Create heatmap and distribution plots of the different steps of computing log R ratios

This function generates heatmap and distribution plots for tumor and reference cells for any step of computing log R ratios matrices. The input object corresponds to the output of computeLogRatioATAC() or computeLogRatioRNA() with the argument all_steps = TRUE.

Usage

heatmapStep(
  obj,
  step,
  filename,
  title = NULL,
  col_quantiles = c(0.1, 0.4, 0.6, 0.9),
  col_breaks = NULL,
  colors = c("#00008E", "white", "white", "#630000")
)

Arguments

obj: A list provided as output by the computeLogRatioATAC() or computeLogRatioRNA() functions with the argument all_steps = TRUE. It includes tumor and reference matrices at each step of the computing of log R ratio matrices.
step: The step within the obj list to use for plotting (character string). It must match one of the names in obj.
filename: File path to save the output plot (character string). The file format is inferred from the extension (".png", ".pdf" or ".svg").
title: Title of the plot (character string). If NULL, the title is automatically generated using the provided step argument and its corresponding value name (obj[[step]]$name).
col_quantiles: A numeric vector of length 4, specifying the quantiles to use for the color breakpoints in the heatmap (numeric). Either col_quantiles or col_breaks must be provided, if both are provided col_breaks is used. Default is c(0.1, 0.4, 0.6, 0.9).
col_breaks: A numeric vector of length 4, specifying custom breakpoints for the color scale in the heatmap (numeric). Either col_quantiles or col_breaks must be provided, if both are provided col_breaks is used. Default is NULL.
colors: A character vector of 4 colors used for the color scale of the heatmap (character vector). Default is c("#00008E", "white", "white", "#630000").

Value

The function does not return any value but saves a heatmaps-histograms plot to the specified file.

Examples

if (FALSE) { # \dontrun{
# Create muscomic objects
atac <- CreateMuscomicObject(
    type = "ATAC",
    mat_counts = mat_counts_atac_tumor,
    allele_counts = allele_counts_atac_tumor,
    features = peaks
)
rna <- CreateMuscomicObject(
    type = "RNA",
    mat_counts = mat_counts_rna_tumor,
    allele_counts = allele_counts_rna_tumor,
    features = genes
)
atac_ref <- CreateMuscomicObject(
    type = "ATAC",
    mat_counts = mat_counts_atac_ref,
    allele_counts = allele_counts_atac_ref,
    features = peaks
)
rna_ref <- CreateMuscomicObject(
    type = "RNA",
    mat_counts = mat_counts_rna_ref,
    allele_counts = allele_counts_rna_ref,
    features = genes
)

# Create muscadet objects
muscadet <- CreateMuscadetObject(
    omics = list(atac, rna),
    bulk.lrr = bulk_lrr,
    bulk.label = "WGS",
    genome = "hg38"
)
muscadet_ref <- CreateMuscadetObject(
    omics = list(atac_ref, rna_ref),
    genome = "hg38"
)

# Compute log R ratios with `all_steps = TRUE`
obj_atac_all <- computeLogRatioATAC(
    matTumor = matCounts(muscadet)$ATAC,
    matRef = matCounts(muscadet_ref)$ATAC,
    peaksCoord = coordFeatures(muscadet)$ATAC,
    genome = slot(muscadet, "genome"),
    minReads = 1, # low value for example subsampled datasets
    minPeaks = 1, # low value for example subsampled datasets
    all_steps = TRUE
)
names(obj_atac_all)

# Plot heatmap and distribution of values for Step01
heatmapStep(obj = obj_atac_all,
            step = "step01",
            filename = file.path(tempdir(), "step01.png"),
            title = "Example data - Step 01")

# Plot heatmap and distribution of values for all steps
for (step in grep("step", names(obj_atac_all), value = TRUE)) {
    heatmapStep(
        obj_atac_all,
        step,
        filename = file.path(tempdir(), paste0("ATAC_", step, ".pdf")),
        title = paste("ATAC -", step)
    )
}
} # }