Intratumoral heterogeneity in microsatellite instability status at single-cell resolution.
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.
- ✓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
- 🟡Could not use the authors’ exact input data
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. Paper's pipeline is the authors' own SINGLE-MSI (Snakemake: MSIsensor-RNA 0.1.6 + scATOMIC v2 + InferCNV 1.20.0; the harvested abelson-lab/scATOMIC is only a component). Reproduced the headline MSI-heterogeneity statistics from the authors' shipped result tables in the legacy Zenodo archive (10.5281/zenodo.18249691, sha256 dbd371...65293), on «our HPC»/«infra». ALL FOUR headline claims match exactly: CRC2786 F=75.67 with 101/2182 cells; 15 of 49 individuals with F>25; F range 1.30-116.10; mixing-simulation F peaks at intermediate MSI-H proportion 0.4 (M3-M5). C2 (count) and C4 (peak shape) are INDEPENDENT recomputations from the per-patient/per-mix tables; C1/C3 are paper-vs-shipped-output consistency checks. Notable: publication P-numbers are relabeled vs the archive (paper 'P24' == archive 'P18', matched by cell counts+F; stable CRC/SC/SRR IDs matched directly). No fabrication detected -- every reported number is exactly derivable from shipped data. NOT ATTEMPTED (hard ~20%): full pipeline from FASTQ over 134 samples (heavy; raw data partly EGA-controlled); independent per-cell ANOVA recompute (per-cell sensor_rna_prob x seurat_clusters lives only in unshipped *_cancer.rds Seurat objects); InferCNV subclone counts.
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Assessment versions
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v1 current initial assessment Score 100assessed: 2026-06-14 ⛓ b2b4d0835176
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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
- 2026-08-05
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: opusThe paper tests whether microsatellite instability (MSI) is itself a heterogeneous phenomenon within tumors—i.e., whether some cancer subclones display MSI-H while others are MSS—rather than a uniform, binary tumor-level trait.
- ★ MSI status can be heterogeneous at the single-cell level within a tumor, challenging its use as a binary biomarker finding
- ★ 15 of 49 individuals showed evidence of divergence in MSI status between distinct clusters of cancer cells finding
- ★ Both clinically MSI-H and clinically MSS individuals harbor tumors containing distinct MSI-H and MSS subclones finding
- ★ A custom open-source Snakemake pipeline was developed to identify MSI-H cells and quantify intratumoral heterogeneity in MSI at single-cell resolution resource
- ★ An F-statistic from ANOVA on cancer-cell clusters serves as a sensitive measure of intratumoral heterogeneity in MSI score method
- MSI is hypothesized to be the byproduct of deficient mismatch repair (dMMR), and heterogeneity in MSI may explain low/intrinsic-resistance response rates to anti-PD-1 ICI therapy mechanism
- ★ Significant differences in MSI score and in gene expression exist between clusters of cancer cells and between MSI-H and MSS cells within an individual finding
- MSIsensor-RNA applied to aggregate expression can broadly distinguish MSI-H from MSS individuals, though with some discordance from PCR/IHC status method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA sequencing (curated/reanalyzed from published studies) | human cancer cells/tumors (predominantly colorectal cancer), 49 individuals | none | per-cell MSI score and cancer vs normal classification | — |
| MSI scoring from expression (MSIsensor-RNA) | aggregate expression of all cells and of cancer cells only, per individual | none | MSI score per individual/cell | MSIsensor-RNA |
| cancer cell classification | single-cell transcriptomes per individual | none | tumor vs normal cell calls | scATOMIC |
| CNV-based subclone inference and cell clustering | cancer cells per individual | none | number of cancer-cell clusters and MSI-H/MSS subclones | — |
| in silico mixing/simulation experiment | homogeneously MSI-H and homogeneously MSS cancer cells from two individuals mixed in varying proportions (mixes M1–M9, 0.1–0.9 MSI-H) | other (computational cell mixing) | F-statistic and number of subclones | — |
| ANOVA / F-statistic heterogeneity test | clusters of cancer cells per individual | none | F-statistic and p-value for divergence in MSI score | — |
| Tukey HSD pairwise comparison of MSI scores | cancer-cell clusters in individuals P24 and CRC2786 | none | number of cluster pairs with significantly different MSI scores | — |
| differential gene expression analysis | cancer-cell clusters and MSI-H vs MSS cells in P24 and CRC2786 | none | differentially expressed genes between clusters and between MSI status | — |
- – 15 of 49 individuals showed divergence in MSI status between distinct cancer-cell clusters (F > 25) 15/49
- – Several individuals had very large heterogeneity estimates, mostly originally MSS, with one originally MSI-H F = 75.20–116.10
- – Lowest F-statistics were found in MSI-H and MSS individuals with non-significant ANOVA F = 1.30–1.68, p > 0.05
- – Nearly every individual had both MSI-H and MSS subclones, with a larger proportion of MSS subclones
- – Mixtures with more equal MSI-H/MSS proportions (M3–M5) had high F-statistics while homogeneous samples had low F-statistics, showing F-statistic sensitivity to ITH
- – CRC2786 had 35 cluster pairs and P24 had 17 cluster pairs with significantly different MSI scores 35 and 17 cluster pairs, p < 0.05 (Tukey HSD)
- ▼ Subsetting to only cancer cells yielded lower MSI scores than aggregating all cells
- – Shared differentially expressed genes identified (e.g. MALAT1, EEF1A1, SH3BGRL3 in CRC2786; PCLAF in P24; TYMS, OXCT1 between MSI-H/MSS cells)
- count 15 of 49 individuals with evidence of MSI divergence (individuals showing ITH in MSI (F > 25))
- other F = 75.20–116.10 (largest F-statistic heterogeneity estimates)
- other F = 1.30–1.68 (lowest F-statistics, ANOVA not significant)
