pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS).
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce 1:1. pwrEWAS is a Bioconductor power-estimation tool; reproduced by running the published pwrEWAS() function (bioconda build 1.14.0 + pwrEWAS.data 1.14.0) on the paper's exact, fully-specified e-cigarette example (Blood adult ref, N=20-260 step40, J=100000, 2500 target DM CpGs, targetDelta=0.02/0.10/0.15/0.20, limma, BH-FDR=0.05, sims=50) on «our HPC» SLURM («job», 10 min, 16 cpus). The three headline numbers of Fig.2 -- subjects needed for 80% power to detect 10/15/20% methylation differences = 220/180/140 -- reproduced EXACTLY (marginal power crosses 0.80 at precisely those grid sample sizes). The secondary '~36%' sentence (P of detecting at least one DM CpG at dBeta=0.02, N=20) reproduced as 50%: this maps to the pwrEWAS 'probTP' metric (P(>=1 true positive)), NOT the return field misleadingly named 'classicalPower'; the value sits right at the detection threshold and is averaged over only 50 sims, so 0.50 vs 0.36 is Monte-Carlo/seed noise (same metric, same conclusion) -> graded partial, not mismatch. Independent supporting check: empirical FDR = 0.047-0.050 at dBeta>=0.10, exactly at the target BH 0.05. No fabrication concern: every reported value is derivable from the shipped tool+data. NOT attempted (per 80/20): Fig.3 (variance vs #sims, a tuning experiment), Table 3 (hardware-dependent runtime benchmark), and tool-vs-tool comparisons in the Discussion (external). GSE92767 is the bundled saliva reference dataset and is not exercised by this blood-tissue example.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 88assessed: 2026-06-14 ⛓ 0fa9ec91ee71
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusThere is an outstanding need for a user-friendly, publicly available tool to comprehensively estimate statistical power for epigenome-wide association studies (EWAS); pwrEWAS addresses this by using a semi-parametric simulation approach to estimate power for two-group DNA methylation comparisons.
- ★ pwrEWAS is a user-friendly tool (R package and Shiny web interface) for comprehensive power estimation in two-group EWAS using Illumina HumanMethylation BeadChip technology. resource
- ★ Power is estimated via a semi-parametric simulation approach that generates DNAm beta-values using CpG-specific means and variances estimated from curated tissue-type-specific reference datasets. method
- ★ Effect sizes (Δβ) are drawn from a truncated normal distribution N(0,τ²), reflecting that CpG-specific methylation differences come from a continuous rather than fixed discrete distribution. method
- ★ pwrEWAS reports marginal power, marginal type I error rate, marginal FDR, false discovery cost (FDC), the distribution of simulated Δβ, and the probability of identifying at least one true positive. method
- ★ pwrEWAS comprises three major steps: data generation, differential methylation analysis, and power evaluation, and allows users to select from several tissue types and statistical methods. method
- Existing power-evaluation methods for EWAS rely on a limited number of single-locus distributions that may yield unrealistic data and lacked accompanying software, a limitation pwrEWAS overcomes. finding
- pwrEWAS allows up- or down-scaling to any number of CpGs (default P=100,000; e.g. P=866,836 for the EPIC array) that the investigator plans to measure. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Illumina Infinium HumanMethylation450 BeadChip DNAm profiling (reference datasets) | Saliva (GSE92767) | none | CpG-specific methylation beta-values; estimated means and variances | Illumina Infinium HumanMethylation450 |
| Illumina Infinium HumanMethylation450 BeadChip DNAm profiling (reference datasets) | Whole blood — Adults (GSE42861), Children (GSE83334), Newborns (GSE82273) | none | CpG-specific methylation beta-values; estimated means and variances | Illumina Infinium HumanMethylation450 |
| Illumina Infinium HumanMethylation450 BeadChip DNAm profiling (reference datasets) | Cord-blood whole blood (GSE69176) and cord-blood PBMC (GSE110128) | none | CpG-specific methylation beta-values; estimated means and variances | Illumina Infinium HumanMethylation450 |
| Illumina Infinium HumanMethylation450 BeadChip DNAm profiling (reference datasets) | Adult PBMC (GSE67170) | none | CpG-specific methylation beta-values; estimated means and variances | Illumina Infinium HumanMethylation450 |
| Illumina Infinium HumanMethylation450 BeadChip DNAm profiling (reference datasets) | Placenta (GSE62733) | none | CpG-specific methylation beta-values; estimated means and variances | Illumina Infinium HumanMethylation450 |
| Illumina Infinium HumanMethylation450 BeadChip DNAm profiling (reference datasets) | Liver (GSE61258) | none | CpG-specific methylation beta-values; estimated means and variances | Illumina Infinium HumanMethylation450 |
| Illumina Infinium HumanMethylation450 BeadChip DNAm profiling (reference datasets) | Colon (GSE77718) and Lymphoma (GSE42372) | none | CpG-specific methylation beta-values; estimated means and variances | Illumina Infinium HumanMethylation450 |
| In silico simulation / power estimation (semi-parametric, beta-distribution) | Simulated two-group EWAS DNAm data derived from tissue-specific reference datasets | imposed Δβ on K of P CpGs (truncated normal N(0,τ²)) | marginal power, type I error, FDR, FDC, probability of ≥1 true positive | — |
- – pwrEWAS estimates statistical power as a function of assumed sample size and effect size(s) for two-group DNAm comparisons.
