OSCR

mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.

Code ↔ Paper

21 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 21 matches
  1. [1] § 3 Results › 3.5 Posterior velocity variability reveals modality-specific uncertainty patterns ↔ mmVelo_tutorial_v2/src/mmvelo_multi/velocity_off_manifold_mod.py, lines 1–37 · score 0.74 · low dimensional embedding, local tangent space, manifold instability, velocity samples, fluctuation, metric
  2. [2] § 2 Methods › 2.13 Preprocessing of data › 2.13.1 10× embryonic E18 mouse brain ↔ mmVelo_tutorial_v2/src/fig_E18_mose_brain/08_benchmarking_latent.py, lines 78–137 · score 0.71 · E18 mouse, highly variable genes, Scanpy, matrix, filtered, brain
  3. [3] § 3 Results › 3.7 mmVelo estimates velocity in missing modalities ↔ mmVelo_tutorial_v2/src/fig_human_brain/01_clustering_pseudotime.py, lines 17–43 · score 0.70 · mGPC, GluN, nIPC, cyc, prog, SP
  4. [4] § 2 Methods › 2.12 Velocity inference on missing modality ↔ mmVelo_tutorial_v2/tutorial_2_human_brain_missing_modality.ipynb, lines 1–59 · score 0.69 · cross modal, human cortical development, scRNA, scATAC, missing modality, profiles
  5. [5] § 3 Results › 3.4 mmVelo reveals the dynamics of TF binding motifs in mouse hair follicle development ↔ mmVelo_tutorial_v2/src/fig_mouse_hair_follicle/03_clustering_motif.py, lines 305–341 · score 0.65 · hair shaft cuticle, root sheath, hair follicle, medulla, cortex, motifs
  6. [6] § 3 Results › 3.4 mmVelo reveals the dynamics of TF binding motifs in mouse hair follicle development ↔ mmVelo_tutorial_v2/src/fig_mouse_hair_follicle/03_clustering_motif_vjp.py, lines 404–427 · score 0.65 · hair shaft cuticle, root sheath, hair follicle, medulla, cortex, motifs
  7. [7] § 3 Results › 3.7 mmVelo estimates velocity in missing modalities ↔ mmVelo_tutorial_v2/src/mmvelo_tutorial/viz_human_brain.py, lines 94–124 · score 0.64 · excitatory neuron lineage, GluN, nIPC, pseudotime, mmVelo, cells
  8. [8] § 2 Methods › 2.13 Preprocessing of data › 2.13.2 SHARE-seq mouse skin (hair follicle) data ↔ mmVelo_tutorial_v2/src/fig_mouse_hair_follicle/02_clustering_heatmap.py, lines 34–65 · score 0.61 · hair shaft cuticle, Hair follicle, TAC, medulla, cortex, mouse
  9. [9] § 2 Methods › 2.13 Preprocessing of data › 2.13.2 SHARE-seq mouse skin (hair follicle) data ↔ mmVelo_tutorial_v2/src/fig_mouse_hair_follicle/03_clustering_motif.py, lines 305–341 · score 0.61 · hair shaft cuticle, Hair follicle, TAC, medulla, cortex, mouse
  10. [10] § 2 Methods › 2.6 Training procedure ↔ mmVelo_tutorial_v2/src/mmvelo_tutorial/train_human_brain.py, lines 257–344 · score 0.60 · fine tuning, smoothed profiles, validation, trained, ELBO, reconstruct
  11. [11] § 2 Methods › 2.6 Training procedure ↔ mmVelo_tutorial_v2/src/mmvelo_tutorial/train_mouse_brain.py, lines 139–218 · score 0.59 · fine tuning, smoothed profiles, validation, trained, ELBO, reconstruct
  12. [12] § 2 Methods › 2.6 Training procedure ↔ mmVelo_tutorial/src/streamlineplot.py, lines 1026–1114 · score 0.57 · steady state model, mRNA, RNA velocity, ratio, unspliced, dynamics
  13. [13] § 2 Methods › 2.6 Training procedure ↔ mmVelo_tutorial_v2/src/mmvelo_multi/streamlineplot.py, lines 1028–1116 · score 0.57 · steady state model, mRNA, RNA velocity, ratio, unspliced, dynamics
  14. [14] § 2 Methods › 2.3 Fine-tuning decoders for smoothed profile reconstruction ↔ mmVelo_tutorial_v2/src/mmvelo_tutorial/train_human_brain.py, lines 257–344 · score 0.56 · fine tuned, smoothed profiles, predict, neighborhood, reconstruction, mmVelo
  15. [15] § 2 Methods › 2.3 Fine-tuning decoders for smoothed profile reconstruction ↔ mmVelo_tutorial_v2/src/mmvelo_tutorial/train_mouse_brain.py, lines 139–218 · score 0.55 · fine tuned, smoothed profiles, predict, reconstruction, mmVelo, cell
  16. [16] § 3 Results › 3.7 mmVelo estimates velocity in missing modalities ↔ mmVelo_tutorial_v2/tutorial_2_human_brain_missing_modality.ipynb, lines 1–59 · score 0.54 · human cortical development, scRNA, scATAC, missing modalities, chromatin velocity, mmVelo
  17. [17] § 2 Methods › 2.11 Inferring TF-peak regulatory relationships using chromatin velocity ↔ mmVelo_tutorial_v2/src/fig_mouse_hair_follicle/05_GRN_inference_pycistopic_res.py, lines 56–98 · score 0.54 · putative regulator TFs, peak cluster, Leiden, motif, mmVelo
  18. [18] § 2 Methods › 2.11 Inferring TF-peak regulatory relationships using chromatin velocity ↔ mmVelo_tutorial_v2/src/fig_mouse_hair_follicle/05_GRN_inference_1st.py, lines 143–200 · score 0.53 · importance scores, mRNA, GRNBoost2, regulate, TFs, Leiden
  19. [19] § 2 Methods › 2.10 Quantification of velocity uncertainty ↔ mmVelo_tutorial_v2/src/mmvelo_multi/velocity_off_manifold.py, lines 1–29 · score 0.53 · manifold instability, latent dynamics, fluctuation, posterior, uncertainty, Quantification
  20. [20] § 2 Methods › 2.10 Quantification of velocity uncertainty ↔ mmVelo_tutorial_v2/src/mmvelo_multi/velocity_off_manifold_mod.py, lines 1–37 · score 0.52 · manifold components, velocity samples, instability, space, Quantification, uncertainty
  21. [21] § 2 Methods › 2.8 Benchmarking against velocity estimation methods ↔ mmVelo_tutorial_v2/src/fig_mouse_hair_follicle/03_clustering_motif_vjp.py, lines 404–427 · score 0.51 · hair shaft cuticle, mouse hair follicle, cortex, score, Pseudotime, cells

