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Subgraph extraction and graph representation learning for single cell Hi-C imputation and clustering.

ABSTRACT: Single-cell Hi-C (scHi-C) technology enables the investigation of 3D chromatin structure variability across individual cells. However, the analysis of scHi-C data is challenged by a large number of missing values. Here, we present a scHi-C data imputation model HiC-SGL, based on Subgraph extraction and graph representation learning. HiC-SGL can also learn informative low-dimensional embeddings of cells. We demonstrate that our method surpasses existing methods in terms of imputation accuracy and clustering performance by various metrics.

SUBMITTER: Zheng J 

PROVIDER: S-EPMC10691963 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Subgraph extraction and graph representation learning for single cell Hi-C imputation and clustering.

Zheng Jiahao J   Yang Yuedong Y   Dai Zhiming Z  

Briefings in bioinformatics 20231101 1

Single-cell Hi-C (scHi-C) technology enables the investigation of 3D chromatin structure variability across individual cells. However, the analysis of scHi-C data is challenged by a large number of missing values. Here, we present a scHi-C data imputation model HiC-SGL, based on Subgraph extraction and graph representation learning. HiC-SGL can also learn informative low-dimensional embeddings of cells. We demonstrate that our method surpasses existing methods in terms of imputation accuracy and  ...[more]

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