Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription, and splicing from single-cell RNA sequencing data with TSvelo.
The 8 matches
- [1] § Results › TSvelo can model 3D gene dynamics and predict cell fate on pancreas dataset ↔ FIG8_paper.r, lines 41–130 · score 0.68 · Ngn3 low EP, Ngn3 high EP, Ductal, Beta, pancreas, Alpha
- [2] § Methods › Lineages segmentation and pseudotime initialization ↔ TSvelo/TSvelo_branch.py, lines 27–47 · score 0.62 · PAGA graph, detect lineages, shortest, Edges, cluster
- [3] § Results › TSvelo can capture gene dynamics well and predict cell fate on mouse brain data ↔ TSvelo/TSvelo_pp.py, lines 16–50 · score 0.59 · Radial Glia, mouse brain, IPCs, RNA
- [4] § Results › Estimate RNA velocity with TSVelo ↔ functions.R, lines 753–786 · score 0.59 · analytical solutions, degradation rate, splicing rate, transcription rate, trajectory, ODE
- [5] § Methods › Preprocessing for scRNA-seq data ↔ dataSimulationScVelo.r, lines 43–107 · score 0.58 · highly variable genes, variance, scVelo, seq, clustering, cell
- [6] § Methods › Optimizing global time and Neural ODE in EM framework ↔ TSvelo_run.py, lines 37–77 · score 0.58 · ChEA, Neural ODE, ENCODE, databases, TF, trained
- [7] § Results › TSvelo can predict cell fate and model lineage-specific gene dynamics for multi-lineage tasks ↔ functions.R, lines 944–985 · score 0.54 · degradation rates, splicing rates, scVelo, transcriptional rates, trajectory, branches
- [8] § Methods › Optimizing global time and Neural ODE in EM framework ↔ TSvelo/TSvelo_model.py, lines 127–187 · score 0.53 · Adam, gradient, trained, optimizer, loss, zero
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
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The authors' code
R · 1,298 lines · 58 KB · no license · 2 matches
functions.R at commit 73b6f0b, no license · at the source
Overview
- State Key Laboratory of Biopharmaceutical Preparation and Delivery, Institute of Process Engineering, Chinese Academy of Sciences Beijing China
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China Shanghai China
Abstract
RNA velocity approaches fit gene dynamics and infer cell fate by modeling the splicing process using single-cell RNA sequencing (scRNA-seq) data. However, due to the short time scale of splicing, high noise, and large complexity of data, existing RNA velocity methods often fail to precisely capture the complex velocity dynamics for individual genes and single cells, which makes their downstream analysis less reliable and less robust. We propose TSvelo, a comprehensive RNA velocity mathematics framework that can model the cascade of gene regulation, Transcription and Splicing using highly interpretable neural ordinary differential equations. TSvelo can precisely capture the transcription–unspliced–
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 8 matches between paragraphs and lines of code.
lijc0804/TSvelo
6e0ab8264e02aa511576278219fd7c1c65efec80, 8 September 2026Availability: 1 check, the latest on 26 September 2026: the link answers
- 26 September 2026: the link answers
11 files
- TSvelo/
TSvelo_branch.py — Python, 226 lines, 1 match - TSvelo/
TSvelo_concate.py — Python, 124 lines - TSvelo/
TSvelo_model.py — Python, 317 lines, 1 match - TSvelo/
TSvelo_pp.py — Python, 84 lines, 1 match - TSvelo/
TSvelo_pp_utils.py — Python, 212 lines - TSvelo/
TSvelo_utils.py — Python, 589 lines - TSvelo_analysis.ipynb — Jupyter, 102 lines
- TSvelo_branch_analysis.i
pynb — Jupyter, 47 lines - TSvelo_run.py — Python, 77 lines, 1 match
- LICENSE.txt — License, 21 lines
- readme.MD — Text, 45 lines
elenasabbioni/bayvel_notebooks
73b6f0b05eaeddad1d155d1b1dce7b8f1aa88a4f, 26 October 2025Availability: 1 check, the latest on 26 September 2026: the link answers
- 26 September 2026: the link answers
26 files, not copied: shown from their source
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- FIG1_paper.r — R, 299 lines, shown from its source
- FIG3_paper.r — R, 120 lines, shown from its source
- FIG4_paper.r — R, 271 lines, shown from its source
- FIG5_paper.r — R, 214 lines, shown from its source
- FIG6_paper.r — R, 158 lines, shown from its source
- FIG7_paper.r — R, 272 lines, shown from its source
