Subgraph extraction and graph representation learning for single cell Hi-C imputation and clustering.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 38040494.
- Also identified by DOI 10.1093/bib/bbad379 and PMC identifier 10691963.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Chromatin