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scKINETICS: inference of regulatory velocity with single-cell transcriptomics data

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...

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Autores principales: Burdziak, Cassandra, Zhao, Chujun Julia, Haviv, Doron, Alonso-Curbelo, Direna, Lowe, Scott W, Pe’er, Dana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311321/
https://www.ncbi.nlm.nih.gov/pubmed/37387147
http://dx.doi.org/10.1093/bioinformatics/btad267
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author Burdziak, Cassandra
Zhao, Chujun Julia
Haviv, Doron
Alonso-Curbelo, Direna
Lowe, Scott W
Pe’er, Dana
author_facet Burdziak, Cassandra
Zhao, Chujun Julia
Haviv, Doron
Alonso-Curbelo, Direna
Lowe, Scott W
Pe’er, Dana
author_sort Burdziak, Cassandra
collection PubMed
description 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. 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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spelling pubmed-103113212023-07-01 scKINETICS: inference of regulatory velocity with single-cell transcriptomics data Burdziak, Cassandra Zhao, Chujun Julia Haviv, Doron Alonso-Curbelo, Direna Lowe, Scott W Pe’er, Dana Bioinformatics Regulatory and Functional Genomics 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. AVAILABILITY AND IMPLEMENTATION: All python code and an accompanying Jupyter notebook with demonstrations are available at http://github.com/dpeerlab/scKINETICS. Oxford University Press 2023-06-30 /pmc/articles/PMC10311321/ /pubmed/37387147 http://dx.doi.org/10.1093/bioinformatics/btad267 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Regulatory and Functional Genomics
Burdziak, Cassandra
Zhao, Chujun Julia
Haviv, Doron
Alonso-Curbelo, Direna
Lowe, Scott W
Pe’er, Dana
scKINETICS: inference of regulatory velocity with single-cell transcriptomics data
title scKINETICS: inference of regulatory velocity with single-cell transcriptomics data
title_full scKINETICS: inference of regulatory velocity with single-cell transcriptomics data
title_fullStr scKINETICS: inference of regulatory velocity with single-cell transcriptomics data
title_full_unstemmed scKINETICS: inference of regulatory velocity with single-cell transcriptomics data
title_short scKINETICS: inference of regulatory velocity with single-cell transcriptomics data
title_sort sckinetics: inference of regulatory velocity with single-cell transcriptomics data
topic Regulatory and Functional Genomics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311321/
https://www.ncbi.nlm.nih.gov/pubmed/37387147
http://dx.doi.org/10.1093/bioinformatics/btad267
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