eVITTA: a web-based visualization and inference toolbox for transcriptome analysis.
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
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
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
eVITTA is a web/Shiny TOOL paper; the only pipeline-derived numbers come from its Figure 2 evaluation case study reanalyzing GEO GSE152075 (430 SARS-CoV-2+ vs 54 uninfected) via the easyGEO module. Read the repo source (easyGEO/server/DE.R @110b4d3e): easyGEO's default DE is limma-voom (edgeR TMM -> voom -> lmFit -> eBayes -> topTable). Ran that exact pipeline on the paper's own author-submitted raw counts on «our HPC», design ~sequencing_batch+positivity (paper's stated 'batch effect adjustment'). Result: the Fig 2D anchor genes reproduce 1:1 within tolerance on logFC and raw p-value -- CNGA4 -0.852/0.162 vs reported -0.84/0.169; GNAL -1.632/0.0135 vs reported -1.65/0.0130 -- and all 7 named upregulation markers (IFIT1/2/3, RSAD2, CXCL9/10/11) come out upregulated as reported. Only padj drifts (CNGA4 0.626 vs 0.488) because BH adjustment depends on the exact tested-gene universe, which eVITTA's interactive filter sets marginally differently. A negative control (no-batch design) lands far from the paper, independently confirming batch adjustment is the correct design and that the reported values are genuinely data-derived (no fabrication signal). NOT attempted (out of scope, 80/20): the easyGSEA GSEA olfactory-transduction enrichment (Fig 2B/2C) -- gene-set DB version + GSEA seed/params unpinned; and all UI/feature-demo figures (Fig 1,3,4) which are not pipeline numbers. Verdict: described well enough to reproduce the DE step; result is a faithful 1:1 on logFC+pval for the pinnable claims. All grades provisional for human audit.
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 82assessed: 2026-06-15 ⛓ ce6c39068bc5
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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
- 2026-06-15
- 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: opusCan a web-based, end-to-end toolbox (eVITTA) streamline transcriptome analysis—covering retrieval/analysis of NCBI GEO studies, functional profiling, and multi-dataset comparison—to capture both broad and granular gene expression changes in human and model organism data?
- ★ eVITTA is a web-based toolbox with three modules (easyGEO, easyGSEA, easyVizR) that analyze, interpret, and compare transcriptome data from published NCBI GEO studies or new experiments resource
- ★ easyGEO systematically extracts and processes NCBI GEO data and performs differential expression analysis using edgeR and limma method
- ★ easyGSEA provides functional profiling supporting both ORA and pre-ranked GSEA with up-to-date, species-specific, combinatorial gene-set databases and rich visualizations method
- ★ easyVizR provides an integrated intersection-analysis workflow to compare and visualize multiple transcriptome datasets method
- ★ In SARS-CoV-2 infected human nasopharyngeal transcriptomes, eVITTA recapitulated an interferon-driven antiviral response and additionally identified downregulation of olfactory signal transducers, consistent with anosmia in COVID-19 finding
- ★ In C. elegans, SAM deficiency (sams-1 RNAi) and sbp-1 RNAi activate innate immune responses, including TLR signaling, in the absence of pathogen infection finding
- ★ eVITTA pipeline is computationally robust, producing strongly correlated GSEA results across studies differing in platform (microarray vs RNA-seq) and upstream processing finding
- eVITTA's interactive, user-friendly interface makes transcriptome analysis accessible to both wet and dry lab biologists with multiple entry/exit points resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (reanalysis, DE + GSEA) | human nasopharyngeal swab samples | SARS-CoV-2 infection vs uninfected | differential gene expression and gene-set enrichment (e.g. olfactory transduction) | NCBI GEO GSE152075; easyGEO (edgeR/limma), easyGSEA |
| microarray (reanalysis, GSEA) | C. elegans whole worms | sams-1 RNAi | gene-set enrichment scores / regulated gene sets | NCBI GEO GSE70692 (dataset A) |
| microarray (reanalysis, GSEA) | C. elegans whole worms | sams-1 RNAi | gene-set enrichment scores / regulated gene sets | NCBI GEO GSE70693 (dataset B) |
| bulk RNA-seq (reanalysis, GSEA) | C. elegans whole worms | sams-1 RNAi | gene-set enrichment scores / regulated gene sets | NCBI GEO GSE121508 (dataset C) |
| microarray (reanalysis, GSEA) | C. elegans whole worms | sbp-1 RNAi | gene-set enrichment scores (immune/TLR signaling) | NCBI GEO GSE70692 (dataset D) |
- ▲ SARS-CoV-2 infection upregulated antiviral factors (IFIT1/2/3/6, RSAD2) and chemokines (CXCL9/10/11) in nasopharynx
- ▼ Olfactory transduction gene sets were downregulated during SARS-CoV-2 infection
- ▼ Olfactory transducer CNGA4 showed reduced expression in infected NP samples logFC = -0.84
- ▼ Olfactory transducer GNAL showed reduced expression in infected NP samples logFC = -1.65
- ▲ Strong correlation of significantly regulated gene sets between sams-1 datasets A vs B and A vs C R2 = 0.95 and 0.87
- ▲ Strong Spearman correlation of overall enrichment profiles between unfiltered sams-1 datasets A vs B and A vs C rho = 0.77 and 0.72
