Data & ReproStatistics & Data AnalysisFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-de-edger-basics

Maintainer FreedomIntelligence · Last updated April 1, 2026

Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.

OpenClawNanoClawAnalysisReproductionbio-de-edger-basics🧬 bioinformatics (gptomics bio-* suite)bioinformatics — differential expression & transcriptomicsperform

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-de-edger-basics

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Differential expression analysis using edgeR's quasi-likelihood framework for RNA-seq count data.
y <- DGEList(counts = counts, group = group).

Source Doc

Excerpt From SKILL.md

Creating DGEList Object

Goal: Construct an edgeR container from a count matrix with sample group information.

Approach: Wrap raw counts and group labels into a DGEList object for normalization and testing.

"Load my RNA-seq counts into edgeR" → Create a DGEList from a count matrix with sample group assignments and optional gene annotations.


## With gene annotation

y <- DGEList(counts = counts, group = group, genes = gene_info)

## Standard edgeR Workflow (Quasi-Likelihood)

**Goal:** Run the complete edgeR QL pipeline from raw counts to differentially expressed gene lists.

**Approach:** Filter, normalize (TMM), estimate dispersions, fit quasi-likelihood GLM, and test coefficients with the QL F-test.

**"Find differentially expressed genes between my groups"** → Test for significant expression differences using negative binomial models with quasi-likelihood F-tests.

```r

Use cases

Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing.
Use when performing DE analysis with edgeR.