跳到正文
cccskills
免费在 GitHub 公开

differential-expression-analysis

Use when analyzing bulk RNA-seq or microarray expression data to identify differentially expressed genes between two biological groups (case vs control), with volcano plots and heatmap visualization. NOT for:single-cell RNA-seq, methylation analysis, non-expression data.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(13)

  • SKILL.md5.9 KB
  • eval_report_differential-expression-analysis_result.json15.6 KB
  • references/algorithm.md2.0 KB
  • references/cli-guide.md1.7 KB
  • references/troubleshooting.md2.1 KB
  • scripts/diff_methods.R2.9 KB
  • scripts/diff_visualization.R3.3 KB
  • scripts/functions.R2.0 KB
  • scripts/main.R3.2 KB
  • scripts/run_analysis.R2.1 KB
  • scripts/utils.R1.6 KB
  • tests/data/Combined_Datasets_Matrix_mus.csv6.6 MB
  • tests/data/Combined_Datasets_mus_Group.csv618 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Source: https://github.com/aipoch/medical-research-skills

Differential Expression Analysis

When to Read External Files

SituationFile to ReadPurpose
Need algorithm detailsreferences/algorithm.mdStatistical methods, formulas, assumptions
Need to run analysisscripts/main.RExecute: Rscript scripts/main.R --input_file ... --group_file ...
Encounter errorsreferences/troubleshooting.mdCommon errors and solutions
Need CLI examplesreferences/cli-guide.mdDetailed CLI usage examples
Need test datatests/data/Sample input files for testing

Usage

Rscript scripts/main.R \
  --input_file ./expression_matrix.csv \
  --group_file ./group_info.csv \
  --output_dir ./output/ \
  --diff_method limma \
  --p_threshold 0.05 \
  --logfc_threshold 0.1 \
  --seed 42

Arguments

ShortLongTypeDefaultDescription
-i--input_filecharacterrequiredExpression matrix file (genes as rows, samples as columns)
-g--group_filecharacterrequiredGroup information file (sample ID + group columns)
-o--output_dircharacter./output/Output directory
-m--diff_methodcharacterlimmaMethod: limma, deseq2, edger, t, wilcox
-n--norm_methodcharacterTMMNormalization for edgeR: TMM, RLE, upperquartile
-p--p_thresholdnumeric0.05P-value threshold
-f--logfc_thresholdnumeric0.1Log fold change threshold
-s--seedinteger42Random seed for reproducibility

Input Format

Expression Matrix (input_file)

Genes as rows, samples as columns, CSV format with gene ID in first column.

"","GSM1442228","GSM1442229","GSM1442230"
"0610006L08Rik",3.438,3.237,3.265
"0610007P14Rik",6.734,7.017,6.807

Group File (group_file)

CSV with sample ID and group columns.

"ID","group"
"GSM1442228","Control"
"GSM1442229","Control"
"GSM1442230","DIC"

Output Files

FileDescription
Diffanalysis.csvComplete DE results with gene_id, logFC, Pvalue, Padj
volcano_plot.pdfVolcano plot with significance thresholds
heatmap.pdfHeatmap of top upregulated/downregulated genes
session_info.txtR session and package version info
temp/rdegs.csvSignificant differentially expressed genes
temp/Diffanalysis_filtered.csvFull results with group annotations

Workflow

Step 1: Validate Input

  • Check file existence
  • Validate sample matching between expression matrix and group file
  • Verify at least 2 samples per group

Step 2: Run Differential Expression

  • Choose method: limma, DESeq2, edgeR, t-test, or Wilcoxon
  • Calculate logFC and p-values
  • Apply multiple testing correction (Benjamini-Hochberg)

Step 3: Filter Results

  • Filter by p-value and logFC thresholds
  • Classify genes as Up, Down, or Not significant

Step 4: Generate Visualizations

  • Volcano plot showing significance vs fold change
  • Heatmap of top differential genes

Methods

limma

Linear models for microarray and RNA-seq with empirical Bayes moderation. Recommended for normalized expression data (FPKM, TPM).

DESeq2

Negative binomial GLM with variance stabilization. Recommended for raw count data.

edgeR

Empirical Bayes methods with TMM normalization. Supports robust dispersion estimation.

t-test / Wilcoxon

Simple pairwise statistical tests. t-test for parametric, Wilcoxon for non-parametric.


Examples

Basic Usage (limma)

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g group_info.csv \
  -o ./output \
  -m limma

With DESeq2 for Count Data

Rscript scripts/main.R \
  -i count_matrix.csv \
  -g group_info.csv \
  -o ./output \
  -m deseq2

Custom Thresholds

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g group_info.csv \
  -o ./output \
  -p 0.01 \
  -f 0.5

Error Handling

Common Errors

ErrorCauseSolution
SKILL_FILE_NOT_FOUNDInput file doesn't existCheck file path
SKILL_SAMPLE_MISMATCHSample names don't matchVerify group file matches expression matrix columns
SKILL_INVALID_DATALess than 2 groups or samples per groupCheck group file
SKILL_FILTER_ERRORNo significant genes foundRelax thresholds or check data quality
SKILL_DEPENDENCY_MISSINGR package not installedInstall required packages

IF error persists, READ: references/troubleshooting.md


Testing

Test with Sample Data

# Check help
Rscript scripts/main.R --help

# Run with sample data
Rscript scripts/main.R \
  -i tests/data/Combined_Datasets_Matrix_mus.csv \
  -g tests/data/Combined_Datasets_mus_Group.csv \
  -o tests/output/

Validation Commands

# Count lines in output
wc -l output/Diffanalysis.csv

# Check volcano plot exists
ls -la output/volcano_plot.pdf

Implementation Checklist

  • CLI parsing with optparse
  • set.seed() for reproducibility
  • requireNamespace() dependency checks
  • Session info recording
  • Temp file cleanup
  • File reading instructions in SKILL.md
  • Modular script structure (<100 lines per file)
  • Test data provided
  • Error handling with SKILL_* codes
  • Scripts in scripts/ directory
  • References in references/ directory

Last updated: 2026-04-01 | Version: 2.0.0

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Transform lengthy academic papers into concise, structured 250-word abstracts.

中文简介正在准备中。当前显示原文说明。

aipoch/medical-research-skills19892026年9月17日 更新

Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.

中文简介正在准备中。当前显示原文说明。

aipoch/medical-research-skills19892026年9月17日 更新

Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.

中文简介正在准备中。当前显示原文说明。

aipoch/medical-research-skills19892026年9月17日 更新

Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.

中文简介正在准备中。当前显示原文说明。

aipoch/medical-research-skills19892026年9月17日 更新

Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.

中文简介正在准备中。当前显示原文说明。

aipoch/medical-research-skills19892026年9月17日 更新

Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

中文简介正在准备中。当前显示原文说明。

aipoch/medical-research-skills19892026年9月17日 更新

查看 aipoch 的全部技能

报告此技能的问题