Empowering integrative and collaborative exploration of single-cell and spatial multimodal data with SGS genome browser.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓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
- 🔴Reported values were only indirectly comparable
▸Reproduction agent’s raw note
INTERIM (will refine). SGS (Cell Genomics 2025, 5(5):100848) is a genome-browser VISUALIZATION tool, not an analysis pipeline. Per BRIEF Rule 2 (a third-party tool on the paper's own data is a valid reproduction) we reproduce the one pipeline-derivable displayed claim: 'RARG shows higher chromatin accessibility in cluster 4' (Fig 5E-F) on the ME11 mouse-embryo spatial-ATAC-seq sample (GSE171943 / GSM5238385_ME11_50um) using snapATAC2 2.7.0 on «our HPC» SLURM. Prior run («job») COMPLETED and found 9 spatially-coherent Leiden clusters with Rarg highest in cluster '4' (0.591 vs 0.370; rank-0 Wilcoxon marker, padj 1.6e-21). Graded 'partial' honestly (paper gives no number; Leiden labels non-deterministic). Re-run in flight for an in-session determinism confirmation.
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 assessmentassessed: 2026-06-20 ⛓ 1ad57719c91f
✎ 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
- 2026-06-23
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-20no 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: sonnetExisting single-cell and spatial multimodal visualization tools are limited to specific modalities, lack robust comparative and collaborative capabilities, and cannot adequately handle complex epigenomic multimodal data or 3D spatial transcriptomics, motivating the development of a new unified browser (SGS) to address these gaps.
- ★ SGS is a user-friendly, collaborative, versatile browser for integrative visualization of single-cell and spatial multimodal (scMulti-omics) data resource
- ★ SGS introduces a novel, flexible genome browser framework with dual-chromosome mode and multi-panel adaptive communication for coordinated visualization of epigenomic multimodal data method
- ★ SGS provides interactive 3D spatially resolved transcriptomics (SRT) visualization using surface model plots, exceeding capabilities of existing tools like Vitessce resource
- ★ SGS offers comparative visualization tools (scCompare, scMultiView, dual-chromosome mode) for cross-modal, cross-sample, and cross-region comparisons method
- ★ SGS supports diverse data formats (AnnData, MuData, Zarr, GFF, VCF, BED, HiC, Biginteract, Longrange, MethylC, GWAS, Bedgraph) and is compatible with Seurat, ArchR, Signac, and Giotto via the SgsAnnData R package resource
- ★ SGS enables graphical, no-code installation and operation (via Docker and Flutter) across Linux, Windows, and MacOS, in contrast to programming-dependent tools like Vitessce resource
- ★ SGS supports multi-user real-time collaboration including co-annotation, commenting, session/URL sharing, and project/user management resource
- ★ In human PFC sn-m3C-seq data, the adult PFC L4-5 FOXP2 cell population shows enhanced chromatin interaction strength at the RORB locus accompanied by decreased CG methylation compared to other cell populations finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| sn-m3C-seq (DNA methylation + 3D chromatin conformation) | human dorsal prefrontal cortex (PFC, 13 developmental/adult samples) and hippocampus (HPC, 9 samples) | none | CG methylation signal and Hi-C chromatin interaction strength at RORB locus across cell types | SGS (SG visualization mode) |
| scATAC-seq | human hematopoietic cells | none | chromatin accessibility, gene CRE links, VSTM1 gene structure and activity score | SGS (SG visualization mode) |
| spatial transcriptomics (10x Genomics Visium) | mouse brain | none | spatial gene expression distribution across tissue slices | 10x Genomics Visium; SGS SC mode |
| single-cell eQTL (sc-eQTL) | human (OneK1K cohort) | none | eQTL loci visualization | SGS (SG visualization mode) |
| spatial-ATAC-seq | mouse tissue | none | spatial chromatin accessibility signals | SGS (SG visualization mode) |
| 3D spatially resolved transcriptomics (SRT) | Drosophila | none | 3D gene expression heterogeneity via surface model plots | SGS (SC mode, 3D visualization) |
- ▲ Adult PFC L4-5 FOXP2 cell population shows noticeably enhanced chromatin interaction strength specifically in the RORB region
- ▼ The enhanced chromatin interaction in PFC L4-5 FOXP2 cells is accompanied by decreased CG methylation signal compared to other cell populations
- ▼ Decreased CG methylation is observed especially in excitatory neurons within the PFC L4-5 FOXP2 cell cluster, consistent with previous findings
- – SGS demonstrates core advantages over Vitessce in visualization capabilities, interactivity, view coordination, multi-user collaboration, and user-friendliness
- count 13 developmental adult PFC samples and 9 HPC samples (sn-m3C-seq case study dataset composition)
- count 10 primary cell types (cell types identified in the sn-m3C-seq PFC/HPC cell atlas)
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.
This is a software/methods paper introducing SGS, a graphical browser for visualizing single-cell and spatial multimodal omics data. The paper describes tool architecture, features, and demonstrates them on previously published datasets (e.g., human PFC/HPC sn-m3C-seq, mouse Visium, OneK1K sc-eQTL, spatial-ATAC-seq, Drosophila 3D SRT); it does not report new hypothesis tests, experimental comparisons, or statistical analyses performed by the authors on new data.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| not named (a p value is displayed in the marker feature table) | Figure 2E, marker gene table in SC mode | — | not stated |
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The marker gene table (Figure 2E) displays a p value without the paper stating which statistical test generated it or whether multiple-testing correction was applied.↳ Could also: Explicitly naming the test (e.g., Wilcoxon rank-sum, as commonly used in Seurat's FindMarkers) and reporting an adjusted p value (e.g., Benjamini-Hochberg FDR) alongside the raw p value — Naming the test and showing both raw and adjusted p values would let users of the browser interpret the displayed marker significance in the context of how many features were tested.
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The paper demonstrates SGS using previously published datasets and does not report new inferential statistics comparing conditions or cell populations itself.↳ Could also: If quantitative comparisons between cell types or conditions were to be added to the tool's outputs, a mixed-effects or pseudobulk-based approach (e.g., DESeq2/edgeR on pseudobulk samples) is often used in single-cell studies to account for biological replicate structure — Pseudobulk or mixed-effects methods can better reflect biological replication (as opposed to treating individual cells as independent units), which is a common consideration when comparing single-cell-derived groups.
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The paper does not describe dispersion or variability measures (e.g., SD, SEM, CI) for any summarized data shown in the visualization panels.↳ Could also: Displaying a chosen dispersion measure (e.g., SD or a 95% CI) alongside summary plots such as violin or dot plots — Showing a dispersion metric can help end users of the browser gauge variability across cells or samples when interpreting visualized features.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
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 clean non_pipeline drop: SGS is a genome-browser visualization tool, not a computational analysis pipeline, and the open-access paper reports no pipeline-derived quantitative result to grade. The determination was verified end-to-end (repo cloned/inspected, full paper read, companion dataset GSE171943 profiled as open and complete, grade A). No deviation exists on anyone's side — there simply is no gradable claim. q2 is red only because no reported value can be placed against any output (no endpoint exists), not because of any defect.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
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Reproduction footprint
<synthetic>Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.