Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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
REPRODUCED (1:1, no fabrication concern). Paper = McKellar et al. 2021 Commun Biol, the scMuscle integrated atlas (365,011 cells/nuclei from 111 mouse skeletal-muscle samples). This room's assigned accession GSE159500 = 2 of the 23 newly generated samples (GSM4831162=D2_Ev3, GSM4831163=D7_Ev3; 10x Chromium v3, notexin TA injury). EVERY in-scope pipeline-derived result reproduced: (C1) from-raw read counts EXACT (STARsolo processed exactly 208,607,334 / 205,196,263 reads, matching SRA/ENA); (C2) from-raw cell calls WITHIN-TOL (STARsolo EmptyDrops_CR 4846/4917 vs deposited CellRanger 4836/4889 = +0.21%/+0.57% -- sub-1% agreement between two independent cell-callers on the same mm10-2.1.0 27,998-gene reference; 97.5-97.8% valid barcodes); (C3) deposited filtered-matrix dims EXACT (27998x4836 / 27998x4889); (C4) final QC'd annotated cells EXACT (3537/3416 from meta.csv); (S1) headline atlas size EXACT -- the deposited Dryad integrated Seurat object holds exactly 365,011 cells across (S2) 111 samples (confirmed). Two SLURM compute jobs ran on «our HPC» (STARsolo re-quant + atlas load). NOT ATTEMPTED (out of scope): full from-scratch 111-sample re-integration (88 of 111 samples are external public accessions; only 2 are in GSE159500) and the 503,929 pre-QC barcode total (not derivable from the post-QC object). Both datasets profiled (GSE159500 + Dryad atlas, both grade A, delivers_promised=yes). All reported numbers are derivable from the shipped data/code -- no possible-fabrication flags.
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 92assessed: 2026-06-22 ⛓ 80f83edd57a4
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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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetThe paper tests whether large-scale integration of single-cell and single-nucleus transcriptomic datasets can overcome the poor sampling of rare, transient myogenic progenitor cell states in skeletal muscle regeneration and provide spatial context for their occurrence.
- ★ Large-scale integration of 111 sc/snRNAseq datasets captures rare, transitional myogenic progenitor states (commitment and fusion) that are poorly represented in individual datasets. finding
- ★ Harmony outperforms Scanorama and BBKNN for batch-correcting heterogeneous muscle sc/snRNAseq datasets based on speed, marker gene consistency, and preservation of continuous myogenic structure. finding
- ★ Integration resolves cell subtypes including endothelial cells by vessel-type origin, functionally distinct fibro-adipogenic progenitors (FAPs), and multiple immune cell populations. finding
- ★ The number of genes expressed per cell increases through myogenic progenitor states and is suppressed again in mature myofibers. finding
- ★ Novel surface markers (e.g., Timp1, Clcn5, Igfbp3, Bst2) and transcription factors (e.g., Purb, Zbtb18, Mycl, Scx) specifically mark committed and fusing myogenic cells. finding
- ★ Spatial RNA sequencing combined with the integrated single-cell reference enables high-resolution local deconvolution of cell subtypes in injured muscle. method
- Single-cell and single-nucleus preparations show systematic, largely cell-type-independent differences in detection of lncRNAs, mitochondrial, ribosomal, and dissociation-stress genes. finding
- ★ The integrated dataset and an interactive web tool are provided as a public resource for the field. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| scRNAseq (10x Chromium v3) | adult (7 mo) and aged (20 mo) C57BL/6J mouse tibialis anterior muscle | notexin injury (multiple timepoints within 1 week) vs. uninjured control | single-cell transcriptomes, cell type/subtype identification | 10x Chromium v3 |
| scRNAseq/snRNAseq (public data integration) | mouse skeletal muscle, various ages (10 days–30 months) | cardiotoxin or notexin injury, 0.5–21 dpi, or none | cell composition and transcriptomic dynamics across conditions | 10x Chromium v2, v3, v3.1 |
| Batch-correction/integration benchmarking | merged sc/snRNAseq compendium (365,011 cells) | none (computational comparison) | integration quality (marker gene expression, local/global structure, sc/sn resolution) | Harmony, Scanorama, BBKNN |
| Differential gene expression (Wilcoxon Rank Sum test) | 84,383 myogenic cells, PHATE-binned along differentiation trajectory | none | genes differentially expressed across myogenic differentiation stages | — |
| Pseudotime/trajectory analysis (PHATE dimensional reduction) | 84,383 myogenic and myofiber cells | none | continuous myogenic differentiation trajectory (quiescence to maturation) | PHATE |
| Spatial RNA sequencing | mouse muscle, 3 timepoints post-injury | injury (timepoint comparison) | local deconvolution of cell subtypes using integrated dataset as reference | — |
| Ligand-receptor co-expression analysis | integrated sc/snRNAseq compendium | injury response comparison | dynamic cell-cell interaction patterns | — |
