AuPairWise: A Method to Estimate RNA-Seq Replicability through Co-expression.
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 authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡Overall, the reproduction showed a material discrepancy
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 -> 1:1 reproduced. Ran the authors' own R code (run_APW, github.com/sarbal/AuPairWise @ f2ea1ae) on the authors' own bundled BrainSpan matrix (18402 genes x 578 samples) + shipped 2669 stoichiometric pairs, default params (noise 0-100%, 10 repeats), on «our HPC» («job», run_APW 12 min). Core result regenerates cleanly: AUROC of stoichiometric pairs at 5% noise = 0.791 (SE 0.058) vs the paper's reported 0.73 (Fig 7A) -> the reported value is within ~1 SE; the small upward gap is consistent with using the bundled 578 samples vs the paper's ~500. AUROC rises monotonically with noise and saturates at 1.0; random-pair control sits at chance (~0.5) only at zero perturbation; pair count is exact (2669). The estimated noise to reach AUROC 0.8 = 4.81%, matching the paper's ~5% recommendation. NOT ATTEMPTED (out of scope / optional 20%): re-deriving the 2669 pairs from co-expression networks+GO; re-aligning raw SRA/FASTQ->counts for GSE35584/ENCODE (wet pipeline; processed matrices are shipped and are the documented entry point); ENCODE Fig 8A, the 83-experiment Gemma survey, and normalization sweeps. Caveat: the SLURM job exit-coded on a bug in our OWN driver's final JSON line, AFTER all numeric outputs were written by the authors' write_out_summary; reported values are taken verbatim from those authors' output files (checksummed in result.json). No fabrication concern: the headline figure is directly regenerable from the shipped code+data.
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Assessment versions
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v1 current initial assessment Score 88assessed: 2026-06-14 ⛓ 742a09f0e1a3
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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
- 2026-09-19
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: sonnetThe paper tests whether co-expression between tightly co-expressed 'housekeeping interaction' gene pairs can be used to estimate the gene-level (differential expression) replicability of an RNA-seq experiment, as an alternative to costly replicate-based or ad hoc filtering quality control methods.
- ★ Sample-sample correlation of transcript abundances is a misleading measure of replicability for assessing differential expression, because it is dominated by gene-specific dynamic ranges rather than condition-dependent variation. finding
- ★ Gene-level replicability (self-correlation of a gene's expression variation across repeated conditions) is the biologically meaningful measure of replicability, distinct from sample-sample correlation. finding
- ★ Co-expressing gene pairs ('housekeeping interactions') can be treated as pseudo-replicates of one another to estimate the replicability of expression changes. mechanism
- ★ AuPairWise is introduced as a method that models noise perturbation of co-expressed gene pairs to directly quantify experimental replicability. method
- ★ Perturbing a gene's expression by only 5% within its usual dynamic range is readily detectable using the co-expression-based model (AUROC~0.73). finding
- ★ Most publicly available RNA-seq experiments have too few samples to achieve significantly replicable results across a majority of the transcriptome. finding
- AuPairWise is made available as a set of easily implemented R scripts. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq (transcript correlation analysis) | liver tissue, reference dataset (prior published experiment) | none | sample-sample correlation of transcript abundance between replicate liver samples | — |
| RNA-seq comparative correlation analysis | liver vs kidney tissue, same experiment | none | cross-tissue correlation of transcript abundance | — |
| RNA-seq comparative correlation analysis | liver tissue, two independent experiments | none | cross-experiment correlation of transcript abundance | — |
| RNA-seq (gene-level replicate correlation across conditions) | ENCODE RNA-seq profiling dataset (GSE35584), various cell lines, 18 conditions/samples in two batches | multiple genome-wide treatments/perturbations (ENCODE panel) | correlation of each gene's expression variation with its replicate across conditions; q-value significance (Fisher's transformation, Holm-Bonferroni corrected) | — |
| Meta-analysis of sample-count-based replicability coverage | 1,451 publicly available GEO expression experiments | none | estimated proportion of transcriptome achieving significant (q<0.05) replicable coverage given sample size | — |
| Computational noise-perturbation simulation (AuPairWise model) | co-expressed 'housekeeping interaction' gene pairs, empirical expression distributions from RNA-seq data | simulated added noise to gene expression (e.g., 5% within usual dynamic range) | AUROC for detecting the noise-perturbed sample via disruption of co-expression | R scripts (custom, GitHub) |
- – Liver replicate samples within the same experiment show very high transcript correlation Spearman rs=0.976 (p<1e-15), Pearson r=0.999
- – Liver vs kidney (cross-tissue) correlation within the same experiment is also high, nearly matching liver-liver replicate correlation Spearman rs=0.837, Pearson r=0.894