Paper

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The authors' code

Python · 490 lines · 22 KB · no license · 2 matches

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It can be read at the source: mmVelo_tutorial_v2/src/mmvelo_multi/velocity_off_manifold_mod.py.

Overview

Authors: Satoshi Nomura1, Yasuhiro Kojima2, Kodai Minoura1, Shuto Hayashi3, Ko Abe3, Haruka Hirose3, Teppei Shimamura1,3
ORCID iDs: Satoshi Nomura, Yasuhiro Kojima, Shuto Hayashi, Teppei Shimamura
  1. Japanese Red Cross Aichi Medical Center, Nagoya Daiichi Hospital, Nagoya, Japan
  2. Laboratory of Computational Life Science, National Cancer Center Research Institute, Tokyo, Japan
  3. Department of Computational and Systems Biology, Division of Biological Data Science, Medical Research Laboratory, Institute for Integrated Research, Institute of Science Tokyo, Tokyo, Japan
Institutions: Japanese Red Cross Nagoya Daiichi Hospital (Japan); Nagoya University (Japan); National Cancer Centre Japan (Japan); National Cancer Center (United States); Institute of Science Tokyo (Japan)
Journal: Bioinformatics (Oxford, England), volume 42, issue 9, article btag652
Dates: received 19 November 2025; accepted 19 August 2026; published online 31 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/bioinformatics/btag652 · PMID 42675615 · PMCID PMC13585172 · OpenAlex W4405659442
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.12.11.628059
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
MeSH: Single-Cell Analysis*, Software*, Animals, Brain, Chromatin, Humans, Mice, Multiomics, Transcriptome (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: JSPS KAKENHI (JP23H04938, JP26K03026, JP22H04925); Japan Agency for Medical Research and Development (AMED) (JP26gm2010002, JP26zf0127012, JP26wm0325068, JP256f0137003, JP26wm0625519, JP26nk0101112, JP26tm0424226); Japan Society for the Promotion of Science (JSPS); Medical Research Center Initiative for High Depth Omics at Institute of Science Tokyo; Multilayered Stress Diseases (JPMXP1323015483)
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

Motivation: Single-cell multiomics reveals regulatory relationships across biological layers but captures only static snapshots, obscuring the dynamics coordinated across modalities. RNA velocity predicts transcriptome dynamics, yet cannot be extended to other layers such as the regulome, leaving chromatin accessibility dynamics unresolved.

