Computational Methods for Single-cell Multi-omics Integration and Alignment.

Computational Methods for Single-cell Multi-omics Integration and Alignment.
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单细胞多组学整合和比对的计算方法。

DOI:
10.1016/j.gpb.2022.11.013
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发表时间:
2022-10
影响因子:
9.5
通讯作者:
Garmire, Lana X.
Garmire, Lana X.
中科院分区:
生物学2区
文献类型:
--
作者:
Stanojevic, Stefan;Li, Yijun;Ristivojevic, Aleksandar;Garmire, Lana X.

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最近开发的生成单细胞基因组数据的技术在生物学领域产生了革命性的影响。多组学分析为了解细胞状态和生物过程提供了更多机会。然而,整合具有截然不同的维度和统计特性的不同组学数据的问题仍然相当具有挑战性。越来越多的计算工具正在为这项任务开发,利用从机器翻译到网络理论的各种思想,代表了生物学和数据科学接口的另一个前沿。我们本次综述的目标是对用于整合单细胞多组学数据的计算技术进行全面、最新的调查,同时使非专家受众也能理解每种算法背后的概念。
Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.
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