Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations

• Published in: Nature communications Vol. 15; no. 1; pp. 8310 - 11
• Main Authors: Zhou, Yufan, Li, Tian, Choppavarapu, Lavanya, Fang, Kun, Lin, Shili, Jin, Victor X.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 49; 49/15; 631/114/2397; 631/114/2401; 631/208/177; 631/208/514/1949; 631/67/1347; Algorithms; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Cell Line, Tumor; Cellular structure; Chromatin; Chromatin - genetics; Chromatin - metabolism; Computational Biology - methods; Context; Data integration; Female; Gene expression; Gene Expression Regulation, Neoplastic; Heterogeneity; Humanities and Social Sciences; Humans; Integration; multidisciplinary; RNA, Small Cytoplasmic - genetics; RNA-Seq - methods; Science; Science (multidisciplinary); Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-Cell Gene Expression Analysis; Subpopulations

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-con...