bio-de-deseq2-basics - DESeq2 Differential Expression Analysis for RNA-Seq Data
Performs differential expression analysis on RNA-seq count data using DESeq2 in R, including dataset creation, model fitting, and LFC shrinkage.
Tags
Updated: 2026-09-24Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Create DESeqDataSet objects
- Run DESeq2 differential analysis pipeline
- Apply log fold change shrinkage
- Specify experimental design formulas
- Extract differential expression results
- Obtain normalized gene expression counts
- Fit interaction and multi-factor models
- Perform likelihood ratio tests
- Filter low expression genes
- Export results to CSV files
Inputs
- RNA-seq count matrices
- Sample metadata dataframes
- Experimental design formulas
- tximport or SummarizedExperiment objects
Outputs
- DESeqDataSet objects
- Differential expression result tables
- Normalized count matrices
- CSV files
Requirements
- R environment
- DESeq2 package 1.42 or higher
- apeglm package