- FIG8_paper.r — R, 131 lines, 1 match, shown from its source
- MCMC_BayVel.jl — Julia, 429 lines, shown from its source
- TABLE1_paper.r — R, 152 lines, shown from its source
- TABLE2_paper.r — R, 168 lines, shown from its source
- TABLE3_paper.r — R, 151 lines, shown from its source
- TABLE4_paper.r — R, 151 lines, shown from its source
- TABLE5_paper.r — R, 58 lines, shown from its source
- computeLikelihood.r — R, 106 lines, shown from its source
- computeWAIC.R — R, 132 lines, shown from its source
- dataRealSubgroups.R — R, 156 lines, shown from its source
- dataSimulationBayVel.r — R, 314 lines, shown from its source
- dataSimulationScVelo.r — R, 108 lines, 1 match, shown from its source
- exporting_realData_toCSV
.ipynb — Jupyter, 115 lines, shown from its source - functions.R — R, 1,298 lines, 2 matches, shown from its source
- loadBayVel_results.r — R, 138 lines, shown from its source
- loadScVelo_results.r — R, 114 lines, shown from its source
- plotSimulation.r — R, 98 lines, shown from its source
- scVelo_onRealData.ipynb — Jupyter, 128 lines, shown from its source
- scVelo_onSimulatedData.i
pynb — Jupyter, 181 lines, shown from its source - README.md — Text, 2 lines, shown from its source
Code availability statement
TSvelo is implemented in Python. The source code can be downloaded from the GitHub repository, https://
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.
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- 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 34 scripts, each with its path and the digest of its content;
- 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
Datasets cited
- figshare:20780344 — at figshare; found in “Data availability”
- multivelo.readthedocs.io
— at multivelo.readthedocs.io; found in “Data availability”
Data availability
The pancreatic endocrinogenesis dataset comprises the single-cell RNA-seq (10X) data of pancreatic epithelial and Ngn3-Venus fusion cells sampled from mouse embryonic day 15.5, which could be loaded using scVelo's package scvelo.datasets.pancreas
The following previously published datasets were used:
QinQ 2022larry_invitro_adata_
TritschlerS 2019Comprehensive single cell mRNA profiling reveals a detailed roadmap for pancreatic endocrinogenesisNCBI Gene Expression OmnibusGSE13218810.1242/
Pijuan-SalaB GriffithsJ 2018Timecourse single-cell RNAseq of whole mouse embryos harvested between days 6.5 and 8.5 of developmentArrayExpressE
10x Genomics 2020Fresh Embryonic E18 Mouse Brain (5k)10x Genomicsfresh-embryonic-
LinnarssonS 2016RNA-seq analysis of single cells of the oligodendrocyte lineage from nine distinct regions of the anterior-posterior and dorsal-ventral axis of the mouse juvenile central nervous systemNCBI Gene Expression OmnibusGSE75330
WeinrebC Rodriguez-FraticelliA CamargoF KleinAM 2019Lineage tracing on transcriptional landscapes links state to fate during differentiationNCBI Gene Expression OmnibusGSE14080210.1126/
HochgernerH ZeiselA LönnerbergP LinnarssonS 2017Transcriptome analysis of single cells from the mouse dentate gyrusNCBI Gene Expression OmnibusGSE95753
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 9 MeSH terms, 2 funders, 60 references.
Cite
This paper
Li, J., Wang, Z., Shen, H.-B., & Yuan, Y. (2026). Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription, and splicing from single-cell RNA sequencing data with TSvelo. eLife, 14, RP108950. https://
BibTeX
@article{li2026comprehen
author = {Li, Jiachen and Wang, Zhe and Shen, Hong-Bin and Yuan, Ye},
title = {{Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription, and splicing from single-cell RNA sequencing data with TSvelo}},
journal = {eLife},
year = {2026},
month = sep,
volume = {14},
pages = {RP108950},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/
url = {https://
pmid = {42742132},
pmcid = {PMC13577664}
}
RIS
TY - JOUR
AU - Li, Jiachen
AU - Wang, Zhe
AU - Shen, Hong-Bin
AU - Yuan, Ye
TI - Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription, and splicing from single-cell RNA sequencing data with TSvelo
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/
VL - 14
SP - RP108950
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/
UR - https://
LA - en
ER -
CSL-JSON
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