Multi-omics integration in the age of million single-cell data

数据集成 背景(考古学) 组学 可视化 模式 过程(计算) 推论 计算生物学 数据挖掘 数据类型 计算机科学 数据科学 系统生物学 生物信息学 人工智能 生物 操作系统 社会学 古生物学 程序设计语言 社会科学
作者
Zhen Miao,Benjamin D. Humphreys,Andrew P. McMahon,Junhyong Kim
出处
期刊:Nature Reviews Nephrology [Springer Nature]
卷期号:17 (11): 710-724 被引量:120
标识
DOI:10.1038/s41581-021-00463-x
摘要

An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis. Analyses of single-cell, multi-omics datasets have potential to provide new insights into biological processes; however, the integration of these complex datasets represents a considerable challenge. This Review describes the principles underlying the integration of multimodal data measured on the same cell (that is, matched data) and on different cells (unmatched data), outlining developments in computational methods and data visualization approaches.
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