Sender-by-receiver heatmap of aggregated CCI scores across LR pairs

Generic function. Aggregates CCI scores across all (or the top-ranked) ligand-receptor pairs and draws a clustered receiver-by-sender heatmap, one cell per Sender \(\rightarrow\) Receiver combination.

Usage

plotCCIsummary(x, ...)

# S3 method for class 'blisa'
plotCCIsummary(
  x,
  top_lr = NULL,
  sender = NULL,
  receiver = NULL,
  agg_fun = sum,
  main = NULL,
  ...
)

# Default S3 method
plotCCIsummary(
  x,
  top_lr = NULL,
  sender = NULL,
  receiver = NULL,
  agg_fun = sum,
  main = NULL,
  ...
)

Arguments

x: A blisa object or a CCI scores data frame (the CCI_scores slot of a blisa object).
...: Additional arguments passed to the relevant method.
top_lr: Integer or NULL. Number of top-ranked LR pairs (by sig_numbers) to include before aggregating. LR pairs in CCI_scores are already ordered by rank, so this takes the first top_lr columns. NULL (default) uses all pairs.
sender: Character vector or NULL. If provided, only rows where Sender is in this vector are kept (AND logic with receiver). Default NULL (all senders).
receiver: Character vector or NULL. If provided, only rows where Receiver is in this vector are kept (AND logic with sender). Default NULL (all receivers).
agg_fun: Function used to aggregate scores across LR pairs for each Sender \(\rightarrow\) Receiver combination. Receives a numeric vector with NAs already removed. Default sum.
main: Character or NULL. Title drawn above the heatmap. When supplied, the heatmap is drawn with this overall title (via ComplexHeatmap::draw); the Heatmap object is returned invisibly. Default NULL (no title; object returned for the caller to print).

Value

A Heatmap object.

Methods (by class)

plotCCIsummary(blisa): Method for a blisa object. Stops with an informative error if CCI_scores is NULL.
plotCCIsummary(default): Method for a CCI scores data frame (e.g. the CCI_scores slot of a blisa object).

Examples

if (FALSE) { # \dontrun{
# Continuing from the blisa() example:
# result <- blisa(spe, bin_size = 50, group = "cell_type")
plotCCIsummary(result)
plotCCIsummary(result, top_lr = 10, agg_fun = mean)
} # }