Differential Expression Analysis

Performs differential expression analysis using DESeq2 on a given count matrix and sample information.

Usage

diffExpAnalysis(
  countData,
  sampleInfo,
  method = "DESeq2",
  cutoff = 10,
  annotation
)

Arguments

countData

A matrix or data frame of raw count data. Rows represent genes, and columns represent samples.

sampleInfo

A data frame containing sample metadata. Must include a `condition` column specifying the experimental conditions.

method

A string specifying the method for differential expression analysis. Currently supports only `"DESeq2"`. Default is `"DESeq2"`.

cutoff

An integer specifying the minimum number of counts required across all samples for a gene to be included in the analysis. Default is `10`.

annotation

ee

Value

A data frame containing the results of the differential expression analysis, including adjusted p-values, log fold changes, and other statistics.

Details

This function performs differential expression analysis using the DESeq2 package. It filters genes with low counts, estimates size factors for normalization, and performs the DESeq2 analysis pipeline. Log fold-change shrinkage is applied using the `lfcShrink` function.