get_cor: Calculate and Visualize Correlation Between Two Variables
In IOBR: Immune Oncology Biological Research

get_cor

R Documentation

Calculate and Visualize Correlation Between Two Variables

Description

Calculates and visualizes the correlation between two variables with options for scaling, handling missing values, and incorporating grouping data.

Usage

get_cor(
  eset,
  pdata = NULL,
  var1,
  var2,
  is.matrix = FALSE,
  id_eset = "ID",
  id_pdata = "ID",
  scale = TRUE,
  subtype = NULL,
  na.subtype.rm = FALSE,
  color_subtype = NULL,
  palette = "jama",
  index = NULL,
  method = c("spearman", "pearson", "kendall"),
  show_cor_result = TRUE,
  col_line = NULL,
  id = NULL,
  show_label = FALSE,
  point_size = 4,
  title = NULL,
  alpha = 0.5,
  title_size = 1.5,
  text_size = 10,
  axis_angle = 0,
  hjust = 0,
  show_plot = TRUE,
  save_plot = FALSE,
  path = NULL,
  fig.format = "png",
  fig.width = 7,
  fig.height = 7.3,
  add.hdr.line = FALSE
)

Arguments

`eset`	Dataset containing the variables (data frame or matrix).
`pdata`	Optional phenotype data frame. Default is 'NULL'.
`var1`	Name of the first variable.
`var2`	Name of the second variable.
`is.matrix`	Logical indicating if 'eset' is a matrix with features as rows. Default is 'FALSE'.
`id_eset`	ID column in 'eset'. Default is '"ID"'.
`id_pdata`	ID column in 'pdata'. Default is '"ID"'.
`scale`	Logical indicating whether to scale data. Default is 'TRUE'.
`subtype`	Optional grouping variable for coloring points. Default is 'NULL'.
`na.subtype.rm`	Logical indicating whether to remove NA in subtype. Default is 'FALSE'.
`color_subtype`	Colors for subtypes. Default is 'NULL'.
`palette`	Color palette name. Default is '"jama"'.
`index`	Plot index for filename. Default is 'NULL' (uses 1).
`method`	Correlation method: '"spearman"', '"pearson"', or '"kendall"'. Default is '"spearman"'.
`show_cor_result`	Logical indicating whether to print correlation result. Default is 'TRUE'.
`col_line`	Color of regression line. Default is 'NULL' (auto-determine).
`id`	Column for point labels. Default is 'NULL'.
`show_label`	Logical indicating whether to show labels. Default is 'FALSE'.
`point_size`	Size of points. Default is 4.
`title`	Plot title. Default is 'NULL'.
`alpha`	Transparency of points. Default is 0.5.
`title_size`	Title font size. Default is 1.5.
`text_size`	Text font size. Default is 10.
`axis_angle`	Axis label angle. Default is 0.
`hjust`	Horizontal justification. Default is 0.
`show_plot`	Logical indicating whether to display plot. Default is 'TRUE'.
`save_plot`	Logical indicating whether to save plot. Default is 'FALSE'.
`path`	Save path. Default is 'NULL'.
`fig.format`	Figure format: '"png"' or '"pdf"'. Default is '"png"'.
`fig.width`	Figure width in inches. Default is 7.
`fig.height`	Figure height in inches. Default is 7.3.
`add.hdr.line`	Logical for adding HDR (high density region) lines. Default is 'FALSE'.

Value

A ggplot object of the correlation plot.

Author(s)

Dongqiang Zeng

Examples

# Simulate data
set.seed(123)
sim_eset <- matrix(rnorm(100 * 20), 100, 20)
rownames(sim_eset) <- c("GZMB", "CD274", paste0("Gene", 3:100))
colnames(sim_eset) <- paste0("Sample", 1:20)

# Calculate and plot correlation
p <- get_cor(eset = sim_eset, is.matrix = TRUE, var1 = "GZMB", var2 = "CD274", show_plot = FALSE)
if (!is.null(p)) print(p)

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