PyDESeq2
Skill Verifiziert AktivDifferential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
To enable users to perform robust differential gene expression analysis on bulk RNA-seq data using a well-documented and executable Python script.
Funktionen
- Differential gene expression analysis with PyDESeq2
- Handles bulk RNA-seq count data
- Supports single-factor and multi-factor designs
- Performs Wald tests and FDR correction
- Generates volcano and MA plots
- Saves results to CSV and pickle formats
Anwendungsfälle
- Analyzing bulk RNA-seq data for differential gene expression
- Comparing gene expression between experimental conditions
- Accounting for batch effects or covariates in analysis
- Integrating differential expression analysis into Python pipelines
Nicht-Ziele
- Performing single-cell RNA-seq analysis
- Analyzing raw sequencing reads (FASTQ files)
- Single-gene statistical tests outside of a DESeq2 framework
- Advanced bioinformatics tasks like variant calling or genome assembly
Workflow
- Load count matrix and metadata
- Filter low-count genes and samples
- Initialize DeseqDataSet with design formula
- Run DESeq2 pipeline for normalization and fitting
- Perform statistical testing with specified contrast
- Optionally apply LFC shrinkage
- Save results and intermediate objects
- Generate plots (optional)
Praktiken
- RNA-seq data analysis
- Statistical modeling
- Differential expression analysis
Voraussetzungen
- Python 3.10+ (3.11+ recommended)
- uv package manager
- pandas
- pydeseq2
- matplotlib (for plots)
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
Scanpy
99Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Gtars
99High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Fit Drift Diffusion Model
100Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
Pysam
99Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
Polars Bio
99High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.
Geniml
99This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.