| name | deseq2-differential-expression |
| description | DESeq2 differential expression analysis skill with normalization, statistical modeling, and visualization |
| allowed-tools | ["Read","Write","Glob","Grep","Edit","WebFetch","WebSearch","Bash"] |
| metadata | {"version":"1.0","category":"bioinformatics","tags":["transcriptomics","differential-expression","statistics","rna-seq"]} |
| graph | {"domains":["domain:bioinformatics"],"specializations":["specialization:biomedical-informatics"],"skillAreas":["skill-area:statistical-analysis","skill-area:python-data-pipelines","skill-area:data-analysis"],"workflows":["workflow:experiment-design"],"roles":["role:research-scientist","role:biomedical-engineer"]} |
DESeq2 Differential Expression Skill
Purpose
Provide DESeq2 differential expression analysis with normalization, statistical modeling, and visualization.
Capabilities
- Size factor normalization
- Negative binomial modeling
- Shrinkage estimation
- Batch effect modeling
- Multi-factor designs
- Result visualization (MA plots, volcano plots)
Usage Guidelines
- Design experiments with appropriate replication
- Include batch effects in model when present
- Apply appropriate shrinkage estimators
- Use multiple testing correction
- Generate publication-quality visualizations
- Document analysis parameters and thresholds
Dependencies
Process Integration
- RNA-seq Differential Expression Analysis (rnaseq-differential-expression)
- Single-Cell RNA-seq Analysis (scrnaseq-analysis)
- CRISPR Screen Analysis (crispr-screen-analysis)