pydeseq2

Differential gene expression analysis on RNA-seq count data uses the DESeq2 statistical model to normalize counts and identify significantly up- or down-regulated genes between experimental conditions. You provide count matrices and sample metadata to generate results tables and plots.

Python DESeq2 supports both single-factor and multi-factor experimental designs for RNA-seq differential expression. It applies Wald tests and FDR correction to the count data to identify statistically significant gene expression changes across multiple conditions.

Running differential expression analysis on bulk RNA-seq data requires two main inputs: a count matrix containing gene expression counts and a metadata file describing the experimental conditions for each sample.

Pydeseq2 works with pandas and anndata for handling RNA-seq count data and sample metadata. It also integrates with numpy, scipy, scikit-learn, and matplotlib for statistical modeling and generating visualizations.

DESeq2 identifies differentially expressed genes by modeling RNA-seq count data, applying a Wald test to compare conditions like cancer and normal cells, and using FDR correction to adjust p-values for false positives.

This Python DESeq2 implementation processes bulk RNA-seq count data and requires count matrices rather than raw sequencing reads. It is designed for standard differential expression workflows and outputs results tables with statistical significance metrics.