Generate a pairwise correlation for chipseq data.

Given an object of the class parcutils_chip it generates correlation heatmap for selected samples and observations (peaks).

Usage

plot_chip_pairwise_correlation_heatmap(
  x,
  samples = NULL,
  peaks = NULL,
  rename_samples = NULL,
  scale_min = 0,
  scale_max = 1,
  col = c("blue", "white", "red"),
  cor_method = c("pearson"),
  plot_title = "title",
  ...
)

Arguments

x

an object of the class parcutils_chip.

samples

a character vector denoting samples to show in the plot. Values must be a subset of colnames(rowData(x)).

peaks

a character vector denoting peaks (observations) to use for correlation. Values must be a subset of rownames(rowData(x)).

rename_samples

a character vector denoting sample names to show in the plot. Values must be of same order and length of values given as an argument sample.

scale_min

a numeric value denoting a minimum value for the scale.

scale_max

a numeric value denoting a maximum value for the scale.

col

a character string of valid colors. Number of colors are not restricted.

cor_method

a character string denoting a method to calculate correlation values. Values can be one of "pearson" (default), "kendall", or "spearman".

plot_title

a character string denoting a plot title.

...

other arguments pass to the function ggcorrplot::ggcorrplot().

Value

a plot

Details

correlation is calculated based on RPM value of each feature. RPM is simply sum of values across the feature i.e. rowsum.

Examples

if (FALSE) {

}