Calculate and Visualize Correlation Matrix Between Two Variable Sets

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

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"
)
#> ℹ Calculating spearman correlation: 3 x 3