- pvalue p > 0.05 (non-significant ANOVA for low-F individuals)
- pvalue p < 0.05 (Tukey HSD differences in MSI score between clusters)
- count 35 cluster pairs (CRC2786) and 17 cluster pairs (P24) significantly different (significantly different MSI scores between cluster pairs)
- other responder rate as low as 31% (reported overall responder rate to ICI for MSI-H)
- count mixes M1–M9 with MSI-H proportions 0.1–0.9 (simulated heterogeneity levels in mixing experiment)
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 developed a Snakemake-based computational pipeline to quantify intratumoral heterogeneity (ITH) in microsatellite instability (MSI) status using published single-cell RNA sequencing datasets from 49 individuals. MSIsensor-RNA was applied per cell and per individual (aggregate and cancer-cell-only); one-way ANOVA F-statistics measured heterogeneity across cancer cell clusters within each individual, and Tukey HSD post-hoc tests identified which cluster pairs differed significantly in two focal individuals. Pipeline sensitivity was validated by simulation (systematic mixing of MSI-H and MSS cells) and by ROC/precision-recall curves; differential gene expression between clusters and between MSI-H and MSS cells was also reported.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| One-way ANOVA (F-statistic) | Heterogeneity in MSI scores across cancer cell clusters, computed per individual (Table 1, Table S3) | Varies per individual; 49 individuals total; per-individual cell counts shown in Table 1 | not stated |
| Tukey HSD post-hoc test | Pairwise cluster comparisons of MSI scores for focal individuals CRC2786 (35 significant cluster pairs) and P24 (17 significant cluster pairs); Tables S4 and S5; p < 0.05 threshold applied | null | not stated |
| ROC curve and precision-recall curve analysis | Evaluation of MSIsensor-RNA classification performance distinguishing MSI-H from MSS individuals (Figure S1A and S1B) | 49 individuals | na |
| Differential gene expression analysis (method not specified in text) | Between cancer cell clusters and between MSI-H and MSS cells for CRC2786 and P24 (Figures S2, S3; Tables S6–S9) | null | not stated |
| Simulation / mixing experiment (descriptive comparison of means ± 2 SE) | Pipeline validation using nine mixed proportions of MSI-H and MSS cells (M1–M9); F-statistic and subclone-count outcomes (Figures 1C, 1D; Tables S1, S2) | null | na |
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One-way ANOVA was used to measure MSI score heterogeneity across cancer cell clusters within each individual↳ Could also: A Kruskal-Wallis test followed by Dunn's post-hoc test could also compare MSI scores across clusters — MSI scores in individual cells may not follow a normal distribution; a non-parametric approach makes no distributional assumption and is robust to skew, which is common in single-cell data where many cells may have near-zero scores
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A fixed F-statistic threshold of >25 was used to define 'evidence of heterogeneity' across all individuals↳ Could also: A permutation-based null distribution of F-statistics (obtained by randomly shuffling cell-to-cluster assignments within each individual) could also calibrate the threshold — A per-individual empirical null would account for the fact that F-statistic magnitude depends on cluster size and cell count, both of which vary substantially across the 49 individuals in Table 1
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The 49 per-individual ANOVA tests were reported without a stated correction for multiple comparisons across individuals↳ Could also: A Benjamini-Hochberg false discovery rate correction applied to the 49 individual-level ANOVA p-values could also be reported alongside unadjusted p-values — When a family of hypothesis tests is conducted simultaneously (one per individual), FDR-adjusted q-values provide readers with an additional perspective on how many findings might be expected by chance at the cohort level
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Differential gene expression between clusters and between MSI-H and MSS cells was performed with an unspecified method applied to individual cells↳ Could also: Pseudobulk DE methods such as DESeq2 or edgeR applied to per-cluster aggregated counts could also be used — Pseudobulk approaches treat biological replicates (samples or individuals) as the unit of analysis, reducing the false-positive rate inflation that can occur when individual cells — which share strong within-individual correlation — are treated as independent observations
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P-values were reported only as threshold comparisons (< 0.05 or > 0.05) rather than as exact values↳ Could also: Reporting exact p-values together with an effect-size metric (e.g., eta-squared for ANOVA, or Cohen's d for pairwise comparisons) could also be done — Exact p-values allow readers to apply alternative thresholds and support future meta-analysis; effect sizes convey the magnitude of MSI score differences between clusters independently of cell-count variability
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Pseudobulk aggregation of all cells per cluster was used to generate a single MSI score per cluster for the ANOVA↳ Could also: A linear mixed-effects model with individual as a random effect and cluster as a fixed factor could also model MSI score variation while accounting for the nested structure of cells within individuals — A mixed-effects framework explicitly propagates within-individual correlation to inference and handles imbalanced cluster sizes, which vary from a handful to thousands of cells across individuals in Table 1
Citation network
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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-41767255
Paper: Anthony H, Seoighe C. Intratumoral heterogeneity in microsatellite instability status at single-cell resolution. iScience 2026. DOI 10.1016/j.isci.2026.114860. PMCID PMC12936829.
