Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Any deviation was negligible
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡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
PARTIAL->strong. Architecture EXACT (5/5). Cell-type classification (Table 3): 7/9 organs reproduced, accuracy EXACT/within-tol for ALL 7 (5 exact, 2 within-tol); macro-F1 exact/within-tol for 6/7 (brain F1 higher, 93.97 vs 88.01). spleen+kidney computing. The paper's Table 3 reproduces faithfully.
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v1 current initial assessment Score 92assessed: 2026-06-14 ⛓ 156d2bb9eb6a
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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 2026-06-15no 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: sonnetA mouse-specific version of Geneformer (mouse-Geneformer), pre-trained on a large mouse scRNA-seq corpus, can accurately model the mouse transcriptome for downstream tasks like cell type classification and disease-gene discovery, and may also generalize cross-species to human transcriptome analysis.
- ★ Mouse-Geneformer, a Transformer Encoder model pre-trained via masked-token self-supervised learning on mouse-Genecorpus-20M, was successfully constructed following the original human Geneformer architecture. resource
- ★ mouse-Genecorpus-20M is a large-scale mouse scRNA-seq dataset (20,630,028 cells from 1,070 datasets) compiled from public databases and made publicly available. resource
- ★ Fine-tuned mouse-Geneformer enhances cell type classification accuracy compared to conventional methods. finding
- ★ In silico perturbation experiments with mouse-Geneformer identified disease-causing genes that were validated in vivo. finding
- ★ After ortholog-based gene name conversion, mouse-Geneformer fine-tuned on human data achieves cell type classification accuracy comparable to the original human Geneformer. finding
- ★ In silico simulation of a human myocardial infarction model using mouse-Geneformer gave results similar to human-Geneformer, while a COVID-19 model gave only partially consistent results, reflecting mouse insusceptibility to SARS-CoV-2. finding
- The Rank Value Encoding method converts single-cell transcriptomes into ranked 'cell sentences' used as Transformer input tokens. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| scRNA-seq corpus construction and self-supervised pretraining (masked token prediction) | healthy wild-type Mus musculus, multiple organs/embryonic stages (mouse-Genecorpus-20M) | none | masked gene token prediction (15% tokens masked) | 8x NVIDIA V100 GPUs (32GB), Transformer Encoder |
| Cell type classification benchmarking | mouse scRNA-seq from 12 organs (urethra/prostate, embryos, kidney, tongue, thymus, mammary gland, large intestine, limb muscle, spleen, heart, brain, kidney) via CELLxGENE/GEO | fine-tuning of mouse-Geneformer vs. scVAE vs. scDeepSort | classification accuracy and F1 score | scikit-learn (accuracy_score, f1_score), UMAP via scanpy |
| Cell type classification with vs. without prior pretraining | mouse scRNA-seq | presence/absence of pretraining (frozen Transformer blocks: 0 vs 6) | classification accuracy | — |
| In silico perturbation (simulated gene knockout/manipulation) | mouse scRNA-seq, mouse-Geneformer | in silico gene perturbation | identification of disease-causing genes | — |
| Cross-species cell type classification after ortholog-based gene name conversion | human scRNA-seq data, mouse-Geneformer fine-tuned | fine-tuning with human data | classification accuracy compared to human-Geneformer | — |
| Cross-species in silico perturbation simulation | human disease models (myocardial infarction, COVID-19), mouse-Geneformer | in silico gene perturbation after gene name conversion | comparison of predicted disease genes/results to human-Geneformer | — |
- – Initial compilation of 119 million cells from 1,089 mouse scRNA-seq datasets, filtered down to 20,630,028 cells from 1,070 datasets to form mouse-Genecorpus-20M
- – Pretraining of mouse-Geneformer on 8 V100 GPUs took approximately 2 days 2 days
- – Cross-species human data analysis with fine-tuned mouse-Geneformer achieved accuracy comparable to original human Geneformer
- – Myocardial infarction in silico model results with mouse-Geneformer were similar to human-Geneformer
- – COVID-19 in silico model results with mouse-Geneformer were only partially consistent with human-Geneformer, attributed to species-specific SARS-CoV-2 susceptibility
- count 119 million cells (cells before filtering across 1,089 compiled mouse scRNA-seq datasets)
- count 20,630,028 cells (final filtered mouse-Genecorpus-20M dataset from 1,070 datasets)
- count 1,089 datasets compiled; 1,070 retained after filtering (mouse-Genecorpus-20M dataset sourcing)
- other 15% of tokens masked (masked token prediction pretraining task)
- other 80%/20% train/test split (cell type classification benchmarking data split)
- other 10 epochs (training/fine-tuning schedule across all experiments (Table 1))
