Data & ReproBioinformatics & GenomicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-experimental-design-sample-size

Maintainer FreedomIntelligence · Last updated April 1, 2026

Power analysis and sample size calculation for omics experiments.

OpenClawNanoClawAnalysisReproductionbio-experimental-design-sample-size🧠 bioos extended suitebioos extended bioinformatics suitepower

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-experimental-design-sample-size

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

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

result <- ssizeRNA_single(nGenes = 20000, pi0 = 0.9, m = 200, mu = 10, disp = 0.1, fc = 2, fdr = 0.05, power = 0.8) result$ssize # Required n per group.
dds_pilot <- DESeqDataSetFromMatrix(pilot_counts, colData, ~condition) dds_pilot <- DESeq(dds_pilot).

Source Doc

Excerpt From SKILL.md

Extract dispersion estimates for power calculation

dispersions <- mcols(dds_pilot)$dispGeneEst median_disp <- median(dispersions, na.rm = TRUE)

Accounts for dropout and cell-to-cell variability

params <- estimateParam(pilot_sce) power <- simulateDE(params, n1 = 100, n2 = 100, p.DE = 0.1, pLFC = 1)


## Sample Size by Assay Type

| Assay | Min Recommended | For Small Effects |
|-------|-----------------|-------------------|
| Bulk RNA-seq | 3 | 6-12 |
| scRNA-seq | 3 samples, 1000 cells | 6+ samples |
| ATAC-seq | 2 | 4-6 |
| ChIP-seq | 2 | 3-4 |
| Proteomics | 3 | 6-10 |
| Methylation | 4 | 8-12 |

Use cases

Use bio-experimental-design-sample-size for genomics and bioinformatics workflows.
Apply bio-experimental-design-sample-size to sequencing, variant, or omics analysis tasks.