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Create a heatmap of features organized by cluster

Source: R/plot_fxns.R
feat_heatmap.Rd

Creates a heatmap of transcription factor activation scores by cells grouped by cluster.

Usage

feat_heatmap(
  dom,
  feats = NULL,
  bool = FALSE,
  bool_thresh = 0.2,
  title = TRUE,
  norm = FALSE,
  cols = NULL,
  ann_cols = TRUE,
  min_thresh = NULL,
  max_thresh = NULL,
  ...
)

Arguments

dom

Domino object with network built (build_domino())

feats

Either a vector of features to include in the heatmap or 'all' for all features. If left NULL then the features selected for the signaling network will be shown.

bool

Boolean indicating whether the heatmap should be continuous or boolean. If boolean then bool_thresh will be used to determine how to define activity as positive or negative.

bool_thresh

Numeric indicating the threshold separating 'on' or 'off' for feature activity if making a boolean heatmap.

title

Either a string to use as the title or a boolean describing whether to include a title. In order to pass the 'main' parameter to ComplexHeatmap::Heatmap() you must set title to FALSE.

norm

Boolean indicating whether or not to normalize the transcrption factors to their max value.

cols

Named vector of colors to annotate cells by cluster color. Values are taken as colors and names as cluster. If left as NULL then default ggplot colors will be generated.

ann_cols

Boolean indicating whether to include cell cluster as a column annotation. Colors can be defined with cols. If FALSE then custom annotations can be passed to ComplexHeatmap::Heatmap().

min_thresh

Minimum threshold for color scaling if not a boolean heatmap

max_thresh

Maximum threshold for color scaling if not a boolean heatmap

...

Other parameters to pass to ComplexHeatmap::Heatmap() . Note that to use the 'main' parameter of ComplexHeatmap::Heatmap() you must set title = FALSE and to use 'annCol' or 'annColors' ann_cols must be FALSE.

Value

A heatmap rendered to the active graphics device

Examples

#basic usage
example(build_domino, echo = FALSE)
feat_heatmap(pbmc_dom_built_tiny)

#using thresholds
feat_heatmap(
 pbmc_dom_built_tiny, min_thresh = 0.1, 
 max_thresh = 0.6, norm = TRUE, bool = FALSE)
#> Warning: You are using norm with min_thresh and max_thresh. Note that values will be thresholded AFTER normalization.