Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Genomics Proteomics Bioinformatics. 2022 Oct;20(5):836-849. doi: 10.1016/j.gpb.2022.11.013. Epub 2022 Dec 26.
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.
最近开发的单细胞基因组数据生成技术在生物学领域产生了革命性的影响。多组学分析提供了更大的机会来理解细胞状态和生物过程。然而，将具有非常不同维度和统计特性的不同组学数据进行整合仍然是一个极具挑战性的问题。为了解决这个问题，已经开发出越来越多的计算工具，这些工具利用了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界面的另一个前沿领域。我们在这篇综述中的目标是提供一个全面的、最新的单细胞多组学数据整合计算技术的调查，同时使每个算法背后的概念能够被非专业人士理解。
