_index) {
oc = setdiff(class, subgroup_index)
pmat = matrix(NA, nrow = nrow(mat), ncol = length(oc))
for(i in seq_along(oc)) {
l = class == subgroup_index | class == oc[i]
m2 = mat[, l, drop = FALSE]
fa = factor(class[l])
pmat[, i] = genefilter::rowttests(m2, fa)[, "p.value"]
}
rowMaxs(pmat, na.rm = TRUE)
}
p = tapply(seq_len(nrow(mat)), group, function(ind) {
compare_to_one_subgroup(mat[ind, , drop = FALSE], class, group[ind][1])
})
p2 = numeric(length(group))
for(i in seq_along(p)) {
p2[group == i] = p[[i]]
}
return(p2)
}
ttest = function(mat, class) {
p1 = compare_to_subgroup(mat, class, "highest")
p2 = compare_to_subgroup(mat, class, "lowest")
p = pmin(p1, p2, na.rm = TRUE)
fdr = p.adjust(p, method = "BH")
fdr[is.na(fdr)] = Inf
fdr[is.infinite(fdr)] = Inf
attr(fdr, "p_value") = p
fdr
}
one_vs_others = function(mat, class) {
check_pkg("genefilter", bioc = TRUE)
le = unique(class)
dfl = list()
for(x in le) {
fa = as.vector(class)
fa[class == x] = "a"
fa[class != x] = "b"
fa = factor(fa, levels = c("a", "b"))
dfl[[x]] = genefilter::rowttests(mat, fa)[, "p.value"]
}
df = do.call("cbind", dfl)
p = rowMins(df)
fdr = p.adjust(p, "BH")
attr(fdr, "p_value") = p
fdr
}
samr = function(mat, class, ...) {
check_pkg("samr", bioc = FALSE)
on.exit(if(sink.number()) sink(NULL))
class = as.numeric(factor(class))
n_class = length(unique(class))
tempf = tempfile()
sink(tempf)
if(n_class == 2) {
samfit = samr::SAM(mat, class, resp.type = "Two class unpaired", nperms = 1000, ...)
} else {
samfit = samr::SAM(mat, class, resp.type = "Multiclass", nperms = 1000, ...)
}
sink(NULL)
file.remove(tempf)
sig_index = NULL
if(!is.null(samfit$siggenes.table$genes.up)) {
id = samfit$siggenes.table$genes.up
if(is.null(dim(id))) id = matrix(id, nrow = 1)
sig_index = c(sig_index, id[, 2])
}
if(!is.null(samfit$siggenes.table$genes.lo)) {
id = samfit$siggenes.table$genes.lo
if(is.null(dim(id))) id = matrix(id, nrow = 1)
sig_index = c(sig_index, id[, 2])
}
sig_index = as.numeric(sig_index)
fdr = rep(1, nrow(mat))
fdr[sig_index] = 0
return(fdr)
}
pamr = function(mat, class, fdr.cutoff = 0.1, ...) {
check_pkg("pamr", bioc = FALSE)
on.exit(if(sink.number()) sink(NULL))
class = as.numeric(factor(class))
tempf = tempfile()
sink(tempf)
mydata <- list(x=mat, y=class, geneid = rownames(mat))
mydata.fit <- pamr::pamr.train(mydata)
mydata.cv <- pamr::pamr.cv(mydata.fit, mydata)
mydata.fdr <- pamr::pamr.fdr(mydata.fit, mydata)
threshold = min(mydata.fdr$results[mydata.fdr$results[,"Median FDR"] < fdr.cutoff, "Threshold"])
mydata.genelist <- pamr::pamr.listgenes(mydata.fit, mydata, threshold = threshold, fitcv=mydata.cv)
sink(NULL)
file.remove(tempf)
fdr = rep(1, nrow(mat))
fdr[rownames(mat) %in% mydata.genelist[,"id"]] = 0
return(fdr)
}
Ftest = function(mat, class) {
check_pkg("genefilter", bioc = TRUE)
rownames(mat) = NULL
p = genefilter::rowFtests(mat, factor(class))[, "p.value"]
fdr = p.adjust(p, "BH")
fdr[is.na(fdr)] = Inf
attr(fdr, "p_value") = p
return(fdr)
}
uniquely_high_in_one_group = function (mat, class) {
le = unique(class)
dfl = list()
for (x in le) {
fa = as.vector(class)
fa[class == x] = "a"
fa[class != x] = "b"
fa = factor(fa, levels = c("a", "b"))
df = genefilter::rowttests(mat, fa)
l = df[, "statistic"] < 0; l[is.na(l)] = FALSE
df[l, "p.value"] = NA
# then for the remaining subgroups, they show no difference
if(length(le) > 2) {
l = class != x
p1 = compare_to_subgroup(mat[, l, drop = FALSE], class[l], "highest")
p2 = compare_to_subgroup(mat[, l, drop = FALSE], class[l], "lowest")
p = pmin(p1, p2, na.rm = TRUE)
df[p < 0.05, "p.value"] = NA
}
dfl[[x]] = df[, "p.value"]
}
df = do.call("cbind", dfl)
p = rowMins(df, na.rm = TRUE)
fdr = p.adjust(p, "BH")
attr(fdr, "p_value") = p
fdr
}
# test_row_diff_fun = function(fun, fdr_cutoff = 0.1) {
# set.seed(100)
# x = matrix(rnorm(1000 * 20), ncol = 20)
# rownames(x) = rep(paste0("gene1", 1:1000))
# dd = sample(1:1000, size = 100)
# u = matrix(2 * rnorm(100), ncol = 10, nrow = 100)
# x[dd, 11:20] = x[dd, 11:20] + u
# row_diff = rep("no", 1000)
# row_diff[dd] = "yes"
# y = c(rep(1, 10), rep(2, 10))
# fdr = fun(x, y)
# ht = Heatmap(x, top_annotation = HeatmapAnnotation(foo = as.character(y), col = list(foo = c("1" = "blue", "2" = "red"))), show_row_names = FALSE) +
