Agent Skills › Runchuan-BU/BioClaw › differential-expression

differential-expression

GitHub

用于批量转录组差异表达分析，支持计数模型拟合、对比处理、阈值筛选及可视化，生成发表级图表和通路分析就绪的基因列表。

container/skills/differential-expression/SKILL.md Runchuan-BU/BioClaw

Trigger Scenarios

需要分析bulk RNA-seq或类似计数矩阵的差异表达需要生成火山图、MA图或PCA等可视化结果需要进行条件、治疗或基因型比较并导出显著差异基因表

Install

npx skills add Runchuan-BU/BioClaw --skill differential-expression -g -y

More Options

Non-standard path

npx skills add https://github.com/Runchuan-BU/BioClaw/tree/main/container/skills/differential-expression -g -y

Use without installing

npx skills use Runchuan-BU/BioClaw@differential-expression

指定 Agent (Claude Code)

npx skills add Runchuan-BU/BioClaw --skill differential-expression -a claude-code -g -y

安装 repo 全部 skill

npx skills add Runchuan-BU/BioClaw --all -g -y

预览 repo 内 skill

npx skills add Runchuan-BU/BioClaw --list

SKILL.md

Frontmatter

{
    "name": "differential-expression",
    "tool_type": "python",
    "description": "Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE figures.",
    "primary_tool": "PyDESeq2"
}

Differential Expression

Version Compatibility

Reference examples assume:

pydeseq2 0.4+
pandas 2.2+
numpy 1.26+
matplotlib 3.8+

Verify before use:

Python: python -c "import pydeseq2, pandas; print(pydeseq2.__version__, pandas.__version__)"

Overview

Use this skill for count-based DE from bulk RNA-seq or similar count matrices when the user needs:

robust model fitting
explicit contrasts
ranked gene tables
volcano and MA plots
pathway-ready output tables

When To Use This Skill

raw count matrix and sample metadata are available
the task is condition, treatment, or genotype comparison
batch or pairing terms may need explicit modeling

Quick Route

no replicates: do not pretend formal DE is robust
2 replicates per group: possible but conservative interpretation
3 or more replicates per group: standard starting point

Progressive Disclosure

Read technical_reference.md for design formulas, confounding checks, and contrast logic.
Read commands_and_thresholds.md for PyDESeq2 code, recommended filters, and output file conventions.

Prerequisites

Requirement	Recommendation
minimum replicates per group	`>= 2`
preferred replicates per group	`>= 3`
input values	raw integer counts

Expected Inputs

raw count matrix
sample metadata
explicit contrast such as treated vs control

Expected Outputs

results/de_results.tsv
results/de_ranked_genes.tsv
figures/volcano.pdf
figures/ma_plot.pdf
qc/sample_pca.pdf

Starter Pattern

from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata_df,
    design_factors=["condition", "batch"],
)
dds.deseq2()
stats = DeseqStats(dds, contrast=("condition", "treated", "control"))
stats.summary()
res = stats.results_df.sort_values("padj")
res.to_csv("results/de_results.tsv", sep="\t")

Workflow

1. Validate the design

Check:

replicate counts
factor levels
batch balance
paired structure
confounded variables

2. Fit a count-aware model

Use raw counts, not TPM or log-normalized expression, for count-based DE frameworks.

3. Apply explicit filtering and ranking

Common reporting thresholds:

padj < 0.05
abs(log2FoldChange) >= 1

Export both the full table and a thresholded table.

4. Visualize results

At minimum:

sample PCA
volcano plot
MA plot

5. Export pathway-ready artifacts

Produce a ranked gene list sorted by signed effect or Wald statistic for enrichment workflows.

Output Artifacts

results/
├── de_results.tsv
├── de_significant.tsv
└── de_ranked_genes.tsv
figures/
├── sample_pca.pdf
├── volcano.pdf
└── ma_plot.pdf
qc/
└── design_check.tsv

Quality Review

raw counts only for model fitting
no fully confounded batch and condition
outlier samples reviewed before publication claims
all final tables should include baseMean, log2FoldChange, pvalue, and padj