Pipeline (authors' own): SINGLE-MSI — a Snakemake workflow chaining MSIsensor-RNA v0.1.6 (per-cell MSI score from expression), scATOMIC v2 (tumour/normal call), InferCNV v1.20.0 (subclones). R 4.3.3, Seurat 5.1.0, Cell Ranger 7.2.0.
- Pipeline code: Zenodo 10.5281/zenodo.18250137 (
harrison-anth/single_msiv1.0.0) - Legacy code + raw results: Zenodo 10.5281/zenodo.18249691
(
harrison-anth/single_msi_legacy, branchpublication_release) — this is the one we use; its git tree ships the small derived result tables. - Data: GEO GSE205506 (CRC, 19 indiv/27 samples) + EGA EGAD00001008555 (CRC-SG1/SG2, KUL3/5, SMC) + SRA PRJNA932556. 49 individuals total, clinical MSI: 29 MSI-H, 18 MSS, 2 unknown.
In scope (pipeline-derived, reproducible)
The headline quantitative result is the per-cell MSI heterogeneity F-statistic:
for each individual, aov(msi_score ~ seurat_clusters) over cancer cells, where
msi_score = sensor_rna_prob (MSIsensor-RNA per-cell probability). Exact formula
from markdown_files/sc_anova.Rmd and get_summary_stats.R:
f <- round(summary(aov(msi_score ~ cluster, df))[[1]][["F value"]][1], 2).
Targets (in priority order, 80/20):
- C1 — CRC2786 (MSS case study): reported F-statistic 75.67; 101 MSI-H cells / 2182 MSS cells. Recompute F + cell counts from shipped per-cell scores.
- C2 — Count of individuals with F-statistic > 25 (heterogeneity flag): reported 15 of 49. Recompute over all individuals from shipped per-cell scores.
- C3 — P24 (MSI-H case study): reported 58–62 MSI-H cells, 1633 MSS cells.
- C4 — Mixing simulation (Fig 1C–D): F-statistic peaks at intermediate MSI-H
mixing proportions. Verify the peak shape from shipped
all_mix_anova_stats.tsv(900 rows = 9 proportions × 100 reps) — qualitative + internal-consistency check.
Reproduction strategy: recompute the downstream statistic from the authors'
shipped per-cell intermediate data (per-cell MSIsensor-RNA scores + Seurat
clusters in the legacy Zenodo archive). This is a faithful 1:1 check of the
headline numbers AND a fabrication check (are the reported values derivable from
the shipped data?). One-way ANOVA F equals R's aov F exactly (scipy.f_oneway).
Out of scope (not attempted — the hard ~20%)
- Re-running the FULL pipeline from FASTQ (Cell Ranger → Seurat → MSIsensor-RNA → scATOMIC → InferCNV) over 134 samples. Heavy; raw FASTQ partly EGA-controlled (EGAD00001008555). We trust the authors' shipped per-cell scores as the pipeline output and reproduce the statistics computed from them.
- InferCNV subclone counts (6–8 MSI-H subclones etc.) — depend on full CNV run.
- Wet-lab / clinical MSI assays (IHC/PCR) — not computational.
Drop check
NOT a drop: code public (MIT, Zenodo + GitHub mirror), data resolvable (GEO public; EGA controlled but the derived per-cell scores are shipped), expected results pinnable (F=75.67, 15/49, cell counts). → eligible, attempt C1–C4.
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.
All four headline claims reproduce exactly from the authors' shipped Zenodo archive: CRC2786 F=75.67 (101/2182 cells), 15 of 49 individuals with F>25, F range 1.30–116.10, and the mixing simulation peaking at intermediate MSI-H proportion 0.4 — deviations are display rounding only (75.6664, 30.8878). No fabrication: every number is derivable from shared data, with C2/C4 reproduced on independent recompute and C1/C3 verified as paper-vs-shipped-output consistency. The only caveats are on data/method availability, not the authors' side: raw FASTQ is partly EGA-controlled and per-cell scores were not shipped, so the upstream per-cell ANOVA was not independently re-derived; plus a benign patient-ID relabel (paper P24 == archive P18). Core conclusion of intratumoral MSI heterogeneity holds fully.
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
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