scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression.
Bioinformatics
; 40(Suppl 1): i490-i500, 2024 06 28.
Article
em En
| MEDLINE
| ID: mdl-38940151
ABSTRACT
SUMMARY:
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 23.2% 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. AVAILABILITY AND IMPLEMENTATION The GitHub link https//github.com/rsinghlab/scGrapHiC contains the source code of scGrapHiC and associated scripts to preprocess publicly available datasets to produce the results and visualizations we have discuss in this manuscript.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cromatina
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Análise de Célula Única
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Aprendizado Profundo
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article