edgeR_analyze: Differential Gene Expression Analysis using 'edgeR'

In TransProR: Analysis and Visualization of Multi-Omics Data

Differential Gene Expression Analysis using 'edgeR'

Description

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

Usage

edgeR_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

edgeR: Differential analysis of sequence read count data. For more information, visit the edgeR Bioconductor page: https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.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_edgeR.rds")

DEG_edgeR <- edgeR_analyze(
  tumor_file = tumor_file,
  normal_file = normal_file,
  output_file = output_file,
  2.5,
  0.01
)

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

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