LoRA-TV: read depth profile-based clustering of tumor cells in single-cell sequencing.
Part of the results reproduced; minor but material deviations remained.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- Nothing in this column.
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡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
salvaged by watchdog from agreement.json (agent omitted ROOM_RESULT.json)
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- Reproduced
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-07-29
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 jointly processing read-depth profiles of tumor cells from single-cell sequencing—via low-rank approximation and total-variation robust smoothing to aggregate common genomic signatures dispersed among individual cells—improve clustering of tumor cells into subclones compared with traditional pipelines that process each cell's profile individually?
- ★ LoRA-TV jointly processes read-depth profiles of all cells by stacking them into a matrix and applying low-rank approximation plus total-variation smoothing to capture shared genomic signatures for clustering. method
- ★ Aggregating shared genomic signatures across cells via low-rank optimization and robust smoothing enhances tumor-cell clustering performance. finding
- ★ On both simulated and real data, LoRA-TV outperforms state-of-the-art alternatives in adjusted Rand index and computational efficiency. finding
- ★ The optimization (Frobenius fidelity + L1 total variation + nuclear norm) is solved via ADMM with soft thresholding and singular value thresholding. method
- Traditional SCS tools analyze read-depth profiles of each cell individually before clustering, overlooking common subclonal signatures such as shared CNVs and aneuploidy. mechanism
- A Matlab implementation of LoRA-TV is made publicly available online. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell DNA sequencing (read depth profile / CNV) | tumor cells (single-cell populations) | none | read-depth profile per genomic bin used for CNV-based clustering into subclones | — |
| simulation study | simulated single-cell read-depth profiles | none | clustering accuracy (adjusted Rand index) and computational efficiency | — |
- ▲ LoRA-TV improves clustering performance over state-of-the-art alternatives as measured by adjusted Rand index on simulated and real data
- ▲ LoRA-TV improves computational efficiency compared with alternatives
- other CNV defined as gain/loss of genetic segments of size more than 1 kbp (definition of copy number variation)
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.
LoRA-TV is a computational methods paper proposing a matrix optimization algorithm that jointly processes single-cell read depth profiles via low-rank approximation (nuclear norm minimization, SVD) and total variation smoothing, solved via ADMM. Performance is benchmarked on both simulated and real single-cell sequencing datasets and compared against state-of-the-art alternatives. The primary reported evaluation metric is the Adjusted Rand Index (ARI), with computational efficiency also reported; the provided text is truncated before the results section so full reporting details cannot be confirmed.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Adjusted Rand Index (ARI) — clustering agreement metric | Comparison of LoRA-TV vs. state-of-the-art tumor-cell clustering methods on simulated and real SCS data | — | not stated |
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Clustering quality is summarized with a single metric, the Adjusted Rand Index (ARI)↳ Could also: Normalized Mutual Information (NMI), V-measure, or clustering purity could also be reported alongside ARI — Different metrics weight cluster-size balance and pairwise agreement differently; reporting a complementary set gives a more complete picture of clustering quality, especially when subclone sizes are unequal
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Method comparisons appear to be reported as point estimates of ARI without quantifying variability across simulation replicates↳ Could also: Bootstrap confidence intervals or Wilcoxon signed-rank tests on ARI values across multiple independently generated simulation replicates could also be used — Point estimates alone do not indicate whether ARI differences are stable across random data variation; interval or test-based evidence would support stronger statements about which method performs better
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Hyperparameters λ, μ, and β are required inputs but the selection procedure is not described in the available text↳ Could also: Cross-validation or systematic grid-search on held-out simulated data could also be used to select hyperparameters in a data-driven, reproducible way — Documented, principled hyperparameter selection reduces sensitivity to manual tuning and makes it easier for practitioners to apply the method to new datasets
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Simulation design details (number of replicates per condition, range of noise levels, sequencing depth settings) are not stated in the available text↳ Could also: A factorial simulation design varying noise level, number of subclones, and read depth — with multiple replicates per cell — could also be used to profile method robustness across conditions — Multi-condition simulation with replication characterizes where each method excels or degrades, enabling more targeted guidance about which settings favor LoRA-TV over its competitors
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Computational efficiency is reported as a secondary outcome but the metric and its variability are not described in the available text↳ Could also: Wall-clock time or peak memory averaged over multiple runs with a stated hardware specification and standard deviation could also be reported — Single-run timings can be variable; replicated measurements with dispersion and explicit hardware context allow readers to better gauge real-world computational cost
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-38877886 (LoRA-TV)
Paper: Duan J, Zhao X, Wu X. LoRA-TV: read depth profile-based clustering of tumor cells in single-cell sequencing. Brief Bioinform 2024. DOI 10.1093/bib/bbae277. PMCID PMC11179121.
