Machine learning uncovers cell identity regulator by histone code

Bo Xia, Dongyu Zhao, Guangyu Wang, Min Zhang, Jie Lv, Alin S. Tomoiaga, Yanqiang Li, Xin Wang, Shu Meng, John P. Cooke, Qi Cao (), Lili Zhang () and Kaifu Chen ()
Additional contact information
Bo Xia: Houston Methodist Research Institute
Dongyu Zhao: Houston Methodist Research Institute
Guangyu Wang: Houston Methodist Research Institute
Min Zhang: Houston Methodist Research Institute
Jie Lv: Houston Methodist Research Institute
Alin S. Tomoiaga: Manhattan College
Yanqiang Li: Houston Methodist Research Institute
Xin Wang: Houston Methodist Research Institute
Shu Meng: Houston Methodist Research Institute
John P. Cooke: Houston Methodist Research Institute
Qi Cao: Robert H. Lurie Comprehensive Cancer Center
Lili Zhang: Houston Methodist Research Institute
Kaifu Chen: Houston Methodist Research Institute

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and their expression regulation. Here, we develop CEFCIG, an artificial intelligent framework to uncover CIGs and further define their master regulators. On the basis of machine learning, CEFCIG reveals unique histone codes for transcriptional regulation of reported CIGs, and utilizes these codes to predict CIGs and their master regulators with high accuracy. Applying CEFCIG to 1,005 epigenetic profiles, our analysis uncovers the landscape of regulation network for identity genes in individual cell or tissue types. Together, this work provides insights into cell identity regulation, and delivers a powerful technique to facilitate regenerative medicine.

Date: 2020
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DOI: 10.1038/s41467-020-16539-4

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