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.
Reference examples tested with: DESeq2 1.42+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"How many samples do I need for my experiment?" → Estimate required biological replicates per group for a target power level given expected effect sizes and variability.
ssizeRNA::ssizeRNA_single(), DESeq2 pilot dispersion estimatespowsimR::simulateDE()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$ssize # Required n per group
Goal: Derive realistic dispersion estimates from pilot RNA-seq data for use in power and sample size calculations.
Approach: Run DESeq2 on pilot count data to estimate per-gene dispersions, then extract the median dispersion as a representative variability parameter for power formulas.
library(DESeq2)
# From pilot data
dds_pilot <- DESeqDataSetFromMatrix(pilot_counts, colData, ~condition)
dds_pilot <- DESeq(dds_pilot)
# Extract dispersion estimates for power calculation
dispersions <- mcols(dds_pilot)$dispGeneEst
median_disp <- median(dispersions, na.rm = TRUE)
# Use median_disp in power calculations
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)
| 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 |
When resources are limited, prioritize: