Skill · em Dados, IA e pesquisa

pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Procedência

Origem: davila7/claude-code-templates
Caminho: cli-tool/components/skills/scientific/pydeseq2
Versão fixada: 57f899e5394bb8ca166f38eacae8f0853cbfe033
Licença: MIT
Espelhado em 25/09/2026
nenhum download no Claude Code Templates (lido em 25/09/2026)

Antes de instalar

4 arquivos · 47,1 KB · inclui 1 script que executa: scripts/run_deseq2_analysis.py

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/pydeseq2/.

d=".claude/skills/pydeseq2"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pydeseq2"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflow_guide.md" "$u/references/workflow_guide.md" \
  -o "$d/scripts/run_deseq2_analysis.py" "$u/scripts/run_deseq2_analysis.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/pydeseq2/.

d="$HOME/.claude/skills/pydeseq2"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pydeseq2"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflow_guide.md" "$u/references/workflow_guide.md" \
  -o "$d/scripts/run_deseq2_analysis.py" "$u/scripts/run_deseq2_analysis.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/pydeseq2/.

d=".agents/skills/pydeseq2"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pydeseq2"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflow_guide.md" "$u/references/workflow_guide.md" \
  -o "$d/scripts/run_deseq2_analysis.py" "$u/scripts/run_deseq2_analysis.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/pydeseq2/.

d="$HOME/.agents/skills/pydeseq2"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pydeseq2"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflow_guide.md" "$u/references/workflow_guide.md" \
  -o "$d/scripts/run_deseq2_analysis.py" "$u/scripts/run_deseq2_analysis.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/pydeseq2/.

d=".agents/skills/pydeseq2"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pydeseq2"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflow_guide.md" "$u/references/workflow_guide.md" \
  -o "$d/scripts/run_deseq2_analysis.py" "$u/scripts/run_deseq2_analysis.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/pydeseq2/.

d="$HOME/.gemini/antigravity-cli/skills/pydeseq2"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pydeseq2"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflow_guide.md" "$u/references/workflow_guide.md" \
  -o "$d/scripts/run_deseq2_analysis.py" "$u/scripts/run_deseq2_analysis.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.pydeseq2

Prévia do SKILL.md

---
name: pydeseq2
description: "Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis."
---

# PyDESeq2

## Overview

PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and …

## When to Use This Skill

This skill should be used when:
- Analyzing bulk RNA-seq count data for differential expression
- Comparing gene expression between experimental conditions (e.g., treated vs control)
- Performing multi-factor designs accounting for batch effects or covariates
- Converting R-based DESeq2 workflows to Python
- Integrating differential expression analysis into Python-based pipelines
- Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2"

## Quick Start Workflow

For users who want to perform a standard differential expression analysis:

```python
import pandas as pd
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

# 1. Load data
counts_df = pd.read_csv("counts.csv", index_col=0).T  # Transpose to samples × genes
metadata = pd.read_csv("metadata.csv", index_col=0)

# 2. Filter low-count genes
genes_to_keep = counts_df.columns[counts_df.sum(axis=0) >= 10]
counts_df = counts_df[genes_to_keep]

# 3. Initialize and fit DESeq2
dds = DeseqDataSet(
…

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