Time course regulatory analysis based on paired expression and chromatin accessibility data
- 1Department of Statistics, Stanford University, Stanford, California 94305, USA;
- 2CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China;
- 3Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China;
- 4Department of Biomedical Data Science, Bio-X Program, Center for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA
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↵5 These authors contributed equally to this work.
Abstract
A time course experiment is a widely used design in the study of cellular processes such as differentiation or response to stimuli. In this paper, we propose time course regulatory analysis (TimeReg) as a method for the analysis of gene regulatory networks based on paired gene expression and chromatin accessibility data from a time course. TimeReg can be used to prioritize regulatory elements, to extract core regulatory modules at each time point, to identify key regulators driving changes of the cellular state, and to causally connect the modules across different time points. We applied the method to analyze paired chromatin accessibility and gene expression data from a retinoic acid (RA)–induced mouse embryonic stem cells (mESCs) differentiation experiment. The analysis identified 57,048 novel regulatory elements regulating cerebellar development, synapse assembly, and hindbrain morphogenesis, which substantially extended our knowledge of cis-regulatory elements during differentiation. Using single-cell RNA-seq data, we showed that the core regulatory modules can reflect the properties of different subpopulations of cells. Finally, the driver regulators are shown to be important in clarifying the relations between modules across adjacent time points. As a second example, our method on Ascl1-induced direct reprogramming from fibroblast to neuron time course data identified Id1/2 as driver regulators of early stage of reprogramming.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.257063.119.
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Freely available online through the Genome Research Open Access option.
- Received September 23, 2019.
- Accepted March 9, 2020.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
