name: bio-experimental-design-sample-size description: 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. tool_type: r primary_tool: ssizeRNA measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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

Budget Optimization

When resources are limited, prioritize:

1. Biological replicates over technical replicates
2. More samples over deeper sequencing (after ~20M reads for RNA-seq)
3. 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