FridrichMethod 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