bio-de-edger-basics
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill edger-basics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # edgeR Basics Differential expression analysis using edgeR's quasi-likelihood framework for RNA-seq count data. ## Required Libraries ```r library(edgeR) library(limma) # For design matrices and voom ``` ## Installation ```r if (!require('BiocManager', quietly = TRUE)) install.packages('BiocManager') BiocManager::install('edgeR') ``` ## 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. ```r # From count matrix # counts: matrix with genes as rows, samples as columns #
- Version Compatibility
- Required Libraries
- Installation
- Creating DGEList Object
- Standard edgeR Workflow (Quasi-Likelihood)
- Filtering Low-Expression Genes
- Normalization Methods
- Design Matrices
- Dispersion Estimation
- Quasi-Likelihood Testing
- Making Contrasts
- Accessing Results
- Result Columns
- Alternative: Exact Test (Classic edgeR)
What does the bio-de-edger-basics skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill edger-basics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.