Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium.
FEBS J. 2019 Apr;286(8):1451-1467. doi: 10.1111/febs.14613. Epub 2018 Aug 30.
During the past decade, the number of novel technologies to interrogate biological systems at the single-cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single-cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high-dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single-cell data in an automated and unbiased way.
在过去的十年中，用于在单细胞水平上检测生物系统的新技术数量呈爆炸式增长。使用各种技术，已经开创了无数种用于在单细胞水平上测量蛋白质组、基因组、转录组和表观基因组的方法。所有这些方法都有一个共同点：它们生成大型和高维数据集，需要先进的计算建模工具来突出和解释这些数据中的有趣模式，从而可能产生新的生物学见解和假设。在这项工作中，我们提供了一种概述，介绍了用于以自动和无偏倚的方式解释各种类型的单细胞数据的计算方法。
