batch_cor: Batch Correlation Analysis

In IOBR: Immune Oncology Biological Research

Batch Correlation Analysis

Description

Performs correlation analysis between a target variable and multiple feature variables. Computes correlation coefficients, p-values, and adjusts for multiple testing using the Benjamini-Hochberg method.

Usage

batch_cor(data, target, feature, method = c("spearman", "pearson", "kendall"))

Arguments

data	Data frame containing the target and feature variables.
target	Character string specifying the name of the target variable.
feature	Character vector specifying the names of feature variables to correlate with the target.
method	Character string specifying the correlation method. Options are '"spearman"', '"pearson"', or '"kendall"'. Default is '"spearman"'.

Value

Tibble containing the following columns for each feature:

sig_names

Feature name

p.value

Raw p-value

statistic

Correlation coefficient

p.adj

Adjusted p-value (Benjamini-Hochberg)

log10pvalue

Negative log10-transformed p-value

stars

Significance stars: **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05, + p<0.5

Author(s)

Dongqiang Zeng

Examples

# Create a small example dataset
set.seed(123)
data_df <- as.data.frame(matrix(runif(100 * 10), 100, 10))
colnames(data_df) <- paste0("Signature", 1:10)

# Perform batch correlation
results <- batch_cor(
  data = data_df,
  target = "Signature1",
  feature = colnames(data_df)[2:10]
)
head(results)

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