bio-expression-matrix-normalization
Normalize and transform RNA-seq count matrices for differential expression, visualization, and clustering. Covers between-sample (TMM, RLE, upper quartile), within-sample (TPM, FPKM), variance-stabilizing (VST, rlog), and single-cell (scran) methods. Use when choosing or applying normalization to expression data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill normalization --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+, pandas 2.2+, numpy 1.26+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - 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. # Expression Matrix Normalization ## Why Normalization Matters: Composition Bias Simple library-size normalization (dividing by total counts) fails because RNA populations differ between samples. If a treatment massively upregulates a few genes, those genes consume a disproportionate share of sequencing reads, making every other gene appear downregulated even if unchanged. This composition bias is not a theoretical edge case -- it occurs routinely in stress responses, viral infection, and knockout experiments. All robust normalization methods (TMM, RLE/DESeq2, upper quartile) address composition bias by estimating scaling factors from the majority of non-differentially-expres
- Version Compatibility
- Why Normalization Matters: Composition Bias
- Normalization Decision Table
- Between-Sample Normalization
- RLE / Median of Ratios (DESeq2)
- TMM (edgeR)
- Upper Quartile
- Within-Sample Normalization (TPM, FPKM)
- Why FPKM/RPKM Is Problematic
- Computing TPM
- Variance-Stabilizing Transformations
- VST (DESeq2)
- rlog (DESeq2)
- log-CPM (edgeR)
What does the bio-expression-matrix-normalization skill do?
Normalize and transform RNA-seq count matrices for differential expression, visualization, and clustering. Covers between-sample (TMM, RLE, upper quartile), within-sample (TPM, FPKM), variance-stabilizing (VST, rlog), and single-cell (scran) methods. Use when choosing or applying normalization to expression data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill normalization --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.