- – Integration of 111 samples yielded 365,011 cells/nuclei after quality control from 503,929 raw barcodes
- – Harmony was the only method to resolve sc/sn differences across all cell types and maintain continuous myogenic structure
- – 84,383 myogenic cells selected; PHATE dimension 1 captured 95.6% of variance and served as differentiation pseudotime 95.6% variance
- ▼ Committed and fusing myogenic cells (bins 8-18) comprised only 0.65% of the compendium, totaling 2,366 cells across 92 of 111 samples 0.65%
- – Genes expressed per cell (normalized to sequencing saturation) increase from quiescence through differentiation, then decline in mature myofibers
- ▲ 2,031 genes differentially expressed across differentiation bins after filtering (adjusted p<10^-10, log2FC>0.5); 67 surface marker genes and 12 transcription factors identified as specific to committed/fusing cells
- – Single-nucleus data showed increased intronic/intergenic reads, lncRNA detection, and decreased mitochondrial/ribosomal transcripts versus single-cell data
- ▼ Average of ~5 committed myoblasts and ~15 fusing myocytes captured per sample, representing 0.2% and 0.5% of all cells respectively 0.2% and 0.5%
- count 365,011 cells/nuclei after QC and integration (final integrated compendium size from 111 samples)
- count 503,929 total cell barcodes before QC (raw compendium prior to filtering)
- count 84,383 myogenic and myofiber cells (cells used for PHATE differentiation trajectory)
- count 2,366 committed or fusing cells (PHATE bins 8-18, representing 0.65% of compendium)
- other PHATE_Harmony_1 accounts for 95.6% of variance (used as pseudotime proxy for myogenic differentiation)
- pvalue adjusted p-value < 10^-10, average log2-fold-change > 0.5 (filtering threshold for differential gene expression across PHATE bins)
- count 2,031 differentially expressed genes across differentiation (Wilcoxon Rank Sum test between PHATE bins)
- count 67 surface marker genes; 12 transcription factors (genes enriched specifically in committed and fusing myogenic cells)
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 study integrates 111 mouse skeletal muscle sc/snRNAseq datasets (23 newly collected, 88 public) totaling ~365,000 cells after quality control, using Harmony batch correction selected over Scanorama and BBKNN based on qualitative and marker-gene criteria. Differential gene expression across 25 evenly spaced bins of a PHATE-derived myogenic differentiation axis was tested with the Wilcoxon Rank Sum test, filtered by adjusted p-value and log2-fold-change thresholds. Results were summarized descriptively through UMAP and PHATE visualizations, canonical marker gene violin/dot plots, boxplots of transcriptomic diversity, and a Simpson diversity index to assess batch-correction mixing quality.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon Rank Sum test | Differential gene expression between each of 25 PHATE bins along the myogenic differentiation axis | 84,383 myogenic cells distributed across 25 bins | not stated |
| Simpson's diversity index | Assessment of sample-source mixing per PHATE bin to evaluate Harmony batch-correction quality | 84,383 myogenic cells across 111 samples | na |
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Differential gene expression across differentiation bins was tested with the Wilcoxon Rank Sum test, treating individual cells as independent observations↳ Could also: Pseudo-bulk approaches (e.g., DESeq2 or edgeR on per-sample aggregated counts) could also be used to test bin-level differences — Pseudo-bulk methods account for the non-independence of cells originating from the same biological sample and have been shown to better control false-positive rates in multi-sample single-cell DGE comparisons
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Batch-correction method selection (Harmony over Scanorama and BBKNN) was based on qualitative assessment of UMAP/PHATE visualizations and marker gene consistency↳ Could also: Quantitative integration benchmarking metrics such as kBET, LISI (Local Inverse Simpson's Index), or Average Silhouette Width (ASW) could also be computed for each method — Quantitative metrics provide a reproducible, method-independent basis for comparing integration quality and enable readers to assess mixing independently of visualization choices
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The specific multiple-testing correction procedure underlying the 'adjusted p-value' threshold is not named in the available text↳ Could also: Benjamini-Hochberg FDR correction with an explicit target alpha (e.g., FDR < 0.05) is a standard and widely reported approach for scRNAseq DGE — Naming the correction method and its target error rate aids reproducibility and allows direct comparison with results from other published datasets
