scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression

Published

June 28, 2024

Author(s)

Ghulam Murtaza, Justin Wagner, Ritambhara Singh

Abstract

Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the complexity of the experimental protocol. We present a deep learning framework, scGrapHiC, that predicts pseudo-bulk scHi-C contact maps using pseudo-bulk scRNA-seq data. Specifically, scGrapHiC performs graph deconvolution to extract genome-wide single-cell interactions from a bulk Hi-C contact map using scRNA-seq as a guiding signal. Our evaluations show that scGrapHiC, trained on seven cell-type co-assay datasets, outperforms typical sequence encoder approaches. For example, scGrapHiC achieves a substantial improvement of in recovering cell-type-specific Topologically Associating Domains over the baselines. It also generalizes to unseen embryo and brain tissue samples. scGrapHiC is a novel method to generate cell-type-specific scHi-C contact maps using widely available genomic signals that enables the study of cell-type-specific chromatin interactions.

Proceedings Title

Bioinformatics Volume 40 Supplement 1 ISMB 2024 Proceedings

Volume

Volume 40

Conference Dates

July 12-16, 2024

Conference Location

Montreal, CA

Conference Title

ISMB 2024

Pub Type

Conferences

Download Paper

https://doi.org/10.1093/bioinformatics/btae223

Local Download

Keywords

Hi-C, single cell, single cell Hi-C

Citation

Murtaza, G. , Wagner, J. and Singh, R. (2024), scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression, Bioinformatics Volume 40 Supplement 1 ISMB 2024 Proceedings , Montreal, CA, [online], https://doi.org/10.1093/bioinformatics/btae223, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957298 (Accessed September 25, 2026)

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