Single-Cell Multiomics

被引:13
|
作者
Flynn, Emily [1 ]
Almonte-Loya, Ana [1 ,2 ]
Fragiadakis, Gabriela K. [1 ,3 ]
机构
[1] Univ Calif San Francisco, CoLabs, San Francisco, CA 94143 USA
[2] Univ Calif San Francisco, Biomed Informat Program, San Francisco, CA USA
[3] Univ Calif San Francisco, Dept Med, Div Rheumatol, San Francisco, CA 94143 USA
来源
ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE | 2023年 / 6卷
基金
美国国家卫生研究院;
关键词
multiomics; integration; single-cell; computation; next-generation sequencing; multimodal; COMPUTATIONAL METHODS; INTEGRATED ANALYSIS; EXPRESSION ANALYSIS; RNA; GENOME; INFERENCE; CHROMATIN; REVEALS; PATHWAY; OMICS;
D O I
10.1146/annurev-biodatasci-020422-050645
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.
引用
收藏
页码:313 / 337
页数:25
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