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Published on: April 21, 2023
scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data
Siqi Shen1, Ye Zheng2, Sündüz Keleş1,3
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA.
Summary:
Quantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data.
Availability And Implementation:
scGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
