数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-experimental-design-sample-size

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Power analysis and sample size calculation for omics experiments.

OpenClawNanoClaw分析处理复现实验bio-experimental-design-sample-size🧠 bioos extended suitebioos extended bioinformatics suitepower

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

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 <- DESeq数据集FromMatrix(pilot_counts，colData，~condition) dds_pilot <- DESeq(dds_pilot)。

原始文档

SKILL.md 摘录

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 |

适用场景

Use bio-experimental-design-sample-size ，用于 genomics 、 bioinformatics workflows。
Apply bio-experimental-design-sample-size to sequencing，variant，或 omics analysis tasks。