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

Haitham Ashoor, Xiaowen Chen, Wojciech Rosikiewicz, Jiahui Wang, Albert Cheng, Ping Wang, Yijun Ruan and Sheng Li ()
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Haitham Ashoor: The Jackson Laboratory for Genomic Medicine
Xiaowen Chen: The Jackson Laboratory for Genomic Medicine
Wojciech Rosikiewicz: The Jackson Laboratory for Genomic Medicine
Jiahui Wang: The Jackson Laboratory for Genomic Medicine
Albert Cheng: The Jackson Laboratory for Genomic Medicine
Ping Wang: The Jackson Laboratory for Genomic Medicine
Yijun Ruan: The Jackson Laboratory for Genomic Medicine
Sheng Li: The Jackson Laboratory for Genomic Medicine

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

Abstract: Abstract Chromatin 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.

Date: 2020
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DOI: 10.1038/s41467-020-14974-x

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