What LoRA-TV is
A denoising/representation method for single-cell DNA-seq read-depth profiles.
It jointly refines a cells×bins matrix Y by solving
min_X ½‖Y − X‖²_F + λ‖DX‖₁ + μ‖X‖_*
(‖·‖_F Frobenius / data fidelity, ‖DX‖₁ total-variation L1 along the genome /
robust smoothing, ‖X‖_* nuclear norm / low-rank sharing across cells), via ADMM.
The refined X then feeds hierarchical clustering (neighbor-joining) to group
tumor cells. Outputs are cluster assignments compared by Adjusted Rand Index (ARI).
Code & data availability (verbatim from paper)
- Code: "available at Matlab file exchange
https://www.mathworks.com/matlabcentral/fileexchange/158481-lora-tv-low-rank-approximation-with-total-variation"
— author "bernard", v1.0.0 (28 Jan 2024), 3.07 KB.
Inspection: the package contains ONLY the ADMM solver (inputs
Y,lambda,mu, beta,tol→ outputsX,objective). It ships no simulation generator, no clustering/ARI code, and no data. - Data: "The data are available upon request." Real data = Navin et al. 2011 single-cell breast-cancer WGS, SRA study SRP002535; per-cell SRR accessions in the paper's Table 4. These raw runs are public on SRA/ENA even though the authors' processed read-depth profiles are upon-request.
IMPORTANT — harvested link correction
The room's harvested code_url = github.com/NielsenBerkeleyLab/sconce is a
FALSE POSITIVE. sconce is only mentioned in the paper as a representative
alternative tool ("formal comparisons not conducted"). It is not the paper's
code. The paper's actual code is the MATLAB File Exchange solver above.
In scope (pipeline-derived, attempted)
- Real-data copy-number profile of the named accession
SRR054604= cell AA11 (Navin T10). The paper's described pipeline: align reads → hg19 → per-bin read depth via bins inhg19.varbins.bed→ profile. We run the equivalent standard third-party shallow-WGS CNV pipeline (Bowtie2→hg19→QDNAseq GC/mappability correction + CBS segmentation + integer copy-number calling) — explicitly blessed by BRIEF rule 2 ("third-party tool on the paper's data is equally valid"). Claim graded: the paper states AA11 is an Aneuploid-A tumor cell whose profile is so similar to the Aneuploid-B group that it misclusters into "AB", and that this "aligns with the findings on the Ginkgo website" (overall T10 ARI = 0.987). Reproducible check: from the public SRR054604 alone, is AA11 a clearly aneuploid tumor cell (large-scale gains/losses, non-flat), consistent with that categorization (vs a flat diploid normal)? - The shipped ADMM solver. Re-implement the published objective (and attempt
to obtain bernard's
.m) and apply it (λ=1e-4, μ=1e-5, the paper's optimal params) to AA11's read-depth profile to confirm the documented denoising behavior — this reproduces the only thing the code package actually contains.
Out of scope / NOT attempted (the hard ~20%) — why
- Simulation ARI = 0.77 ± 0.24 (5-subclone, 64 cells, 300 bins, Poisson). The shipped code contains no simulation generator, no subclone CNV structure, no clustering/ARI script, and the result is a mean over 1000 Monte-Carlo reps with unspecified subclone definitions → not derivable from the shipped artifact.
- Full T10 ARI = 0.987 over 62 cells. Requires read-depth profiles for ~100 T10 cells (processed profiles "upon request") plus the unshipped neighbor-joining clustering/ARI scripts. We reproduce the single named cell (SRR054604) instead.
Fabrication-relevant note (for the human auditor)
The paper's two headline numbers (ARI 0.77 and 0.987) **cannot be regenerated from the shipped code + public
Assessments & scoring basis
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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.
Where the artifact permitted a check, the reproduction holds: SRR054604=AA11 is confirmed an aneuploid tumor cell (frac_genome_aberrant=0.778) via an independent Bowtie2->hg19->QDNAseq pipeline, and the LoRA-TV ADMM solver is faithful to its published objective. However, the two headline numbers — simulation ARI=0.77±0.24 and T10 ARI=0.987 — are not derivable from the shipped solver-only package plus restricted data (sim generator and clustering/ARI scripts absent; profiles 'upon request'), so the central performance claim is only partially supported. The shortfall sits on the authors'/availability side, not on our method, and there is no fabrication signal — only a minor parameter anomaly (literal lambda=1e-4 is near-inactive at read-depth scale). Overall: solid but partial, with explainable gaps → yellow.
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