pydeseq2

What problem does it solve?

This Skill automates the complex process of identifying differentially expressed genes from RNA-sequencing count data, a crucial step in understanding biological responses and disease mechanisms.

Core Features & Use Cases

• Differential Expression Analysis: Perform statistical tests to find genes with significant expression changes between experimental conditions.
• Data Handling: Supports various input formats (CSV, TSV, AnnData) and includes robust data filtering and preparation steps.
• Multi-Factor Designs: Accommodates complex experimental designs, including batch effects and covariates.
• Visualization: Generates standard plots like volcano and MA plots for result interpretation.
• Use Case: A researcher has RNA-seq data from treated and control cell lines. They can use this Skill to quickly identify which genes are significantly upregulated or downregulated in the treated group compared to the control.

Quick Start

Run the pydeseq2 analysis script with your count and metadata files.