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, Kaifu Chen
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.