小柯机器人

美国卡内基梅隆大学Jian Ma研究小组实现多尺度和综合单细胞Hi-C分析。相关论文于2021年10月11日在线发表在《自然—生物技术》杂志上。

研究人员报告了Higashi，一种基于超图表示学习的算法，可以将单细胞之间的潜在相关性纳入其中，从而加强接触图的整体归纳。Higashi优于现有的单细胞Hi-C（scHi-C）数据的嵌入和估算方法，能够识别单细胞中的多尺度三维基因组特征，如区室化和TAD样域边界，能够细化划定其细胞间的变化。此外，与单独分析两种模式相比，Higashi可以将同一细胞中联合剖析的表观基因组信号纳入超图表示学习框架，从而改善单核甲基3C数据的嵌入。

在人类前额叶皮层的scHi-C数据集中，Higashi确定了三维基因组特征和细胞类型特定基因调控之间的联系。Higashi也有可能被扩展到分析单细胞多向染色质相互作用和其他多模态单细胞全向数据上。

据悉，scHi-C可以识别细胞间三维（3D）染色质组织的变异性，但相互作用的稀疏性构成了分析的挑战。

附：英文原文

Title: Multiscale and integrative single-cell Hi-C analysis with Higashi

Author: Zhang, Ruochi, Zhou, Tianming, Ma, Jian

Issue&Volume: 2021-10-11

Abstract: Single-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data. Single-cell Hi-C analysis with Higashi identifies cell-to-cell variability of 3D genome organization.

DOI: 10.1038/s41587-021-01034-y

Source: https://www.nature.com/articles/s41587-021-01034-y

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex