show_sig_feature_corrplot: Draw Corrplot for Signature Exposures and Other Features

In sigminer: Extract, Analyze and Visualize Mutational Signatures for Genomic Variations

Draw Corrplot for Signature Exposures and Other Features

Description

This function is for association visualization. Of note, the parameters p_val and drop will affect the visualization of association results under p value threshold.

Usage

show_sig_feature_corrplot(
  tidy_cor,
  feature_list,
  sort_features = FALSE,
  sig_orders = NULL,
  drop = TRUE,
  return_plotlist = FALSE,
  p_val = 0.05,
  xlab = "Signatures",
  ylab = "Features",
  co_gradient_colors = scale_color_gradient2(low = "blue", mid = "white", high = "red",
    midpoint = 0),
  ca_gradient_colors = co_gradient_colors,
  plot_ratio = "auto",
  breaks_count = NULL
)

Arguments

tidy_cor	data returned by get_tidy_association.
feature_list	a character vector contains features want to be plotted. If missing, all features will be used.
sort_features	default is FALSE, use feature order obtained from the previous step. If TRUE, sort features as feature_list.
sig_orders	signature levels for ordering.
drop	if TRUE, when a feature has no association with all signatures (p value larger than threshold set by p_val), this feature will be removed from the plot. Otherwise, this feature (a row) will keep with all blank white.
return_plotlist	if TRUE, return as a list of ggplot objects.
p_val	p value threshold. If p value larger than this threshold, the result becomes blank white.
xlab	label for x axis.
ylab	label for y axis.
co_gradient_colors	a Scale object representing gradient colors used to plot for continuous features.
ca_gradient_colors	a Scale object representing gradient colors used to plot for categorical features.
plot_ratio	a length-2 numeric vector to set the height/width ratio.
breaks_count	breaks for sample count. If set it to NULL, ggplot bin scale will be used to automatically determine the breaks. If set it to NA, aes for sample will be not used.

Value

a ggplot2 object

See Also

get_tidy_association and get_sig_feature_association

Examples

# The data is generated from Wang, Shixiang et al.
load(system.file("extdata", "asso_data.RData",
  package = "sigminer", mustWork = TRUE
))

p <- show_sig_feature_corrplot(
            tidy_data.seqz.feature,
            p_val = 0.05,
            breaks_count = c(0L,200L, 400L, 600L, 800L, 1020L))
p

sigminer documentation built on May 29, 2024, 3:11 a.m.