RNA-seq差异表达分析DESeq2教程：PyDESeq2与gseapy富集分析完整流程

tooluniverse-rnaseq-deseq2 by mims-harvard/tooluniverse

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安装命令

npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-rnaseq-deseq2

科研工具生物信息学数据处理

🇨🇳中文介绍

RNA-seq 差异表达分析 (DESeq2)

使用 PyDESeq2 对 RNA-seq 计数数据进行差异表达分析，并通过 ToolUniverse 进行富集分析 (gseapy) 和基因注释。

BixBench 覆盖率 : 已在 15 个计算生物学项目的 53 个 BixBench 问题上验证。

核心原则

数据优先 - 在进行任何分析之前，先加载并验证计数数据和元数据
统计严谨性 - 正确的归一化、离散度估计、多重检验校正
灵活设计 - 单因素、多因素和交互作用设计
阈值意识 - 精确应用用户指定的阈值 (padj, log2FC, baseMean)
可重复性 - 设置随机种子，记录所有参数
问题驱动 - 解析用户实际询问的内容；提取具体答案
富集分析集成 - 在需要时将 DESeq2 结果链接到通路/GO 富集分析

何时使用

需要进行差异表达分析的 RNA-seq 计数矩阵
关于 DESeq2、差异表达基因 (DEGs)、padj、log2FC 的问题
离散度估计或诊断
对差异表达基因进行 GO、KEGG、Reactome 富集分析
特定基因在不同条件间的表达变化
RNA-seq 中的批次效应校正

所需包

import pandas as pd, numpy as np
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats
import gseapy as gp          # enrichment (optional)
from tooluniverse import ToolUniverse  # annotation (optional)

分析流程

步骤 1：解析问题

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步骤 2：加载和验证数据

加载计数数据和元数据，确保样本为行/基因为列，验证整数计数，对齐样本名称，移除零计数基因。参见 data_loading.md。

步骤 2.5：检查元数据 (必需)

列出所有元数据列和水平。将其分类为生物学关注因素与批次/区组因素。构建设计公式时，协变量在前，关注因素在后。参见 design_formula_guide.md。

步骤 3：运行 PyDESeq2

通过 pd.Categorical 设置参考水平，创建 DeseqDataSet，调用 dds.deseq2()，使用对比提取 DeseqStats，运行 Wald 检验，可选择应用 LFC 收缩。参见 pydeseq2_workflow.md。

Python (PyDESeq2) : 所有 DESeq2 分析
ToolUniverse : 仅用于基因注释 (ID 转换、通路背景)
gseapy : 富集分析 (GO/KEGG/Reactome)

步骤 4：过滤结果

应用 padj、log2FC、baseMean 阈值。如果需要，按方向拆分。参见 result_filtering.md。

步骤 5：离散度分析 (如果被问及)

关键列：genewise_dispersions, fitted_dispersions, MAP_dispersions, dispersions。参见 dispersion_analysis.md。

步骤 6：富集分析 (可选)

使用 gseapy 的 enrich() 函数和适当的基因集库。参见 enrichment_analysis.md。

步骤 7：基因注释 (可选)

仅使用 ToolUniverse 进行 ID 转换和基因背景信息获取。参见 output_formatting.md。

模式	类型	关键操作
1	差异表达基因数量	`len(results[(padj<0.05) & (abs(lfc)>0.5)])`
2	基因值	`results.loc['GENE', 'log2FoldChange']`
3	方向	过滤 `log2FoldChange > 0` 或 `< 0`
4	集合操作	`degs_A - degs_B` 用于查找独特的差异表达基因
5	离散度	`(dds.var['genewise_dispersions'] < thr).sum()`

查看 bixbench_examples.md 获取包含示例的全部 10 种模式。

错误	修复方法
无匹配样本	转置计数矩阵；去除空白字符
离散度趋势不收敛	使用 `fit_type='mean'`
未找到对比	检查 `metadata['factor'].unique()`
非整数计数	四舍五入为整数或使用 t 检验
padj 中出现 NaN	独立过滤移除了基因

查看 troubleshooting.md 获取完整的调试指南。

PyDESeq2 与 R DESeq2 : 对于离散度非常低 (<1e-05) 的基因存在数值差异。如需与 R 结果完全一致，请使用 rpy2。
gseapy 与 R clusterProfiler : 结果可能不同。参见 r_clusterprofiler_guide.md。

question_parsing.md - 从问题中提取参数
data_loading.md - 数据加载和验证
design_formula_guide.md - 多因素设计决策树
pydeseq2_workflow.md - 完整的 PyDESeq2 代码示例
result_filtering.md - 高级过滤和提取
dispersion_analysis.md - 离散度诊断
enrichment_analysis.md - GO/KEGG/Reactome 工作流程
output_formatting.md - 正确格式化答案
bixbench_examples.md - 所有 10 种问题模式
troubleshooting.md - 常见问题和调试
r_clusterprofiler_guide.md - 通过 rpy2 使用 R clusterProfiler

format_deseq2_output.py - 输出格式化工具
load_count_matrix.py - 数据加载工具

🇺🇸English

RNA-seq Differential Expression Analysis (DESeq2)

Differential expression analysis of RNA-seq count data using PyDESeq2, with enrichment analysis (gseapy) and gene annotation via ToolUniverse.

