scKINETICS: inference of regulatory velocity with single-cell transcriptomics data.
scKINETICS: inference of regulatory velocity with single-cell transcriptomics data.
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DOI：
10.1093/bioinformatics/btad267
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发表时间：
2023-06-30
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转录动力学受调节蛋白的作用控制，是从正常发育到疾病等系统的基础。用于跟踪表型动态的 RNA 速度方法忽略了基因表达随时间变化的调控驱动因素的信息。我们引入了 scKINETICS（推断细胞速度的关键调控相互作用网络），这是一种基因表达变化的动态模型，适合同时学习每细胞转录速度和控制基因调控网络。拟合是通过期望最大化方法来完成的，该方法旨在了解每个调节器对其目标基因的影响，利用来自表观遗传数据、基因-基因共表达的生物动机先验以及表型流形对细胞未来状态的限制。将这种方法应用于急性胰腺炎数据集，概括了经过充分研究的腺泡到导管转分化的轴，同时提出了该过程的新调节因子，包括先前在驱动胰腺肿瘤发生中发挥作用的因素。在基准测试实验中，我们表明 scKINETICS 成功扩展和改进了现有的速度方法，以生成可解释的基因调控动力学机械模型。所有 python 代码和随附的带有演示的 Jupyter 笔记本均可在 http://github.com/dpeerlab/scKINETICS 上获取。
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. 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. All python code and an accompanying Jupyter notebook with demonstrations are available at http://github.com/dpeerlab/scKINETICS.
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影响因子：
46.9
作者：
Setty M;Tadmor MD;Reich-Zeliger S;Angel O;Salame TM;Kathail P;Choi K;Bendall S;Friedman N;Pe'er D
通讯作者：
Pe'er D
影响因子：
64.5
作者：
Levine JH;Simonds EF;Bendall SC;Davis KL;Amir el-AD;Tadmor MD;Litvin O;Fienberg HG;Jager A;Zunder ER;Finck R;Gedman AL;Radtke I;Downing JR;Pe'er D;Nolan GP
通讯作者：
Nolan GP
影响因子：
14.9
作者：
Dong, Xin;Tang, Ke;Xu, Yunfan;Wei, Hailin;Han, Tong;Wang, Chenfei
通讯作者：
Wang, Chenfei
影响因子：
16.6
作者：
通讯作者：
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影响因子：
30.8
作者：
Kawaguchi, Y;Cooper, B;Wright, CVE
通讯作者：
Wright, CVE