Bio Experimental Design Sample Size

Version Compatibility

Reference examples tested with: ssizeRNA 1.3+, PROPER 1.34+, powsimR 1.2+ (GitHub), DESeq2 1.42+, edgeR 4.0+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws an error, introspect the installed package and adapt to the actual API. Notes: ssizeRNA provides ssizeRNA_single() (one mean/dispersion for all genes), ssizeRNA_vary() (genes vary), and check.power() (average power and true FDR for a given n); powsimR is GitHub-only with drifting signatures. Verify against the installed help before use.

Sample Size for Genomics Experiments

"How many samples do I need?" -> Find the smallest number of biological replicates per group that achieves a target marginal power at a target FDR, given the dispersion and effect-size distribution expected for the assay — counting biological units, not measurements.

• R: ssizeRNA::ssizeRNA_vary(), ssizeRNA::check.power() — FDR-aware NB sample size; pilot dispersions from DESeq2/edgeR

The Single Most Important Modern Insight -- The Biological Replicate Is the Unit, and n=3 Is a Convention

Sample size is a count of biological replicates — independent experimental units (animals, donors, cultures from independent passages), not measurements. Technical replicates (one library split across lanes, one RNA split into preps) reduce measurement noise but add no degrees of freedom for biological inference; averaging them into their biological unit is correct, and selling "n = 3 samples x 3 technical reps = 9" as biological power is a standard error (Blainey, Krzywinski & Altman 2014 Nat Methods 11:879). The ubiquitous "n=3" is a publication convention, not a calculation: in the 48-vs-48 yeast benchmark, >=6 biological replicates were needed to recover most true DE genes at realistic effect sizes, and below that the choice of DE tool mattered more than at higher n (Schurch 2016 RNA 22:839). Human and primary material, with higher dispersion, need more. For single-cell, the corollary is sharp: population-level DE power is set by the number of donors, not the number of cells, because cells are pseudoreplicates — pseudobulk per donor is the correct unit (Squair 2021 Nat Commun 12:5692; Murphy & Skene 2022 Nat Commun 13:7851).

Algorithmic Taxonomy

Decision Tree by Scenario

FDR-Aware NB Sample Size -- ssizeRNA

Goal: Find the minimum biological replicates per group for a target power at a target FDR, accounting for the proportion of DE genes and the mean-dispersion structure.

Approach: Specify the number of genes, the proportion non-DE (pi0), the mean count and dispersion (ideally from pilot data), the fold change, the target FDR, and the target power; let ssizeRNA_vary search replicate numbers and return the smallest that reaches the target.

library(ssizeRNA)
res <- ssizeRNA_vary(nGenes = 20000, pi0 = 0.95,        # 5% DE
                     mu = 10, disp = 0.2,                # mean count + dispersion (from pilot ideally)
                     fc = 1.5, fdr = 0.05, power = 0.80,
                     maxN = 30)
res$ssize                                                # minimum n per group

# Verify a budget-fixed n: average power and TRUE realized FDR
check.power(nGenes = 20000, pi0 = 0.95, m = 6, mu = 10, disp = 0.2, fc = 1.5, fdr = 0.05, sims = 50)

Pilot Dispersions Drive Honest Sample Size

Goal: Replace a guessed CV with a measured dispersion-mean trend from pilot data.

Approach: Fit dispersions on the pilot with DESeq2 or edgeR, summarize them, and feed them into the simulation-based estimator (PROPER or powsimR) rather than a single-CV closed form.

library(DESeq2)
dds <- DESeqDataSetFromMatrix(pilot_counts, pilot_coldata, ~ condition)
dds <- DESeq(dds)
disp <- dispersions(dds)                                 # per-gene dispersion estimates
summary(disp[is.finite(disp)])                           # feed median/trend to PROPER/powsimR
# A literature CV can be off by ~2x; a pilot dispersion is the defensible input.

Biological vs Technical Replication

Technical replicates estimate measurement variance; biological replicates estimate the variance that generalizes to the population, and only the latter supports inference about the biology. Average or sum technical replicates into their biological unit before any test. "n = 3 samples x 3 technical reps" is n = 3, not n = 9 (Blainey 2014). This is the sample-size face of the experimental-unit principle (see experimental-design/randomization-blocking).

Replicates vs Depth Under a Fixed Budget

Once depth is adequate (roughly >=10-20M mapped reads for bulk RNA-seq DE), additional biological replicates buy more power than additional depth (Liu 2014 Bioinformatics 30:301). Allocate a fixed budget toward more biological units first. scRNA-seq has an analogous rule at the donor level: more donors beat more cells per donor for population DE, with cells per cell type showing diminishing returns past a few hundred (Squair 2021; Murphy-Skene 2022).

Sample Size by Assay (floors under favorable conditions, not targets)

The "minimum" columns are floors that assume low dispersion and large effects; treat them as the smallest defensible n only after a pilot or literature dispersion supports them.

Per-Method Failure Modes