limma_analyze: Differential Gene Expression Analysis using limma and voom

In TransProR: Analysis and Visualization of Multi-Omics Data

Differential Gene Expression Analysis using limma and voom

Description

This function performs differential gene expression analysis using the 'limma' package with voom normalization. It reads tumor and normal expression data, merges them, filters low-expressed genes, normalizes the data, performs limma analysis, and outputs the results along with information on gene expression changes.

Usage

limma_analyze(
  tumor_file,
  normal_file,
  output_file,
  logFC_threshold = 2.5,
  p_value_threshold = 0.01
)

Arguments

tumor_file	Path to the tumor data file (RDS format).
normal_file	Path to the normal data file (RDS format).
output_file	Path to save the output DEG data (RDS format).
logFC_threshold	Threshold for log fold change for marking up/down-regulated genes.
p_value_threshold	Threshold for p-value for filtering significant genes.

Value

A data frame of differential expression results.

References

limma:Linear Models for Microarray and RNA-Seq Data User’s Guide. For more information, visit the page: https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf

Examples

# Define file paths for tumor and normal data from the data folder
tumor_file <- system.file("extdata",
                          "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds",
                          package = "TransProR")
normal_file <- system.file("extdata",
                           "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds",
                           package = "TransProR")
output_file <- file.path(tempdir(), "DEG_limma_voom.rds")

DEG_limma_voom <- limma_analyze(
  tumor_file = tumor_file,
  normal_file = normal_file,
  output_file = output_file,
  logFC_threshold = 2.5,
  p_value_threshold = 0.01
)

# View the top 5 rows of the result
head(DEG_limma_voom, 5)

TransProR documentation built on April 4, 2025, 3:16 a.m.