Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43235, USA.
Imagenetics, Sanford Health, Sioux Falls, SD 57104, USA; Department of Internal Medicine, University of South Dakota, Virmillion, SD 57069, USA.
Trends Biotechnol. 2020 Sep;38(9):1007-1022. doi: 10.1016/j.tibtech.2020.02.013. Epub 2020 Mar 26.
Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses.
单细胞多组学（scMulti-omics）技术发展迅速，能够从同一个细胞中测量多种模式，如 DNA 甲基化、染色质可及性、RNA 表达、蛋白质丰度、基因扰动和空间信息。scMulti-omics 可以全面探索和识别细胞特征，但也对计算方法和工具的发展提出了挑战，以便进行综合分析。在这里，我们回顾了这些综合方法，并总结了用于研究各种 scMulti-omics 数据的现有工具。通过几个案例研究，探讨了在公共领域使用现有工具时的各种功能和实际挑战。最后，我们确定了 scMulti-omics 建模和分析中仍然存在的挑战和未来趋势。
