pydeseq2-differential-expression
CommunityAnalyze RNA-seq gene expression differences.
Education & Research#bioinformatics#genomics#differential expression#rna-seq#gene expression#pydeseq2
Authorjaechang-hits
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the identification of differentially expressed genes from bulk RNA-sequencing data, providing statistically robust results and visualizations.
Core Features & Use Cases
- Differential Expression Analysis: Perform statistical tests (Wald test) to find genes that change significantly between experimental conditions.
- Data Normalization & Fitting: Handles library size normalization and fits negative binomial models for accurate expression estimation.
- Visualization: Generates volcano and MA plots for easy interpretation of results.
- Use Case: Identify genes that are significantly upregulated or downregulated in cancer cells compared to normal cells using RNA-seq count data.
Quick Start
Use the pydeseq2-differential-expression skill to analyze your RNA-seq counts and metadata to find differentially expressed genes between 'treated' and 'control' groups.
Dependency Matrix
Required Modules
pydeseq2pandasnumpyscipyscikit-learnanndatamatplotlibseaborn
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: pydeseq2-differential-expression Download link: https://github.com/jaechang-hits/SciAgent-Skills/archive/main.zip#pydeseq2-differential-expression Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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