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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/DESeq2Analyze.R
+\name{DESeq2_analyze}
+\alias{DESeq2_analyze}
+\title{Differential Gene Expression Analysis using 'DESeq2'}
+\usage{
+DESeq2_analyze(
+  tumor_file,
+  normal_file,
+  output_file,
+  logFC = 2.5,
+  p_value = 0.01
+)
+}
+\arguments{
+\item{tumor_file}{Path to the tumor data file (RDS format).}
+
+\item{normal_file}{Path to the normal data file (RDS format).}
+
+\item{output_file}{Path to save the output DEG data (RDS format).}
+
+\item{logFC}{Threshold for log fold change.}
+
+\item{p_value}{Threshold for p-value.}
+}
+\value{
+A data frame of differential expression results.
+}
+\description{
+'DESeq2': Differential gene expression analysis based on the negative binomial distribution.
+This function utilizes the 'DESeq2' package to conduct differential gene expression analysis.
+It processes tumor and normal expression data, applies DESeq2 analysis,
+and outputs the results along with information on gene expression changes.
+}
+\details{
+The DESeq2 methodology is based on modeling count data using a negative binomial distribution,
+which allows for handling the variability observed in gene expression data, especially in
+small sample sizes. This approach is well-suited for RNA-Seq data analysis.
+}
+\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_DESeq2.rds")
+
+DEG_DESeq2 <- DESeq2_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_DESeq2, 5)
+
+}
+\references{
+DESeq2:Differential gene expression analysis based on the negative binomial distribution.
+For more information, visit the page:
+https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/
+}