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Transcriptomic diversity (genes detected per cell) was normalized to per-sample sequencing saturation before comparing across bins↳ Could also: Downsampling (rarefaction) to a common UMI depth per cell before computing gene detection rates could also equalize sampling effort — Rarefaction is an established ecology-derived approach that directly standardizes sequencing effort without relying on saturation estimates, and its properties are well understood in diversity analysis
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Dispersion of genes-per-cell across differentiation bins is summarized with boxplots (median and quartiles)↳ Could also: 95% confidence intervals around the per-bin mean could additionally be reported alongside or instead of boxplots — CIs convey both within-bin variability and uncertainty in the mean estimate, complementing the distributional summary of boxplots; many journals now recommend CIs for small or heterogeneous group sizes
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Simpson's diversity index was computed at the bin level on sample identifiers to assess batch-correction quality↳ Could also: Cell-neighborhood-level mixing metrics such as the Local Inverse Simpson's Index (LISI) or Shannon entropy per cell could also quantify integration quality — Neighborhood-level metrics capture local mixing structure more granularly than bin-level summaries and are increasingly reported as standard in single-cell integration benchmarks
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34773081
Paper: McKellar et al. 2021, Commun Biol 4:1280. "Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration." (scMuscle) PMCID PMC8589952 · DOI 10.1038/s42003-021-02810-x
This RU's data accession: GEO GSE159500 — "Single-cell transcriptomic sampling of regenerating mouse muscle tissue". This is a small slice of the paper's new data: 2 samples from 7-month-old mice, 10x Chromium v3, notexin injury timepoints (D2_Ev3, D7_Ev3). The paper's other new data live under GSE162172 (20 mo scRNAseq) and GSE161318 (spatial/Visium); the full atlas also folds in 88 public datasets. The brief assigns GSE159500 to this room, so the in-scope reproduction is anchored on those 2 samples.
Code:
- Brief's
code_url= github.com/rvalieris/parallel-fastq-dump — the SRA→FASTQ download utility named in the paper's Methods. Per P16 this counts: a third-party tool applied to the paper's own data is a valid reproduction. - Authors' own analysis repo = github.com/mckellardw/scMuscle (Seurat v3.2.1 + Harmony v1.0 integration, SoupX, DoubletFinder, PHATE, CellChat). README is thin on a runnable per-sample pipeline; the heavy lifting (Cell Ranger / count matrices) is described in the paper Methods, not scripted in the repo.
In scope (pipeline-derived, attempted)
| # | Result | Pipeline | Feasibility |
|---|---|---|---|
| C1 | Raw read counts per run (SRR12820638 D2, SRR12820639 D7) | parallel-fastq-dump SRA→FASTQ, count reads | HIGH — direct, cheap |
| C2 | Per-sample cell count vs deposited matrix (GSE159500_RAW.tar) | STARsolo (10x v3) re-quantification from raw FASTQ → cell calling; compare to authors' deposited filtered matrix | MEDIUM — needs mouse ref + STAR index + EmptyDrops cell calling; depends on barcode read (R1) surviving on SRA |
| C3 | Genes detected / matrix dimensions of deposited GSE159500 matrices | load deposited CSV/MTX/TSV, report rows×cols | HIGH — direct profiling |
Stretch (attempted if compute allows)
| # | Result | Approach |
|---|---|---|
| S1 | Atlas headline 365,011 cells/nuclei (Results) + cluster count | download processed Seurat object from Dryad (doi:10.5061/dryad.t4b8gtj34), count cells/clusters. NOTE: this verifies the deposited artifact, not a from-scratch re-integration of 111 samples. |
Out of scope (not attempted, stated why)
- Full atlas re-integration of 111 samples (23 new + 88 public, ~365k cells, Harmony): requires gathering 88 external accessions across 14 groups + very large compute; not feasible as a faithful 1:1 within this room. GSE159500 contributes only 2 of the 111 samples.
- Wet-lab / experimental results (notexin injury, dissociation, library prep): not computational.
- Spatial RNA-seq (Visium/Slide-seq) results: different accession (GSE161318), out of this RU's data scope.
- CellChat / BayesPrism / PHATE downstream biological interpretations: depend on the full integrated object; not pinnable to GSE159500's 2 samples.
Reproduction strategy
- front1 («infra»): download GSE159500_RAW.tar (deposited matrices = authors' "truth"), prefetch SRR12820638/39, parallel-fastq-dump → FASTQ, fetch mouse reference (GRCm38/mm10 + GTF) and 10x v3 whitelist.
- SLURM (compute only): build STAR index, run STARsolo per sample (CB=16, UMI=12, EmptyDrops_CR cell filter), emit filtered matrix.
- Compare reproduced cell/gene counts vs deposited matrices (C2/C3) and read counts vs SRA metadata (C1). Grade provisionally.
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Reproduction footprint
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