get_cor_matrix: Calculate and Visualize Correlation Matrix Between Two...

In IOBR: Immune Oncology Biological Research

Calculate and Visualize Correlation Matrix Between Two Variable Sets

Description

Calculates and visualizes the correlation matrix between two sets of variables. Supports Pearson, Spearman, and Kendall correlation methods. The function generates a customizable heatmap with significance stars.

Usage

get_cor_matrix(
  data,
  feas1,
  feas2,
  method = c("pearson", "spearman", "kendall"),
  path = NULL,
  index = 1,
  fig.type = "pdf",
  width = NULL,
  height = NULL,
  project = NULL,
  is.matrix = FALSE,
  scale = TRUE,
  font.size = 15,
  fill_by_cor = FALSE,
  round.num = 1,
  font.size.star = 8,
  cols = NULL
)

Arguments

data	Input data frame or matrix. Variables should be in columns.
feas1	Character vector of variable names for the first set.
feas2	Character vector of variable names for the second set.
method	Correlation method: '"pearson"', '"spearman"', or '"kendall"'. Default is '"pearson"'.
path	Directory to save the plot. If 'NULL', plot is not saved. Default is 'NULL'.
index	Numeric prefix for output filename. Default is 1.
fig.type	File format: '"pdf"', '"png"', etc. Default is '"pdf"'.
width	Plot width in inches. Auto-calculated if 'NULL'.
height	Plot height in inches. Auto-calculated if 'NULL'.
project	Project name for plot title. Default is 'NULL'.
is.matrix	Logical: if 'TRUE', data is transposed. Default is 'FALSE'.
scale	Logical: scale variables before correlation. Default is 'TRUE'.
font.size	Font size for axis labels. Default is 15.
fill_by_cor	Logical: show correlation values instead of stars. Default is 'FALSE'.
round.num	Decimal places for correlation values. Default is 1.
font.size.star	Font size for significance stars. Default is 8.
cols	Custom colors for gradient (low, mid, high). If 'NULL', uses blue-white-red. Default is 'NULL'.

Value

ggplot object displaying the correlation matrix heatmap.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
data <- as.data.frame(matrix(rnorm(1000), nrow = 100, ncol = 10))
colnames(data) <- paste0("Gene_", 1:10)

feas1 <- c("Gene_1", "Gene_2", "Gene_3")
feas2 <- c("Gene_4", "Gene_5", "Gene_6")

cor_plot <- get_cor_matrix(
  data = data,
  feas1 = feas1,
  feas2 = feas2,
  method = "spearman",
  project = "Example Correlation"
)

IOBR documentation built on May 30, 2026, 5:07 p.m.