# Heatmap(row_diff, name = "diff", col = c("yes" = "red", "no" = "white"), width = unit(5, "mm")) +
# Heatmap(fdr, name = "fdr", width = unit(5, "mm"), show_row_names = FALSE)
# draw(ht, split = fdr < fdr_cutoff)
# }
# title
# Density for the signatures
#
# == param
# -object A `ConsensusPartition-class` object.
# -k number of partitions
# -... pass to `get_signatures,ConsensusPartition-method`
#
# == details
# The function makes density distributio nf of signatures in all columns.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# setMethod(f = "signature_density",
# signature = "ConsensusPartition",
# definition = function(object, k, ...) {
# cl = get_class(object, k = k)$class
# data = object@.env$data[, object@column_index, drop = FALSE]
# all_den_list = lapply(seq_len(ncol(data)), function(i) {
# x = data[, i]
# density(x)
# })
# x_range = range(unlist(lapply(all_den_list, function(x) x$x)))
# y_range = range(unlist(lapply(all_den_list, function(x) x$y)))
# x = get_signatures(object, k = k, plot = FALSE, verbose = FALSE, ...)
# gp_tb = table(x$df$group)
# n_gp = sum(gp_tb > 5)
# gp_tb = gp_tb[gp_tb > 5]
# op = par(no.readonly = TRUE)
# par(mfrow = c(n_gp + 1, 1), mar = c(2, 4, 1, 3))
# plot(NULL, type = "n", xlim = x_range, ylim = y_range, ylab = "density", xlab = NULL)
# for(i in 1:ncol(data)) {
# lines(all_den_list[[i]], col = cola_opt$color_set_2[cl[i]], lwd = 1)
# }
# mtext("all rows", side = 4, line = 1)
# gp = x$df$group
# for(j in as.numeric(names(gp_tb))) {
# gp2 = gp[gp == as.character(j)]
# all_den_list = lapply(seq_len(ncol(data)), function(i) density(data[names(gp2), i]))
# # x_range = range(unlist(lapply(all_den_list, function(x) x$x)))
# y_range = range(unlist(lapply(all_den_list, function(x) x$y)))
# plot(NULL, type = "n", xlim = x_range, ylim = y_range, ylab = "density", xlab = NULL)
# for(i in 1:ncol(data)) {
# lines(all_den_list[[i]], col = cola_opt$color_set_2[cl[i]], lwd = ifelse(cl[i] == j, 2, 0.5))
# }
# mtext(qq("subgroup @{j}/@{k}"), side = 4, line = 1)
# }
# par(op)
# })
# == title
# Compare Signatures from Different k
#
# == param
# -object A `ConsensusPartition-class` object.
# -k Number of subgroups. Value should be a vector.
# -verbose Whether to print message.
# -... Other arguments passed to `get_signatures,ConsensusPartition-method`.
#
# == details
# It plots an Euler diagram showing the overlap of signatures from different k.
#
# == example
# \donttest{
# data(golub_cola)
# res = golub_cola["ATC", "skmeans"]
# compare_signatures(res)
# }
setMethod(f = "compare_signatures",
signature = "ConsensusPartition",
definition = function(object, k = object@k, verbose = interactive(), ...) {
check_pkg("eulerr", bioc = FALSE)
sig_list = sapply(k, function(x) {
tb = get_signatures(object, k = x, verbose = verbose, ..., plot = FALSE)
if(is.null(tb)) {
return(integer(0))
} else {
return(tb$which_row)
}
})
l = sapply(sig_list, length) > 0
if(any(l) && verbose) {
qqcat("Following k have no signature found: \"@{paste(k[l], collapse=', ')}\"\n")
}
sig_list = sig_list[l]
names(sig_list) = paste(k[l], "-group", sep = "")
plot(eulerr::euler(sig_list), legend = TRUE, quantities = TRUE, main = "Signatures from different k")
})
# == title
# Find a best k for the k-means clustering
#
# == param
# -mat A matrix where k-means clustering is executed by rows.
# -max_km Maximal k to try.
#
# == details
# The best k is determined by looking for the knee/elbow of the WSS curve (within-cluster sum of square).
#
# Note this function is only for a rough and quick estimation of the best k.
#
find_best_km = function(mat, max_km = 15) {
wss = (nrow(mat)-1)*sum(apply(mat,2,var))
max_km = min(c(nrow(mat) - 1, max_km))
for (i in 2:max_km) wss[i] = sum(kmeans(mat, centers = i, iter.max = 50)$withinss)
row_km = min(elbow_finder(1:max_km, wss)[1], knee_finder(1:max_km, wss)[1])
return(row_km)
}
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