Skill UI

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

PyDESeq2 is a robust Python implementation of DESeq2, designed for comprehensive differential expression analysis using bulk RNA-seq count data. It supports complex formulaic designs, performs Wald tests with multiple testing correction (FDR), and includes advanced features like LFC shrinkage, enabling researchers to analyze gene expression changes between various experimental conditions within Python pipelines.

scvi-tools is a comprehensive Python framework utilizing deep generative models and variational inference for analyzing diverse single-cell data modalities. It excels in complex analyses such as probabilistic batch correction (scVI), multi-modal integration (MultiVI), and differential expression analysis with uncertainty. It supports single-cell RNA-seq, ATAC-seq, and various multi-omics datasets, providing advanced tools for modern genomic research.

This skill orchestrates a complete, reproducible pipeline for bulk RNA-seq differential expression analysis. It guides users from raw FASTQ reads through QC, trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and concluding with differential expression analysis (DESeq2) and pathway enrichment. It ensures statistical soundness and reproducibility by connecting dedicated tools and enforcing proper experimental design.