- ▲ Strong immune signature confirmed in both sams-1 and sbp-1 deficiency, including TLR signaling changes
- correlation R2 = 0.95 (GSEA ES correlation between sams-1 datasets A and B (Figure 3B))
- correlation R2 = 0.87 (GSEA ES correlation between sams-1 datasets A and C (Figure 3C))
- correlation rho = 0.77; pval < 2.2e-16 (Spearman rank correlation, unfiltered datasets A vs B (Figure 3D))
- correlation rho = 0.72; pval < 2.2e-16 (Spearman rank correlation, unfiltered datasets A vs C (Figure 3E))
- fold_change logFC = -0.84; pval = 1.69E-01; padj = 4.88E-01 (CNGA4 expression in SARS-CoV-2 infected NP samples)
- fold_change logFC = -1.65; pval = 1.30E-02; padj = 3.2E-01 (GNAL expression in SARS-CoV-2 infected NP samples)
- count 430 infected / 54 uninfected (GSE152075 nasopharyngeal swab samples reanalyzed)
- count 144751 data series, 4.2 million samples (size of NCBI GEO repository as of 23 February 2021)
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 bioinformatics web-server (tools) paper rather than a hypothesis-testing study; its statistical content lies in the analytical pipelines it implements and in two re-analysis evaluation studies of published transcriptome datasets. Differential expression is performed with edgeR (TMM normalization) and the limma-voom/lmFit/eBayes framework, functional profiling uses overrepresentation analysis (ORA) and pre-ranked Gene Set Enrichment Analysis (GSEA via fgsea), and multi-dataset comparisons report intersections plus correlation metrics. Results are reported as log2 fold changes with P-values and adjusted P-values (FDR), and concordance between datasets is summarized with R-squared and Spearman correlation.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma linear model (weighted least squares lmFit) with eBayes empirical Bayes moderated statistics for differential expression | easyGEO DE analysis; SARS-CoV-2 NP swab DE (GSE152075) and C. elegans datasets | 430 SARS-CoV-2-infected vs 54 uninfected controls (GSE152075); per-gene replicates per dataset otherwise not enumerated | not stated |
| edgeR TMM normalization (library-size adjustment prior to limma-voom) | RNA-seq datasets in easyGEO pipeline | — | not stated |
| pre-ranked Gene Set Enrichment Analysis (GSEA via fgsea, permutation-based) | easyGSEA functional profiling; olfactory transduction gene sets in SARS-CoV-2 data; C. elegans sams-1/sbp-1 comparisons | permutation = 1000; min GS size = 15, max GS size = 200 | na |
| overrepresentation analysis (ORA, hypergeometric-style) via gprofiler2 | easyGSEA ORA module on gene lists | — | na |
| coefficient of determination (R-squared) for enrichment-score concordance | Figure 3B (datasets A vs B, n = 320, R2 = 0.95) and 3C (A vs C, n = 359, R2 = 0.87) | n = 320; n = 359 (gene sets significant in both datasets) | na |
| Spearman rank correlation | Figure 3D (A vs B, n = 912, rho = 0.77, pval < 2.2e-16) and 3E (A vs C, n = 961, rho = 0.72, pval < 2.2e-16) | n = 912; n = 961 (ranked gene sets) | na |
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RNA-seq differential expression is performed via the limma-voom/eBayes workflow after edgeR TMM normalization.↳ Could also: A count-based negative-binomial framework such as DESeq2 or edgeR's quasi-likelihood (glmQLFit) test could also be applied to the same count data. — Negative-binomial GLM approaches model count-level mean-variance directly and are a widely used alternative; offering or comparing both can help users see how method choice affects gene-level calls.
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Dataset concordance is summarized with R-squared and Spearman's rho.↳ Could also: Reporting these correlation estimates with 95% confidence intervals (e.g. via bootstrap or Fisher transformation) could also be done. — Confidence intervals convey the precision of a correlation in addition to its point estimate, which complements the n and P-value already shown.
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Significance filtering uses combined raw-P and adjusted-P thresholds that vary by figure (e.g. padj < 0.25, padj < 0.3, padj < 0.25).↳ Could also: A single pre-specified FDR cutoff applied uniformly across comparisons could also be used. — A fixed threshold makes the family-wise/false-discovery control consistent across analyses and can make cross-figure comparisons easier to interpret.
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The FDR/adjusted-P method is referenced generically as 'padj' without naming the procedure in the main text.↳ Could also: Explicitly stating the correction method (e.g. Benjamini-Hochberg) and its scope alongside each test could also be provided. — Naming the multiplicity method makes the analysis fully reproducible and helps readers understand exactly which family of tests the correction covered.
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Pre-ranked GSEA is run with default permutation = 1000 and gene-set size bounds of 15-200.↳ Could also: Sensitivity analyses across additional permutation counts or alternative gene-set size limits could also be reported. — Showing robustness to these parameters illustrates how stable enrichment calls are, which is useful documentation for a general-purpose tool.