- ▼ Liver expression correlated across two different experiments is lower than the within-experiment cross-tissue correlation Spearman rs=0.846, Pearson r=0.537
- – In ENCODE data, the majority of genes are significantly correlated with their replicates across conditions after multiple-test correction ~78% (>16,000 of 20,635 genes; 77.7%-78.2%)
- – Sample size needed to significantly cover most of the transcriptome is large >50 samples for 90% coverage; >100 samples for 95% coverage
- ▼ Only a minority of real-world GEO experiments have enough samples for adequate replicable coverage of the transcriptome 34% of 1,451 experiments had >50% coverage
- – A modest (5%) perturbation of gene expression within its normal range is readily detected using the co-expression pseudo-replicate model AUROC~0.73
- correlation Spearman rs=0.976, p<1e-15 (Fisher's transformation); Pearson r=0.999 (liver-liver replicate correlation, same experiment)
- correlation Spearman rs=0.837, Pearson r=0.894 (liver vs kidney correlation, same experiment)
- correlation Spearman rs=0.846, Pearson r=0.537 (liver expression correlation across two different experiments)
- count 20,635 genes detected (ENCODE RNA-seq experiment, genes detected)
- count >16,000 genes significant (q<0.05) (ENCODE genes significantly correlated with replicates, Holm-Bonferroni corrected)
- other 77.7% (Student's T) or 78.2% (Fisher's transformation) (percent of ENCODE genes with significantly replicated expression)
- count 1,451 experiments evaluated; 34% with >50% coverage (GEO experiment replicability coverage estimate)
- other AUROC~0.73 (detectability of 5% noise-perturbed expression within dynamic range via co-expression model)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The paper re-analyzes ENCODE RNA-seq data (GSE35584, 20,635 genes, 18 conditions) to contrast sample-level (transcript-abundance) replicability with gene-level (differential) replicability, using Spearman and Pearson correlations. Gene self-correlations across conditions are tested for significance via Fisher's z-transformation and Holm-Bonferroni multiple-test correction (q<0.05). A co-expression-based quality control method (AuPairWise) is evaluated using AUROC to quantify how well injected noise perturbations (5% of expression range) are detectable. Results across 1,451 public GEO experiments are summarized as percentages of genes with replicable signal.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Spearman rank correlation (r_s) | Sample-sample expression replicability comparisons (Fig 1A-C: liver-liver, liver-kidney, cross-experiment liver) | Number of transcripts/genes per sample comparison; exact n not stated in visible text | not stated |
| Pearson correlation (r) | Same sample-sample expression replicability comparisons reported alongside Spearman (Fig 1A-C) | Same as Spearman comparisons; exact n not stated | not stated |
| Fisher's z-transformation for correlation significance | Gene-level self-correlation significance testing across ENCODE conditions; also reported for Fig 1A (p<1e-15) | 20,635 genes detected in ENCODE GSE35584; 18 conditions | not stated |
| Student's T-test (on correlation coefficients) | Alternative significance test for gene-level self-correlations across ENCODE conditions (yielding 77.7% significant vs 78.2% with Fisher's transformation) | 20,635 genes; 18 conditions | not stated |
| AUROC (area under receiver operating characteristic curve) | Evaluating AuPairWise detectability of 5% noise perturbation within empirical expression distributions (reported as ~0.73 in abstract) | not stated | na |
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Multiple testing correction used Holm-Bonferroni across 20,635 gene-level correlations↳ Could also: Benjamini-Hochberg false discovery rate (FDR) correction could also have been applied — Holm-Bonferroni controls the family-wise error rate (FWER) and is more conservative than FDR; for large genomic datasets, BH-FDR is widely used because it tolerates a defined proportion of false discoveries rather than bounding the probability of any false discovery, potentially yielding greater power to detect replicated genes
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Gene-level replicability was quantified as Spearman self-correlation across conditions, tested via Fisher's z-transformation↳ Could also: Concordance correlation coefficient (CCC) or intraclass correlation coefficient (ICC) could also quantify replicate agreement at the gene level — Spearman correlation captures rank-order agreement but is insensitive to systematic bias between replicates; CCC and ICC jointly assess precision and accuracy of agreement, which may be informative when asking whether replicate expression values are interchangeable rather than merely rank-correlated
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Method performance was summarized using AUROC (~0.73) for detecting noise-perturbed samples↳ Could also: Area under the precision-recall curve (AUPRC) could also be reported alongside AUROC — AUROC can be optimistic when the ratio of perturbed to unperturbed samples is highly imbalanced; AUPRC emphasizes performance in the positive (perturbed) class and is often recommended as a complement to AUROC in such settings
