tooluniverse-rnaseq-deseq2

RNA-seq differential expression analysis with DESeq2 requires a count matrix and metadata file in CSV, TSV, or H5AD format. The process automates normalization, dispersion estimation, Wald testing, and LFC shrinkage to identify differentially expressed genes.

Yes, RNA-seq differential expression analysis supports multi-factor designs and multiple contrasts. You can account for batch effects by specifying them in your metadata file alongside your primary experimental condition variables.

Gene enrichment and annotation integration uses gseapy and tooluniverse dependencies to automatically map differentially expressed genes to functional annotations and perform enrichment analysis directly after the DESeq2 statistical testing workflow.

Yes, DESeq2 differential expression analysis supports H5AD input format along with CSV and TSV files. You can provide your RNA-seq count matrix in any of these formats for processing with the pydeseq2 implementation.

Pydeseq2 for RNA-seq differential expression requires input count matrices rather than raw sequencing reads, meaning prior alignment and quantification must be completed. It relies on Python implementations of the DESeq2 statistical framework for analysis.

LFC shrinkage in RNA-seq differential expression analysis is needed when estimating log2 fold changes for genes with low counts or high variance. It stabilizes effect size estimates for more accurate ranking and visualization of differentially expressed genes.