normalize_genomic_signals_to_bins: Overlap genomic signals to the genomic bins

In jokergoo/ComplexHeatmap: Make Complex Heatmaps

Overlap genomic signals to the genomic bins

Description

Overlap genomic signals to the genomic bins

Usage

normalize_genomic_signals_to_bins(gr, value, value_column = NULL, method = "weighted",
    empty_value = NA, window = GHEATMAP_ENV$chr_window)

Arguments

gr	A GRanges object.
value	The corresponding signals corresponding to gr.
value_column	If value is not set and the values are in the meta-columns in gr, you can specify the column indices for these value columns, better to use name indices.
method	One of "weighted", "w0" and "absolute". For the three different methods, please refer to https://bioconductor.org/packages/release/bioc/vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html#toc_7 .
empty_value	The value for the bins where no signal is overlapped.
window	The genomic bins generated from bin_genome.

Details

The genomic bins should be generated by bin_genome in advance. The genomic bins are saved internally, so that multiple uses of bin_genome ensure they all return the matrices with the same rows.

It supports following values.

• When neither value nor value_column is set, it simply overlap gr to the genomic bins and returns a one-column logical matrix which represents whether the current genomic bin overlaps to any signal.

• When the signals are numeric, value can be a numeric vector or a matrix, or value_column can contain multiple columns. The function returns a numeric matrix where the values are properly averaged depending on what method was used.

• When the signals are character, value can only be a vector or value_column can only contain one single column. The function returns a one-column character matrix.

Value

A matrix with the same row as the genomic bins.

Examples

## Not run: 
require(circlize)
require(GenomicRanges)

chr_window = bin_genome("hg19")

#### the first is a numeric matrix #######
bed1 = generateRandomBed(nr = 1000, nc = 10)
gr1 = GRanges(seqnames = bed1[, 1], ranges = IRanges(bed1[, 2], bed1[, 3]))

num_mat = normalize_genomic_signals_to_bins(gr1, bed1[, -(1:3)])

#### the second is a character matrix ######
bed_list = lapply(1:10, function(i) {
    generateRandomBed(nr = 1000, nc = 1, 
        fun = function(n) sample(c("gain", "loss"), n, replace = TRUE))
})
char_mat = NULL
for(i in 1:10) {
    bed = bed_list[[i]]
    bed = bed[sample(nrow(bed), 20), , drop = FALSE]
    gr_cnv = GRanges(seqnames = bed[, 1], ranges = IRanges(bed[, 2], bed[, 3]))

    char_mat = cbind(char_mat, normalize_genomic_signals_to_bins(gr_cnv, bed[, 4]))
}

#### two numeric columns ##########
bed2 = generateRandomBed(nr = 100, nc = 2)
gr2 = GRanges(seqnames = bed2[, 1], ranges = IRanges(bed2[, 2], bed2[, 3]))

v = normalize_genomic_signals_to_bins(gr2, bed2[, 4:5])

##### a list of genes need to be highlighted
bed3 = generateRandomBed(nr = 40, nc = 0)
gr3 = GRanges(seqnames = bed3[, 1], ranges = IRanges(bed3[, 2], bed3[, 2]))
gr3$gene = paste0("gene_", 1:length(gr3))

mtch = as.matrix(findOverlaps(chr_window, gr3))
at = mtch[, 1]
labels = mcols(gr3)[mtch[, 2], 1]

##### order of the chromosomes ########
chr = as.vector(seqnames(chr_window))
chr_level = paste0("chr", c(1:22, "X", "Y"))
chr = factor(chr, levels = chr_level)

#### make the heatmap #######
subgroup = rep(c("A", "B"), each = 5)

ht_opt$TITLE_PADDING = unit(c(4, 4), "points")
ht_list = Heatmap(num_mat, name = "mat", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")),
    row_split = chr, cluster_rows = FALSE, show_column_dend = FALSE,
    column_split = subgroup, cluster_column_slices = FALSE,
    column_title = "numeric matrix",
    top_annotation = HeatmapAnnotation(subgroup = subgroup, annotation_name_side = "left"),
    row_title_rot = 0, row_title_gp = gpar(fontsize = 10), border = TRUE,
    row_gap = unit(0, "points")) +
Heatmap(char_mat, name = "CNV", col = c("gain" = "red", "loss" = "blue"),
    border = TRUE, column_title = "character matrix") +
rowAnnotation(label = anno_mark(at = at, labels = labels)) +
rowAnnotation(pt = anno_points(v, gp = gpar(col = 4:5), pch = c(1, 16)), 
    width = unit(2, "cm")) +
rowAnnotation(bar = anno_barplot(v[, 1], gp = gpar(col = ifelse(v[ ,1] > 0, 2, 3))), 
    width = unit(2, "cm"))
draw(ht_list, merge_legend = TRUE)

##### or horizontally ###
ht_list = Heatmap(t(num_mat), name = "mat", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")),
    column_split = chr, cluster_columns = FALSE, show_row_dend = FALSE,
    row_split = subgroup, cluster_row_slices = FALSE,
    row_title = "numeric matrix",
    left_annotation = rowAnnotation(subgroup = subgroup, show_annotation_name = FALSE,
        annotation_legend_param = list(
            subgroup = list(direction = "horizontal", title_position = "lefttop", nrow = 1))),
    column_title_gp = gpar(fontsize = 10), border = TRUE,
    column_gap = unit(0, "points"),
    column_title = ifelse(seq_along(chr_level) %% 2 == 0, paste0("\n", chr_level), paste0(chr_level, "\n")),
    heatmap_legend_param = list(direction = "horizontal", title_position = "lefttop")) %v%
Heatmap(t(char_mat), name = "CNV", col = c("gain" = "red", "loss" = "blue"),
    border = TRUE, row_title = "character matrix",
    heatmap_legend_param = list(direction = "horizontal", title_position = "lefttop", nrow = 1)) %v%
HeatmapAnnotation(label = anno_mark(at = at, labels = labels, side = "bottom")) %v%
HeatmapAnnotation(pt = anno_points(v, gp = gpar(col = 4:5), pch = c(1, 16)),
    annotation_name_side = "left", height = unit(2, "cm")) %v%
HeatmapAnnotation(bar = anno_barplot(v[, 1], gp = gpar(col = ifelse(v[ ,1] > 0, 2, 3))), 
    annotation_name_side = "left", height = unit(2, "cm"))
draw(ht_list, heatmap_legend_side = "bottom", merge_legend = TRUE)

## End(Not run)

jokergoo/ComplexHeatmap documentation built on Sept. 24, 2024, 4:48 p.m.