Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data

• DOI: 10.1038/s41467-020-14974-x
• Publication Date: 2020-03-03T11:03:21Z
• Authors: Haitham Ashoor; Xiaowen Chen; Wojciech Rosikiewicz; Jiahui Wang; Albert Cheng; Ping Wang; Yijun Ruan; Sheng Li
• Keywords: Data Analysis; 0301 basic medicine; 570; JGM; Science; Q; JAXCC; Gene Expression; Reproducibility of Results; Genomics; Article; Chromatin; Markov Chains; 004; Epigenome; 03 medical and health sciences; Computer Graphics; Cluster Analysis; Humans; Neural Networks, Computer; K562 Cells; Algorithms; Unsupervised Machine Learning

AbstractChromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub-compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training.