ArticleBioinformatics (Oxford, England)2023
scKINETICS: inference of regulatory velocity with single-cell transcriptomics data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 26 citations in OpenAlex.
- Article
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- CellRank: consistent and data view agnostic fate mapping for single-cell genomics.Nature protocols · 2026Review
- Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.Molecular systems biology · 2026Article
- Simultaneously infer cell pseudotime, velocity field, and gene interaction from multi-branch scRNA-seq data with scPN.NAR genomics and bioinformatics · 2025Article
- A comparison of computational methods for expression forecasting.Genome biology · 2025Article
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
- TRENDY: gene regulatory network inference enhanced by transformer.Bioinformatics (Oxford, England) · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.Nature methods · 2024Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
motivationTranscriptional dynamics are governed by the action of regulatory proteins and are fundamental to systems ranging from normal development to disease. RNA velocity methods for tracking phenotypic dynamics ignore information on the regulatory drivers of gene expression variability through time.
resultsWe introduce scKINETICS (Key regulatory Interaction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit with the simultaneous learning of per-cell transcriptional velocities and a governing gene regulatory network. Fitting is accomplished through an expectation-maximization approach designed to learn the impact of each regulator on its target genes, leveraging biologically motivated priors from epigenetic data, gene-gene coexpression, and constraints on cells' future states imposed by the phenotypic manifold. Applying this approach to an acute pancreatitis dataset recapitulates a well-studied axis of acinar-to-ductal transdifferentiation whilst proposing novel regulators of this process, including factors with previously appreciated roles in driving pancreatic tumorigenesis. In benchmarking experiments, we show that scKINETICS successfully extends and improves existing velocity approaches to generate interpretable, mechanistic models of gene regulatory dynamics. AVAILABILITY AND IMPLEMENTATION: All python code and an accompanying Jupyter notebook with demonstrations are available at http://github.com/dpeerlab/scKINETICS.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.