pydeseq2-differential-expression

To find differentially expressed genes from RNA-seq data, you need count matrices and metadata to fit negative binomial models. This Skill performs statistical tests using PyDESeq2 to identify genes with significant expression changes between experimental conditions.

Yes, you can perform batch correction for multi-factor RNA-seq differential expression analysis. The Skill supports both two-group and multi-factor designs, allowing you to specify design and contrast parameters to account for batch effects in your metadata.

The best way to visualize RNA-seq differential expression results is through volcano and MA plots. This Skill generates publication-ready visualizations directly from your analyzed count matrices to help interpret log2 fold changes and statistical significance.

Yes, PyDESeq2 supports log2 fold change shrinkage for bulk RNA-seq data. Along with library size normalization and Wald statistical tests, this feature provides more accurate expression estimation for identifying significantly upregulated or downregulated genes.

You need pandas and anndata along with numpy and scipy to run bulk RNA-seq differential expression analysis. These dependencies handle the input count matrices and metadata required to fit the negative binomial models for statistical testing.