Plots heatmap of top contributing genes for each mode

plot_heatmap_pathway_top_contrib_genes(
  expr_mat,
  PCA_object,
  module,
  n = 10,
  meta = NULL,
  annot_name = NULL,
  cluster_cols = T,
  fontsize = 7,
  colors_annot = NULL
)

Arguments

expr_mat: Numeric matrix, containing counts, normalized or not.
PCA_object: List, output from run_activity_analysis().
module: Character, module for which you want to plot activity
n: Number of top contributors to plot for each mode
meta: Dataframe containing metadata, with cell labels as rownames.
annot_name: Character, name of the column in meta that should be used to compute homogeneity for.
cluster_cols: Boolean, set to TRUE to perform column clustering.
fontsize: Integer, base fontsize for the plot.
colors_annot: Vector of colors to use.

Value

heatmap plot of top contributor genes for each informative mode of activation, ordered by decreasing contribution