scKINETICS: Inference of regulatory velocity with single-cell transcriptomics data Journal Article
| Authors: | Burdziak, C.; Zhao, C. J.; Haviv, D.; Alonso-Curbelo, D.; Lowe, S. W.; Pe’er, D. |
| Article Title: | scKINETICS: Inference of regulatory velocity with single-cell transcriptomics data |
| Abstract: | Motivation: Transcriptional 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. Results: We 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. © The Author(s) 2023. Published by Oxford University Press. |
| Keywords: | gene expression profiling; pancreatitis; benchmarking; transcriptome; acute disease; humans; human |
| Journal Title: | Bioinformatics |
| Volume: | 39 |
| Issue: | Suppl. 1 |
| ISSN: | 1367-4803 |
| Publisher: | Oxford University Press |
| Date Published: | 2023-06-01 |
| Start Page: | i394 |
| End Page: | i403 |
| Language: | English |
| DOI: | 10.1093/bioinformatics/btad267 |
| PUBMED: | 37387147 |
| PROVIDER: | scopus |
| PMCID: | PMC10311321 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Dana Pe’er -- Source: Scopus |
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