TY - JOUR
T1 - scENCORE
T2 - leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding
AU - Duan, Ziheng
AU - Xu, Siwei
AU - Srinivasan, Shushrruth Sai
AU - Hwang, Ahyeon
AU - Lee, Che Yu
AU - Yue, Feng
AU - Gerstein, Mark
AU - Luan, Yu
AU - Girgenti, Matthew
AU - Zhang, Jing
N1 - Publisher Copyright:
© 2024 Oxford University Press. All rights reserved.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.
AB - Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.
KW - chromatin compartments
KW - graph embedding
KW - single-cell epigenetics
UR - http://www.scopus.com/inward/record.url?scp=85188167066&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85188167066&partnerID=8YFLogxK
U2 - 10.1093/bib/bbae096
DO - 10.1093/bib/bbae096
M3 - Article
C2 - 38493342
AN - SCOPUS:85188167066
SN - 1467-5463
VL - 25
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 2
M1 - bbae096
ER -