batch_pcc: Batch Calculation of Partial Correlation Coefficients

In IOBR: Immune Oncology Biological Research

Batch Calculation of Partial Correlation Coefficients

Description

Computes partial correlation coefficients between multiple features and a target variable while controlling for an interference (confounding) variable. Adjusts p-values for multiple testing using the Benjamini-Hochberg method.

Usage

batch_pcc(
  input,
  interferenceid,
  target,
  features,
  method = c("pearson", "spearman", "kendall")
)

Arguments

input	Data frame containing feature variables, target variable, and interference variable.
interferenceid	Character string specifying the column name of the interference (confounding) variable to control for.
target	Character string specifying the column name of the target variable.
features	Character vector specifying the column names of feature variables to correlate with the target.
method	Character string specifying the correlation method. Options are '"pearson"', '"spearman"', or '"kendall"'. Default is '"pearson"'.

Value

Tibble containing the following columns for each feature:

sig_names

Feature name

p.value

Raw p-value

statistic

Partial correlation coefficient

p.adj

Adjusted p-value (Benjamini-Hochberg method)

log10pvalue

Negative log10-transformed p-value

stars

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

Author(s)

Rongfang Shen

Examples

# Create small example data
set.seed(123)
test_data <- data.frame(
  TumorPurity = runif(100),
  TargetVar = rnorm(100),
  Signature1 = rnorm(100),
  Signature2 = rnorm(100)
)
# Calculate partial correlations controlling for tumor purity
res <- batch_pcc(
  input = test_data,
  interferenceid = "TumorPurity",
  target = "TargetVar",
  method = "pearson",
  features = c("Signature1", "Signature2")
)
head(res)

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