Agent skill
bio-experimental-design-sample-size
Estimates required sample sizes for differential expression, ChIP-seq, methylation, and proteomics studies. Use when budgeting experiments, writing grant proposals, or determining minimum replicates needed to achieve statistical significance for expected effect sizes.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/sample-size
SKILL.md
Sample Size Estimation
RNA-seq Sample Size
library(ssizeRNA)
# Estimate sample size for RNA-seq
# m = total genes, m1 = expected DE genes
# fc = fold change, fdr = target FDR
result <- ssizeRNA_single(nGenes = 20000, pi0 = 0.9, m = 200,
mu = 10, disp = 0.1, fc = 2,
fdr = 0.05, power = 0.8)
result$ssize # Required n per group
DESeq2-based Estimation
library(DESeq2)
# From pilot data
dds_pilot <- DESeqDataSetFromMatrix(pilot_counts, colData, ~condition)
dds_pilot <- DESeq(dds_pilot)
# Extract dispersion estimates for power calculation
dispersions <- mcols(dds_pilot)$dispGeneEst
median_disp <- median(dispersions, na.rm = TRUE)
# Use median_disp in power calculations
Single-cell Sample Size
library(powsimR)
# Estimate for scRNA-seq
# 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 |
Budget Optimization
When resources are limited, prioritize:
- Biological replicates over technical replicates
- More samples over deeper sequencing (after ~20M reads for RNA-seq)
- Balanced designs (equal n per group)
Related Skills
- experimental-design/power-analysis - Power calculations
- experimental-design/batch-design - Optimal batch assignment
- single-cell/preprocessing - scRNA-seq experimental design
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