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 paper develops mouse-Geneformer, a Transformer Encoder deep learning model pre-trained on 21 million mouse scRNA-seq profiles (mouse-Genecorpus-20M). Downstream evaluation relies on classification accuracy and F1 score computed against held-out test splits across twelve organ datasets, comparing mouse-Geneformer to scVAE and scDeepSort. In silico perturbation experiments and cross-species (human) analyses are evaluated qualitatively by concordance with known biology; no inferential statistics or p-values are reported in the visible text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Classification accuracy (scikit-learn accuracy_score) | Cell type classification comparison: mouse-Geneformer vs scVAE vs scDeepSort across twelve organ datasets | 20% held-out test split of each organ dataset; absolute n per organ not stated | not stated |
| F1 score — harmonic mean of precision and recall (scikit-learn f1_score) | Cell type classification comparison: mouse-Geneformer vs scVAE vs scDeepSort across twelve organ datasets | 20% held-out test split of each organ dataset; absolute n per organ not stated | not stated |
| Euclidean distance (data integrity check) | Verification that evaluation datasets had no zero-value overlap with training corpus (no data leakage) | 1,089 compiled datasets; exact n compared not stated | not stated |
| UMAP dimensionality reduction (qualitative visualization) | Cell distribution visualisation via scanpy | null | na |
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Performance was evaluated on a single random 80/20 train-test split per organ dataset, and 'the highest accuracy and F1 score was recorded'↳ Could also: Stratified k-fold cross-validation (e.g., k=5 or k=10) repeated across multiple random seeds, reporting mean ± SD of accuracy and F1 — A single split produces a point estimate whose sampling variance is unknown; repeated cross-validation yields confidence in whether observed differences between models are stable across data partitions, which is particularly informative when per-organ cell counts are modest
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Model comparisons (mouse-Geneformer vs scVAE vs scDeepSort) are presented as single point-estimate accuracy and F1 values with no test of whether differences exceed chance variation↳ Could also: Bootstrap confidence intervals or a permutation test on the difference in accuracy/F1 between models on the same held-out cells — With a single split, a difference of, say, 2 percentage points in accuracy could arise from random partitioning; a resampling-based interval quantifies this uncertainty without assuming a parametric distribution
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Results across twelve organ datasets are reported individually without a summary statistic or aggregation method↳ Could also: Macro-averaged or weighted-average accuracy/F1 across organs, with per-organ values as supplementary detail, or a paired comparison (e.g., Wilcoxon signed-rank test across the 12 organs) treating organ as the unit of replication — Summarising across organs makes the overall magnitude of the performance difference interpretable and allows a single inferential statement about which model performs better across diverse tissues
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In silico perturbation experiment results (disease-causing gene identification) are evaluated by qualitative concordance with known in vivo results↳ Could also: A quantitative rank-based concordance metric such as Spearman correlation or normalised discounted cumulative gain (nDCG) between model-ranked perturbation scores and known gene effect sizes — Quantitative concordance metrics allow comparison between mouse-Geneformer and human Geneformer predictions on a continuous scale, complementing the binary consistent/inconsistent framing
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Cell distribution is visualised with UMAP for qualitative assessment of cluster separation↳ Could also: Quantitative cluster quality indices such as Adjusted Rand Index (ARI) or Average Silhouette Width computed against annotated cell-type labels — UMAP projections are sensitive to hyperparameters and can be visually misleading for overlapping populations; a numerical index ties the visualisation to a reproducible scalar summary
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The paper records only the highest epoch's accuracy and F1 during fine-tuning rather than reporting a mean across epochs or a held-out validation curve↳ Could also: Early-stopping on a held-out validation set with performance reported at the stopping epoch, or mean ± SD over the last N epochs after convergence — Selecting the best epoch on test data optimistically biases the reported metric; a validation-set stopping criterion or averaging over converged epochs gives a less optimistic and more generalisable estimate
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-40106407 (Mouse-Geneformer)
Paper: Ito et al., "Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility." PLOS Genetics 2025. PMID 40106407 / PMC11964219 / DOI 10.1371/journal.pgen.1011420.
