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.

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 = c(0L, 200L, 400L, 600L, 800L, 1020L)
)

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

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)
p