Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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: RNASeqPower::rnapower() solves for whichever of or is omitted; PROPER is a multi-step pipeline ( -> -> -> ); powsimR is GitHub-only and its // signatures drift — pin a commit SHA for reproducible work. Verify each against the installed help before relying on argument names.
n
power
RNAseq.SimOptions.2grp
simRNAseq
runSims
comparePower
estimateParam
Setup
simulateDE
Power Analysis for Genomics Experiments
"How many replicates does my sequencing experiment need?" -> Compute the probability of detecting a biologically meaningful effect given replicate number, sequencing depth, and biological variability — modeling counts as negative-binomial and recognizing that power is a per-gene quantity, not one number for the whole transcriptome.
R: RNASeqPower::rnapower() — closed-form NB power/sample size; PROPER, powsimR — simulation from the mean-dispersion trend
The Single Most Important Modern Insight -- Genomics Power Is Per-Gene; Simulate, and Never Report Observed Power
Power in a sequencing experiment is not a single number. It is a per-gene quantity that depends on that gene's mean expression and dispersion, so the honest summary is the marginal (average) power across the expression distribution at a target FDR — the expected discovery rate. A single coefficient of variation plugged into a closed-form formula mis-states power for low- and high-expressed genes alike, because dispersion varies systematically with the mean; the defensible default for count data is simulation from the empirical mean-dispersion trend (PROPER, Wu 2015 Bioinformatics 31:233; powsimR, Vieth 2017 Bioinformatics 33:3486). The second rule is negative: observed (post-hoc) power is information-free. Computed from the effect a study actually estimated, it is a one-to-one function of the p-value and cannot explain a null result (Hoenig & Heisey 2001 Am Stat 55:19). Power is a design-stage quantity, computed for hypothesized effects before data exist. Underpowering does not merely miss true effects — it makes the significant ones overstate magnitude (Type-M) and sometimes reverse sign (Type-S), lowering the chance a significant call is real (Button 2013 Nat Rev Neurosci 14:365; Gelman & Carlin 2014 Perspect Psychol Sci 9:641).
Algorithmic Taxonomy
Approach
Model
Tool
Strength
Fails / costs when
NB closed-form
negative-binomial, single CV/dispersion
RNASeqPower::rnapower
fast; transparent; grant-ready
one CV cannot represent the mean-dispersion trend
Simulation, parametric
NB with mean-dispersion relationship
PROPER
honest marginal power + EDR at target FDR
needs a dispersion model / pilot
Simulation, empirical
resampled from pilot (incl. dropout)
powsimR
bulk AND scRNA-seq; realistic
GitHub-only; heavier; version drift
Gaussian closed-form
t-test / Cohen's d
pwr::pwr.t.test
per-feature ATAC/proteomics after transform
wrong for raw counts; ignores overdispersion
Effect-inflation design analysis
retrodesign for Type-S/Type-M
retrodesign (Gelman)
exposes exaggeration in noisy small-n
needs a plausible true effect
Decision Tree by Scenario
Scenario
Recommended approach
Why
Bulk RNA-seq, pilot data available
PROPER/powsimR simulation from pilot dispersions
matches the real mean-dispersion trend
Bulk RNA-seq, no pilot, quick grant number
rnapower() with a literature CV, stated as approximate
transparent; flag as conservative-to-rough
scRNA-seq cross-condition DE
powsimR on a pseudobulk model; power scales with samples
population power is set by donors, not cells
ATAC/ChIP/methylation per-region
NB simulation (PROPER-style) or pwr after variance-stabilizing
overdispersed counts; per-region power
Proteomics (continuous, log-abundance)
pwr::pwr.t.test per protein with missingness caveat
Gaussian after transform; MNAR matters
Justifying a null result post-hoc
report CI / effect size, NOT observed power
post-hoc power is uninformative (Hoenig-Heisey)
Fixed budget: depth vs replicates
favor replicates past ~10-20M mapped reads
biological variance dominates (Liu 2014)
Clinical-trial endpoint
-> clinical-biostatistics/power-and-sample-size
regulated regime, different machinery
Closed-Form NB Power -- RNASeqPower
Goal: Get a fast, transparent power or replicate number for bulk RNA-seq from depth, biological CV, and fold change.