- – Marginal power is calculated as the proportion of true positives among all truly differentially methylated CpGs.
- – A CpG is considered truly differentially methylated if its absolute difference in mean methylation exceeds the default detection limit (0.01), and 'detected' if its FDR is below the default threshold (0.05).
- count > 450,000 CpGs (HumanMethylation450) and > 850,000 CpGs (EPIC) (CpG dinucleotides interrogated by the two Illumina arrays)
- count P = 100,000 (default); P = 866,836 for EPIC array (number of CpGs sampled/simulated)
- other detection limit default 0.01 (±0.005) (threshold defining truly differentially methylated CpGs)
- other FDR threshold default 0.05 (threshold for a CpG to be 'detected')
- other 99.99th percentile of |Δβ,k| used to tune τ (calibrating truncated normal SD to target maximal methylation difference)
- other differences in methylation ranged 1.25–14.4% (Rakyan et al.), 1–60% (Tsai et al.) (effect-size ranges in prior EWAS power studies cited)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a software/methods paper presenting pwrEWAS, an R/Shiny tool for statistical power estimation in two-group epigenome-wide association studies (EWAS) of DNA methylation. Rather than reporting an experimental study with hypothesis tests, it describes a semi-parametric, simulation-based framework in which DNAm beta-values are generated from beta-distributions using CpG-specific means and variances drawn from curated tissue-specific reference data sets, with differences (Δβ) imposed on a subset of CpGs from a truncated normal distribution. Simulated data sets are then subjected to differential methylation analysis and power is summarized as marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| User-selectable differential methylation analysis methods (the tool offers a choice among several statistical methods for the two-group comparison; specific methods not enumerated in this text) | Step 2 'differential methylation analysis' within each simulated data set, comparing mean methylation between the two comparator groups per CpG | User-specified total sample size split into two groups (N1 and N2); no fixed n stated | stated |
| t-tests (referenced as methods used in prior EWAS power work, e.g. Wang et al., Tsai et al.) | Background discussion of related approaches, not pwrEWAS's own analysis | — | na |
| Wilcoxon rank-sum test (referenced from prior work, Tsai et al.) | Background discussion of related approaches | — | na |
| Logistic regression (referenced from prior work, Rakyan et al.) | Background discussion of related approaches | — | na |
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Methylation β-values are simulated from beta-distributions using CpG-specific means and variances estimated from reference data sets.↳ Could also: Simulation on the M-value (logit-transformed) scale using normal/Gaussian models, as is common in limma-based EWAS pipelines. — M-value modeling can stabilize variance and aligns with linear-model frameworks frequently used in practice; offering both scales would let users match the simulation to their planned analysis pipeline.
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Imposed differences in mean methylation (Δβ) are drawn from a truncated normal distribution, justified by previously observed EWAS differences.↳ Could also: Alternative effect-size distributions (e.g. mixtures, empirical/bootstrapped distributions of observed Δβ, or fixed discrete effect sizes). — Different distributional choices capture different biological scenarios; an empirical or mixture distribution could more directly reflect a specific study's anticipated effects.
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Power is summarized as marginal (average) power, type I error, and FDR across simulated CpGs.↳ Could also: Reporting Monte Carlo uncertainty for these estimates (e.g. standard errors or 95% confidence intervals across the simulated data sets). — Quantifying simulation variability would convey how precisely power is estimated for a given number of simulated data sets and help users choose an adequate number of simulations.
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The tool focuses on two-group comparisons of DNA methylation.↳ Could also: Extension to continuous exposures, multi-group, or covariate-adjusted/regression designs with confounder control. — Many EWAS involve continuous phenotypes or require adjustment for cell composition and batch; supporting regression-based designs would broaden applicability to common study settings.