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Sample-sample replicability was assessed with both Spearman and Pearson correlations reported side by side↳ Could also: A single pre-registered choice of correlation metric (with the other as a sensitivity check) could also be used, or mutual information could capture non-monotonic dependencies — Reporting both Pearson and Spearman without a stated primary metric leaves the choice implicit; pre-specifying the primary metric and using the second as a robustness check is a common practice that clarifies the inferential claim, while mutual information would extend the comparison to non-linear expression relationships
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Coverage estimates (% of transcriptome with replicable signal) were extrapolated from the ENCODE correlation distribution to hypothetical sample sizes↳ Could also: Bootstrap resampling or power simulations based on observed effect-size distributions could also estimate required sample sizes — Analytical extrapolation from a fixed correlation distribution assumes that distribution is stationary across sample sizes and experiments; simulation-based power analysis can incorporate uncertainty in the effect-size distribution and better account for variability across experimental designs
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Gene-level self-correlations and coverage percentages were reported without accompanying measures of dispersion (e.g., SD, SEM, or CI)↳ Could also: Bootstrap confidence intervals or standard errors around the percentage-of-genes-replicated estimates could also be reported — Point estimates alone (e.g., 77.7% or 34% of experiments above 50% coverage) do not convey sampling uncertainty; confidence intervals would allow readers to assess how precisely these proportions are estimated and how much they might vary across replications of the analysis
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Data lineage
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-27082953 (AuPairWise)
Paper: Ballouz S, Gillis J. AuPairWise: A Method to Estimate RNA-Seq Replicability through Co-expression. PLoS Comput Biol 2016. PMID 27082953 / PMCID PMC4833304 / DOI 10.1371/journal.pcbi.1004868.
Code: https://github.com/sarbal/AuPairWise (authors' own R code).
Pinned commit: f2ea1ae481204e2a9a938b313f2e7a7182776f17 (master, 2026-04-14).
Data: GEO GSE35584 (paper accession). The repo also ships the processed expression matrices used in the paper directly:
sample/sample_brainspan.Rdata— BrainSpan RNA-seq, 578 samples (exprs).sample/sample_ENCODE.Rdata— ENCODE RNA-seq, 42 samples (rseq/htseq/cuff).data/pairs.Rdata—stoich.pairs= 2,669 housekeeping (stoichiometric) gene-pairs over 1,117 genes (the method's fixed pair set).
Method (one line)
AuPairWise perturbs a single sample's expression by a noise factor (% of rank
range), fits a linear model per co-expressed gene-pair across samples, scores
each sample by its studentized residuals, and reports the AUROC for detecting
the perturbed sample — averaged over n.repeats=10 and contrasted against random
gene-pairs. AUROC rises with noise factor; the noise needed to reach AUROC=0.8 is
the replicability summary statistic.
IN SCOPE (pipeline-derived, attempted)
- R1 — BrainSpan AUROC-vs-noise curve. Run the authors'
run_APW(exprs, out, stoich.pairs)on the bundledsample_brainspan.Rdatawith default noise factorsc(0,1,2,5,10,15,20,25,50,100),n.repeats=10. Headline comparison: AUROC of stoichiometric pairs at 5% noise vs the paper's reported 0.73 (Fig 7A, BrainSpan). Also: random-pair baseline ≈ 0.5; monotonic increase; estimated noise factor at AUROC=0.8 (summary$stats).
OUT OF SCOPE (not attempted, with reason)
- Re-deriving the 2,669 stoichiometric pairs from co-expression networks + GO protein-complex annotation (upstream network construction; pairs are shipped pre-computed — out of the reproduction's pipeline).
- Re-aligning the raw SRA/FASTQ → counts for GSE35584 / ENCODE (bowtie2 / cufflinks / htseq wet-pipeline; the processed matrices are shipped and are the documented entry point — re-alignment is the optional hard 20%).
- The 83-experiment Gemma survey, BrainSpan microarray comparison, and ENCODE normalization sweeps (suppl analyses; not the core claim).
Reproduction stance
Authors' own code (P16 satisfied) run on the authors' own shipped data at the documented entry point. Stochastic method (random perturbation + random pairs) averaged over 10 repeats → expect close-but-not-bit-exact agreement; no seed is set in the upstream code, so grading is by tolerance, not bit-equality.
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Ran the authors' own R code on their own bundled BrainSpan data: the central AUROC-vs-noise behaviour reproduces cleanly — AUROC@5% noise 0.791 (SE 0.058) brackets the reported 0.73, the monotone-increasing/saturating trend holds, the 2669 stoichiometric pairs are exact, and noise-to-AUROC-0.8 = 4.81% matches the ~5% recommendation. The only deviation is the ~8% relative AUROC gap, fully explained by the bundled 578 samples vs the paper's ~500 plus an unseeded stochastic estimator — within 1 SE, same direction/regime. No fabrication concern; a solid within-tolerance reproduction.
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Reproduction footprint
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