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ScENCORE: leveraging single-cell epigenetic data to predict genome conformation using graph embedding

ABSTRACT: Recent advances in chromatin architecture profiling technologies, such as single-cell Hi-C (scHi-C), allow us to dissect the heterogeneity of chromosome higher-order structures across diverse cell states and different individuals. However, scHi-C experiments are still expensive and not immediately available for population-scale profiling. Here, we present scENCORE, a computational method, to reconstruct personalized and cell-type-specific higher-order chromatin structures, such as A/B compartments, directly from more cost-effective and widely available single-cell epigenetic data (e.g., scATAC-seq). We apply scENCORE on scATAC-seq data from post-mortem prefrontal cortex brains and demonstrate its utility to 1) project Mega-base scale chromatin regions into lower dimensional space by leveraging graph embedding technologies based on cell-type-specific co-variability patterns, 2) define A/B compartments via unsupervised clustering, 3) perform an alignment algorithm for multi-graph embedding to derive comparable chromatin representations and highlight dynamic chromatin compartments across cell states and individuals. Validated by Hi-C experiments using FACS-sorted cells, scENCORE can faithfully reconstruct cell-type-specific chromatin compartments. Furthermore, scENCORE uniformly constructs chromosome conformation across population-scale scATAC-seq data and discovers key 3D structural switching events associated with psychiatric disorders. In summary, scENCORE allows cost-effective cell-type-specific and personalized reconstruction that delineate higher-order chromatin structures.

ORGANISM(S): Homo sapiens

PROVIDER: GSE216270 | GEO | 2023/09/27

REPOSITORIES: GEO

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ScENCORE: leveraging single-cell epigenetic data to predict genome conformation using graph embedding

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