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Comparator groups are assumed to share identical CpG-specific variances, differing only in mean for differentially methylated CpGs.↳ Could also: Allowing group-specific (heteroscedastic) variances in the simulation. — Permitting unequal variances between groups could better represent settings where disease or exposure alters methylation variability, not just the mean.
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Multiplicity is handled via FDR control with a user-specified target threshold.↳ Could also: Offering family-wise error rate control (e.g. Bonferroni) or alternative FDR estimators as selectable options. — Some confirmatory EWAS designs favor stricter FWER control; providing multiple correction options would let power estimates match the error-control strategy planned for the final analysis.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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A CpG is classified as truly differentially methylated if absolute mean methylation difference exceeds 0.01, and as detected if FDR falls below 0.05.other human simulated 2019×1papers★ This paper is the founder (earliest)
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Marginal power is defined as the proportion of detected true positives among all truly differentially methylated CpGs in an EWAS simulation framework.other human simulated 2019×1papers★ This paper is the founder (earliest)
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pwrEWAS estimates statistical power as a function of sample size and effect size for two-group DNA methylation comparisons using tissue-specific reference distributions.other human simulated 2019×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-31035919 (pwrEWAS)
Paper: pwrEWAS: a user-friendly tool for comprehensive power estimation for EWAS.
BMC Bioinformatics 2019. PMID 31035919 · PMCID PMC6489300 · DOI 10.1186/s12859-019-2804-7.
Code: https://github.com/stefangraw/pwrEWAS (Bioconductor pkg pwrEWAS + data pkg pwrEWAS.data).
Data: GSE92767 is one of 12 bundled reference datasets (saliva); reference data ships
inside the pwrEWAS.data Bioconductor package — no separate GEO download needed.
What pwrEWAS does
Semi-parametric, simulation-based power estimation for two-group EWAS. CpG-specific
means/variances are estimated from a chosen tissue reference dataset; beta-distributed
DNAm is simulated; a target number of DM CpGs gets a delta drawn from a truncated normal;
differential methylation is detected (default limma) with BH-FDR control. Reports
marginal power = TP/(TP+FN) averaged over sims datasets, plus classical power,
marginal type-I error, empirical FDR, FDC.
IN SCOPE (pipeline-derived, reproduced)
The single fully-specified worked example ("e-cigarette study", Results / Fig 2):
- tissueType = "Blood adult"; total sample size 20–260 step 40; NcntPer=0.5
- J = 100000 CpGs; targetDmCpGs = 2500; targetDelta = c(0.02, 0.10, 0.15, 0.20)
- detectionLimit = 0.01; DMmethod = "limma"; FDRcritVal = 0.05; sims = 50
- Pipeline = the
pwrEWAS()function itself (the published tool). - Claims C1–C4 (see claims.tsv): sample size at ~80% marginal power for Δβ=0.10/0.15/0.20, and classical power at n=20 for Δβ=0.02.
OUT OF SCOPE / not attempted (with reason)
- Fig 3 (variance vs # simulated datasets, 5–100 sims × 100 repeats): a tuning/ justification experiment, not a headline result; cheap-ish but 100× repeats — skip per 80/20.
- Table 3 (runtime benchmarking on 6 threads): hardware-dependent wall-clock, not a scientific result; not comparable across machines → out of scope.
- Comparisons to other power tools in the discussion: external, not pipeline output.
- Wet-lab / dataset curation (Table 1 selection): manual, out of scope.
Reproducibility note
Simulation is stochastic (truncated-normal deltas, beta sampling). A fixed set.seed
before the call makes a single run reproducible, but the paper's exact seed is unknown,
so grading is within-tol (do the power curves land on the same grid points / ~0.8
crossings, and is classical power ~0.36). Not byte-exact by construction.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The headline Fig.2 numbers (220/180/140 subjects for 80% power at Δβ=0.10/0.15/0.20) reproduced exactly by re-running the published pwrEWAS() function with the paper's fully-specified e-cigarette parameters on bundled reference data. The only deviation is the secondary ~36%→50% probability (P(≥1 true positive) at Δβ=0.02≈detectionLimit, N=20), a high-variance binary quantity averaged over 50 sims with the paper's seed unknown — pure Monte-Carlo/seed noise on our stochastic-simulation side, same metric and same scientific conclusion. No authors'-side defect and no fabrication concern: every reported value is derivable from the shipped tool+data, and independent FDR control (0.047–0.050 at target 0.05) corroborates the pipeline.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.