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Activity Number:
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428
- Clustering and Dimension-Reduction Methods: From Omics to Single-Cell Data
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Type:
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Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #323464
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Title:
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ScGAD: Single-Cell Gene Associating Domain Scores for Exploratory Analysis of ScHi-C Data
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Author(s):
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Siqi Shen* and Sunduz Keles and Ye Zheng
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Companies:
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University of Wisconsin - Madison and University of Wisconsin, Madison and Fred Hutchinson Cancer Research Center
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Keywords:
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single-cell Hi-C;
single-cell genomics;
data integration;
multimodal analysis
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Abstract:
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Quantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and to 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 level while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. scGAD enables identifying genes with significant chromatin interactions within and between cell types. 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 projection enables fast cell-type annotation for 3D genomics data and promotes integrative analyses of 3D genome structure, epigenomics, and transcriptomics to decipher gene regulation mechanisms at single-cell resolution.
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Authors who are presenting talks have a * after their name.
