pydeseq2

To identify differentially expressed genes from RNA-seq count data, this Skill applies normalization, dispersion estimation, and Wald tests with FDR correction to count matrices and metadata to output significant expression changes.

Yes, differential gene expression analysis supports single-factor and multi-factor experimental designs, including batch effect correction and interaction terms, allowing you to isolate specific biological variables from confounding technical noise.

You need bulk RNA-seq count data formatted as a CSV count matrix, a corresponding metadata CSV file, and a specified design formula like '~condition' to compare expression levels across your experimental groups.

PyDESeq2 handles normalization and statistical testing by estimating dispersions from count matrices, fitting statistical models to the metadata, and applying Wald tests with FDR correction to pinpoint significant gene expression changes.

The best way to compare gene expression between treated and control cells is to input your RNA-seq count matrix and metadata with a design formula, allowing the statistical model to identify significantly upregulated or downregulated genes.

A limitation is that this approach is designed specifically for bulk RNA-seq count data and relies on dependencies like pandas, numpy, scipy, and anndata, making it unsuitable for single-cell RNA-seq workflows.