Heatmap of motif enrichments.

Plot motif enrichments (e.g. significance or magnitude) as a heatmap.

Usage

plotMotifHeatmaps(
  x,
  which.plots = c("negLog10P", "pearsonResid", "negLog10Padj", "log2enr"),
  width = 4,
  col.enr = c("#053061", "#2166AC", "#4393C3", "#92C5DE", "#D1E5F0", "#F7F7F7",
    "#FDDBC7", "#F4A582", "#D6604D", "#B2182B", "#67001F"),
  col.sig = c("#F0F0F0", "#D9D9D9", "#BDBDBD", "#969696", "#737373", "#525252",
    "#252525", "#000000"),
  col.gc = c("#F7FCF5", "#E5F5E0", "#C7E9C0", "#A1D99B", "#74C476", "#41AB5D", "#238B45",
    "#006D2C", "#00441B"),
  maxEnr = NULL,
  maxSig = NULL,
  highlight = NULL,
  cluster = FALSE,
  show_dendrogram = FALSE,
  show_motif_GC = FALSE,
  show_seqlogo = FALSE,
  show_bin_legend = FALSE,
  width.seqlogo = 1.5,
  use_raster = FALSE,
  na_col = "white",
  doPlot = TRUE,
  ...
)

Arguments

x: A SummarizedExperiment with numerical matrices (motifs-by-bins) in its assays(), typically the return value of calcBinnedMotifEnrR or calcBinnedMotifEnrHomer.
which.plots: Selects which heatmaps to plot (one or several from "negLog10P", "negLog10Padj", "pearsonResid" and "log2enr").
width: The width (in inches) of each individual heatmap, without legend.
col.enr: Colors used for enrichment heatmap ("pearsonResid" and "log2enr").
col.sig: Colors used for significance hetmaps ("negLog10P" and "negLog10Padj").
col.gc: Colors used for motif GC content (for show_motif_GC = TRUE).
maxEnr: Cap color mapping at enrichment = maxEnr (default: 99.5th percentile).
maxSig: Cap color mapping at -log10 P value or -log10 FDR = maxSig (default: 99.5th percentile).
highlight: A logical vector indicating motifs to be highlighted.
cluster: If TRUE, the order of transcription factors will be determined by hierarchical clustering of the "pearsonResid" component. Alternatively, an hclust-object can be supplied which will determine the motif ordering. No reordering is done for cluster = FALSE.
show_dendrogram: If cluster != FALSE, controls whether to show a row dendrogram for the clustering of motifs. Ignored for cluster = FALSE.
show_motif_GC: If TRUE, show a column with the percent G+C of the motif as part of the heatmap.
show_seqlogo: If TRUE, show a sequence logo next to each motif label. This will likely only make sense for a heatmap with a low number of motifs.
show_bin_legend: If TRUE, show a legend for the bin labels. If FALSE (default), the bin legend will be hidden.
width.seqlogo: The width (in inches) for the longest sequence logo (shorter logos are drawn to scale).
use_raster: TRUE or FALSE (default). Passed to use_raster of Heatmap.
na_col: "white" (default). Passed to na_col of Heatmap.
doPlot: If TRUE (default), plot the generated heatmap(s) using Reduce(ComplexHeatmap::add_heatmap, heatmapList). If FALSE, just return the list of heatmap(s) (heatmapList) in example before), allowing to modify them further before plotting.
...: Further arguments passed to Heatmap when creating the main heatmaps selected by which.plots. For example, the following will set the font size of the motif names: plotMotifHeatmaps(..., row_names_gp = gpar(fontsize = 12))

Value

A list of ComplexHeatmap::Heatmap objects.

Details

The heatmaps are created using the ComplexHeatmap package and plotted side-by-side.

Each heatmap will be width inches wide, so the total plot needs a graphics device with a width of at least length(which.plots) * width plus the space used for motif names and legend. The height will be auto-adjusted to the graphics device.

References

Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 2016.

Examples

se <- readRDS(system.file("extdata", 
                          "results.binned_motif_enrichment_LMRs.rds", 
                          package = "monaLisa"))
i <- which(SummarizedExperiment::assay(se, "negLog10Padj")[, 8] > 4)
plotMotifHeatmaps(se[i, ], which.plots = "pearsonResid",
                  width = 2, show_seqlogo = TRUE)