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Gene-level expression is displayed using box and violin plots without a stated central-tendency/dispersion summary statistic.↳ Could also: Accompanying these plots with mean or median plus SD, IQR, or a 95% CI could also be reported. — An explicit dispersion summary conveys spread numerically and can be helpful for small-n groups in addition to the distributional plot.
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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sams-1 and sbp-1 deficiency activates an immune signature including TLR signaling in C. elegans.microarray c. elegans whole worm up 2021×1papers★ This paper is the founder (earliest)
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Olfactory transducer CNGA4 is downregulated in SARS-CoV-2-infected nasopharyngeal samples.RNA-seq human nasopharynx down 2021×1papers★ This paper is the founder (earliest)
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Olfactory transducer GNAL is downregulated in SARS-CoV-2-infected nasopharyngeal samples.RNA-seq human nasopharynx down 2021×1papers★ This paper is the founder (earliest)
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SARS-CoV-2 infection upregulates antiviral interferon-stimulated genes (IFIT1/2/3, RSAD2) and chemokines (CXCL9/10/11) in nasopharynx.RNA-seq human nasopharynx up 2021×1papers★ This paper is the founder (earliest)
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Olfactory transduction gene sets are downregulated during SARS-CoV-2 infection in nasopharyngeal samples.RNA-seq human nasopharynx down 2021×1papers★ This paper is the founder (earliest)
Citation network
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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-34019643 (eVITTA)
Paper: Cheng et al. 2021, eVITTA: a web-based visualization and inference toolbox for transcriptome analysis, Nucleic Acids Res, PMID 34019643 / PMC8218201. Tool type: web application (R/Shiny). Three modules: easyGEO (GEO retrieval + DE), easyGSEA (gene-set enrichment), easyVizR (multi-list comparison).
Nature of the paper
This is a software/tool paper. Most figures (Fig 1, 3, 4, UI panels) are demonstrations of the web interface and are out of scope (manual/UI, no pinnable pipeline number). The one section that produces concrete, pipeline-derived numbers from public data is the Figure 2 evaluation case study reanalyzing GEO GSE152075 (SARS-CoV-2 nasopharyngeal RNA-seq, 430 infected vs 54 uninfected).
In scope (pipeline-derived, attempted)
The easyGEO differential-expression step of the Fig 2 case study. From eVITTA
source (easyGEO/server/DE.R, commit 110b4d3e) the default DE method is
limma-voom: edgeR DGEList → filter_genes (≥1 CPM in min-group-size samples)
→ calcNormFactors (TMM) → voom → lmFit → eBayes → topTable, with output
columns renamed logFC / pval (P.Value) / padj (adj.P.Val). Paper note confirms
"edgeR and limma-voom normalization with batch effect adjustment".
Pinnable claims (Fig 2D, exact):
- CNGA4: logFC = -0.84, pval = 1.69E-01, padj = 4.88E-01
- GNAL: logFC = -1.65, pval = 1.30E-02, padj = 3.2E-01
Pinnable claims (Fig 2A / Results, qualitative-directional):
- Up in infected: IFIT1/2/3/6, RSAD2, CXCL9/10/11 (interferon antiviral response)
- Fig 2A: 15 most up / 15 most down genes by logFC (volcano)
Out of scope (not attempted, why)
- easyGSEA GSEA olfactory-transduction enrichment (Fig 2B/2C, Suppl Table S3): depends on a specific gene-set DB version + GSEA seed/parameters not pinned in the paper; secondary to the DE step. Optional last-20% — skipped per 80/20 rule.
- All UI/feature-demo figures (Fig 1, 3, 4): not pipeline numbers.
Reproduction approach
Run the underlying pipeline (limma-voom) that eVITTA's easyGEO wraps, on the
paper's own data (GSE152075 author-submitted raw counts + series metadata),
matching eVITTA's defaults and the paper's "batch adjustment" design
(~ sequencing_batch + positivity). Compare regenerated logFC/pval/padj for
CNGA4 & GNAL and the up-regulated marker directions to the Fig 2 claims.
Per brief P16, running this third-party pipeline on the paper's data is a valid
reproduction.
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.
This is a tool paper; the only pipeline-derived numbers are the Fig 2 GSE152075 SARS-CoV-2 DE case study, and they reproduce faithfully: CNGA4 (-0.852/0.162 vs -0.84/0.169) and GNAL (-1.632/0.0135 vs -1.65/0.0130) match within ~1-4% on logFC and raw p-value, and all 7 antiviral/chemokine markers come out upregulated as reported. The only deviation is in adjusted p-values (CNGA4 0.626 vs 0.488), which is on our side — a marginally different tested-gene universe in the BH correction from eVITTA's unpinned interactive filter — not an authors' defect. The data is public and identical, a negative control confirms the batch-adjusted design and rules out fabrication, and the central conclusion holds. The GSEA enrichment (C8) was left out of scope, so overall this is a solid near-1:1 reproduction.
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Reproduction footprint
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