Results: We developed mmVelo (multimodal velocity of single cells), a deep generative model that infers cell state dynamics from spliced and unspliced mRNA and projects them onto other modalities, yielding chromatin velocity at single-peak resolution. In developing mouse brain, mmVelo accurately recovered accessibility dynamics; in mouse skin, it identified transcription factors regulating accessibility. Decomposing posterior velocity variability into manifold-aligned and off-manifold components revealed modality-specific uncertainty structure, with chromatin fluctuation elevated near lineage branching. Using multiomics data as a bridge, mmVelo inferred the dynamics of missing modalities from single-modal human brain data.

Availability and implementation: Source code is freely available under the MIT license at https://github.com/nomuhyooon/mmVelo; the version and test data used here are archived at https://doi.org/10.5281/zenodo.20103609.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 21 matches between paragraphs and lines of code.

nomuhyooon/mmVelo

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 3dbe5d18cad3e2f020df8e8b74f3068f669b3b7f, 10 June 2026
Languages: Python (81), Jupyter (3)
Size: 104 files, 84 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (mmVelo_tutorial/pyproject.toml, mmVelo_tutorial/setup.cfg, mmVelo_tutorial_v2/pyproject.toml, mmVelo_tutorial_v2/setup.cfg), 3 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (76 files), Scanpy (65 files), Matplotlib (59 files), SciPy (55 files), pandas (53 files), anndata (45 files), scVelo (40 files), seaborn (40 files), PyTorch (35 files), UMAP (26 files), PyTorch Lightning (21 files), statsmodels (20 files), scikit-learn (19 files), Pyro (2 files), pysam (2 files), BEDTools (1 file), Pillow (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
85 files, not copied: shown from their source

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At the source: github.com/nomuhyooon/mmVelo

Zenodo 20103609

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (76 files), Scanpy (65 files), Matplotlib (59 files), SciPy (55 files), pandas (53 files), anndata (45 files), scVelo (40 files), seaborn (40 files), PyTorch (35 files), UMAP (26 files), PyTorch Lightning (21 files), statsmodels (20 files), scikit-learn (19 files), Pyro (2 files), pysam (2 files), BEDTools (1 file), Pillow (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
85 files
At the source: doi.org/10.5281/zenodo.20103609

Availability and implementation

Source code is freely available under the MIT license at https://github.com/nomuhyooon/mmVelo; the version and test data used here are archived at https://doi.org/10.5281/zenodo.20103609.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 168 scripts, each with its path and the digest of its content;
  • 21 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The 10× embryonic mouse brain dataset was downloaded from the 10× website at https://www.10xgenomics.com/datasets/fresh-embryonic-e-18-mouse-brain-5-k-1-standard-2-0-0. The SHARE-seq mouse skin dataset (Ma et al. 2020c) was downloaded from GEO (GSE140203 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140203)). The 10× human cortical development dataset was downloaded from GEO (GSE162170 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162170)). The full source code is publicly available at https://github.com/nomuhyooon/mmVelo, and an archived version corresponding to this study is available at https://doi.org/10.5281/zenodo.20103609. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 9 MeSH terms, 5 funders, 98 references.

Cite

This paper

Nomura, S., Kojima, Y., Minoura, K., Hayashi, S., Abe, K., Hirose, H., & Shimamura, T. (2026). mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities. Bioinformatics (Oxford, England), 42(9), btag652. https://doi.org/10.1093/bioinformatics/btag652

BibTeX

@article{nomura2026mmvelo,
author = {Nomura, Satoshi and Kojima, Yasuhiro and Minoura, Kodai and Hayashi, Shuto and Abe, Ko and Hirose, Haruka and Shimamura, Teppei},
title = {{mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities}},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = aug,
volume = {42},
number = {9},
pages = {btag652},
publisher = {Oxford University Press},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btag652},
url = {https://doi.org/10.1093/bioinformatics/btag652},
pmid = {42675615},
pmcid = {PMC13585172}
}

RIS

TY - JOUR
AU - Nomura, Satoshi
AU - Kojima, Yasuhiro
AU - Minoura, Kodai
AU - Hayashi, Shuto
AU - Abe, Ko
AU - Hirose, Haruka
AU - Shimamura, Teppei
TI - mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities
T2 - Bioinformatics (Oxford, England)
J2 - Bioinformatics
PY - 2026
DA - 2026/08/01
VL - 42
IS - 9
SP - btag652
SN - 1367-4803
PB - Oxford University Press
DO - 10.1093/bioinformatics/btag652
UR - https://doi.org/10.1093/bioinformatics/btag652
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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