BixBench Coverage : Validated on 53 BixBench questions across 15 computational biology projects.

Core Principles

Data-first - Load and validate count data and metadata BEFORE any analysis
Statistical rigor - Proper normalization, dispersion estimation, multiple testing correction
Flexible design - Single-factor, multi-factor, and interaction designs
Threshold awareness - Apply user-specified thresholds exactly (padj, log2FC, baseMean)
Reproducible - Set random seeds, document all parameters
Question-driven - Parse what the user is actually asking; extract the specific answer
Enrichment integration - Chain DESeq2 results into pathway/GO enrichment when requested

When to Use

RNA-seq count matrices needing differential expression analysis
DESeq2, DEGs, padj, log2FC questions
Dispersion estimates or diagnostics
GO, KEGG, Reactome enrichment on DEGs
Specific gene expression changes between conditions
Batch effect correction in RNA-seq

Required Packages

import pandas as pd, numpy as np
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats
import gseapy as gp          # enrichment (optional)
from tooluniverse import ToolUniverse  # annotation (optional)

Analysis Workflow

Step 1: Parse the Question

Extract: data files, thresholds (padj/log2FC/baseMean), design factors, contrast, direction, enrichment type, specific genes. See question_parsing.md.

Step 2: Load & Validate Data

Load counts + metadata, ensure samples-as-rows/genes-as-columns, verify integer counts, align sample names, remove zero-count genes. See data_loading.md.

Step 2.5: Inspect Metadata (REQUIRED)

List ALL metadata columns and levels. Categorize as biological interest vs batch/block. Build design formula with covariates first, factor of interest last. See design_formula_guide.md.

Step 3: Run PyDESeq2

Set reference level via pd.Categorical, create DeseqDataSet, call dds.deseq2(), extract DeseqStats with contrast, run Wald test, optionally apply LFC shrinkage. See pydeseq2_workflow.md.

Tool boundaries :

Python (PyDESeq2) : ALL DESeq2 analysis
ToolUniverse : ONLY gene annotation (ID conversion, pathway context)
gseapy : Enrichment analysis (GO/KEGG/Reactome)

Step 4: Filter Results

Apply padj, log2FC, baseMean thresholds. Split by direction if needed. See result_filtering.md.

Step 5: Dispersion Analysis (if asked)

Key columns: genewise_dispersions, fitted_dispersions, MAP_dispersions, dispersions. See dispersion_analysis.md.

Step 6: Enrichment (optional)

Use gseapy enrich() with appropriate gene set library. See enrichment_analysis.md.

Step 7: Gene Annotation (optional)

Use ToolUniverse for ID conversion and gene context only. See output_formatting.md.

Common Patterns

Pattern	Type	Key Operation
1	DEG count	`len(results[(padj<0.05) & (abs(lfc)>0.5)])`
2	Gene value	`results.loc['GENE', 'log2FoldChange']`
3	Direction	Filter `log2FoldChange > 0` or `< 0`
4	Set ops	`degs_A - degs_B` for unique DEGs
5	Dispersion

See bixbench_examples.md for all 10 patterns with examples.

Error Quick Reference

Error	Fix
No matching samples	Transpose counts; strip whitespace
Dispersion trend no converge	`fit_type='mean'`
Contrast not found	Check `metadata['factor'].unique()`
Non-integer counts	Round to int OR use t-test
NaN in padj	Independent filtering removed genes

See troubleshooting.md for full debugging guide.

Known Limitations

PyDESeq2 vs R DESeq2 : Numerical differences exist for very low dispersion genes (<1e-05). For exact R reproducibility, use rpy2.
gseapy vs R clusterProfiler : Results may differ. See r_clusterprofiler_guide.md.

Reference Files

question_parsing.md - Extract parameters from questions
data_loading.md - Data loading and validation
design_formula_guide.md - Multi-factor design decision tree
pydeseq2_workflow.md - Complete PyDESeq2 code examples
result_filtering.md - Advanced filtering and extraction
dispersion_analysis.md - Dispersion diagnostics
enrichment_analysis.md - GO/KEGG/Reactome workflows
output_formatting.md - Format answers correctly
bixbench_examples.md - All 10 question patterns
troubleshooting.md - Common issues and debugging
r_clusterprofiler_guide.md - R clusterProfiler via rpy2

Utility Scripts

format_deseq2_output.py - Output formatters
load_count_matrix.py - Data loading utilities

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