Approach: Supply per-gene depth, biological coefficient of variation, the fold change to detect, and alpha; supply n to get power, or power to get the required n. Treat the result as a single-gene approximation and sanity-check against simulation.
library(RNASeqPower)# depth = reads/gene; cv = biological coefficient of variation; effect = fold change
rnapower(depth =20, n =5, cv =0.4, effect =2, alpha =0.05)# solves for POWER
rnapower(depth =20, cv =0.4, effect =2, alpha =0.05, power =0.80)# solves for n per group
Simulation-Based Power -- the Honest Default for Counts
Goal: Estimate marginal power and the true realized FDR across the whole expression distribution, accounting for the mean-dispersion trend.
Approach: Build (or fit from pilot) a simulation model of counts with a realistic dispersion-mean relationship and DE-effect distribution, simulate many datasets at each candidate sample size, run the intended DE test, and read the average power at the target FDR.
For bulk RNA-seq differential expression, sequencing depth shows diminishing returns once it is adequate — Liu, Zhou & White 2014 (Bioinformatics 30:301) found the inflection near ~10 million mapped reads in MCF7 (commonly generalized to a 10-20M band) — whereas adding biological replicates improves power across the whole range. Under a fixed budget, allocate to more biological units before more depth. ATAC/ChIP have their own depth floors (library complexity, peak detection), but the principle holds: biological variance, not read count, limits discovery once depth is adequate.
CV / Dispersion Guidelines (estimate from pilot when possible)
These are starting points, not substitutes for a pilot estimate; real dispersion is study-specific and a literature CV can be off by a factor of two (estimate via DESeq2/edgeR estimateDispersions — see experimental-design/sample-size).
Per-Method Failure Modes
Single CV for the whole transcriptome
Trigger: one cv plugged into rnapower() for all genes.
Mechanism: dispersion varies with mean expression; a single CV mis-states low/high-expressed genes.
Symptom: simulation gives materially different power than the closed form.
Fix: simulation-based power (PROPER/powsimR) from the mean-dispersion trend.
Observed (post-hoc) power
Trigger: "non-significant, but observed power was 0.3, so add samples."
Mechanism: observed power is a monotone function of the p-value (Hoenig-Heisey 2001).
Symptom: circular reasoning that adds nothing to the CI.
Fix: report effect size + CI; do prospective power for the next study.
Powering to the expected (or pilot-observed) effect
Trigger: setting the effect to the hoped-for or pilot point estimate.
Mechanism: the pilot estimate is itself noisy; building it in bakes in the winner's curse.
Wu H, Wang C, Wu Z. 2015. PROPER: comprehensive power evaluation for differential expression using RNA-seq. Bioinformatics 31:233-241.
Vieth B, Ziegenhain C, Parekh S, Enard W, Hellmann I. 2017. powsimR: power analysis for bulk and single cell RNA-seq experiments. Bioinformatics 33:3486-3488.
Liu Y, Zhou J, White KP. 2014. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30:301-304.
Schurch NJ, Schofield P, Gierliński M, et al. 2016. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22:839-851.
Hoenig JM, Heisey DM. 2001. The abuse of power: the pervasive fallacy of power calculations for data analysis. Am Stat 55:19-24.
Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, Munafò MR. 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14:365-376.
Gelman A, Carlin J. 2014. Beyond power calculations: assessing Type S (sign) and Type M (magnitude) errors. Perspect Psychol Sci 9:641-651.
Ioannidis JPA. 2005. Why most published research findings are false. PLoS Med 2:e124.
Related Skills
sample-size - The inverse problem: minimum replicates for a target power at a target FDR
randomization-blocking - The experimental unit defines what is replicated; blocking changes error variance
batch-design - Account for batch/blocking factors in the power model
differential-expression/deseq2-basics - Estimating dispersions from pilot data for the power model
single-cell/preprocessing - Pseudobulk model underlying scRNA-seq power
clinical-biostatistics/power-and-sample-size - Power for regulated clinical-trial endpoints
