TransProR (version 1.0.2)

edgeR_analyze: 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
)

Value

A data frame of differential expression results.

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.

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)