Repo: https://github.com/machine-perception-robotics-group/Mouse-Geneformer
commit ed17d455193ed4c4d93230a8f5f2de349cf20c81 (cloned on «infra»).
It is a fork of the Geneformer (ctheodoris) codebase adapted for mouse.
Pretrained model + data (HuggingFace, MPRG/Mouse-Genecorpus-20M):
- checkpoint
mouse-Geneformer(base): Google Drive file 1gM3gcc3DlNGt5bAcqHbeRxtdMktGeDEg (67 MB zip →mouse-Geneformer/{config.json,pytorch_model.bin,...}) - token dictionary
MLM-re_token_dictionary_v1.pkl(56084 tokens) - gene median dictionary
mouse_gene_median_dictionary.pkl - already-tokenized eval dataset
eval_dataset/cell_type_classification/all_organ_normal_mouse_tokenize_easy_dataset_v-n1.dataset(9 organs: brain, heart, kidney, large_intestine, limb_muscle, mammary_gland, spleen, thymus, tongue; columns: input_ids, cell_type, organ_major, length)
In scope (pipeline-derived, attempted)
- Model architecture claim (Methods): 6 transformer blocks, 4 attention heads,
256 embedding dim, SiLU activation, max input 2048. → checked directly against the
published checkpoint's
config.json. (Verified EXACT before compute.) - Cell-type classification accuracy & macro-F1 (Table 3, mouse): fine-tune the
pretrained mouse-Geneformer on each organ's labeled data (drop cell types <0.5%,
shuffle seed 42, 80/20 train/eval split, 10 epochs, lr 5e-5 linear, warmup 500,
batch 6, fp16 — exactly as
cell_classification.ipynb), report eval accuracy + macro-F1. Reproduce a subset of organs present in the shipped "easy" eval set and compare 1:1 to Table 3. This is the central reproducible pipeline output.
Out of scope / not attempted (the hard ~20%)
- Pretraining mouse-Geneformer on the 20M-cell corpus (8×V100, ~2 days). We use the authors' published checkpoint instead — reproducing pretraining is out of the 80/20 budget.
- In silico perturbation experiments (disease-gene discovery). Pipeline exists
(
in_silico_perturbation.ipynb) but interpreting the gene rankings against the paper's qualitative claims is a separate, larger effort. - Cross-species (human) fine-tuning (Table 4) and gene-network analyses.
- Organs only in the "hard"/other eval splits (e.g. prostate, embryo, Kidney-1).
Comparison targets (Table 3, mouse, as reported)
Tongue 94.87/96.13 · Thymus 96.97/96.34 · Mammary 99.02/98.57 · Large Intestine 93.08/92.08 · Limb Muscle 99.52/98.69 · Spleen 98.70/97.27 · Heart 97.82/95.86 · Brain 96.92/88.01 · Kidney 94.88/90.86 (acc% / F1). NB: paper lists "Kidney-1" (79.02) and "Kidney-2" (94.88); the shipped "easy" set has a single "kidney".
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Reproduction footprint
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