Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Nucleic Acids Res. 2018 Nov 16;46(20):10546-10562. doi: 10.1093/nar/gky889.
Recent high throughput experimental methods have been used to collect large biomedical omics datasets. Clustering of single omic datasets has proven invaluable for biological and medical research. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here, we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types. In addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic comparison of leading multi-omics and multi-view clustering algorithms. The results highlight key issues regarding the use of single- versus multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the growing use of multi-omics data, we expect these issues to be important for future progress in the field.
最近的高通量实验方法被用于收集大量的生物医学组学数据集。对单个组学数据集进行聚类已被证明对生物和医学研究非常有价值。随着高通量方法成本的降低和进一步的发展，现在可以测量多组学数据。对多组学数据进行聚类有可能揭示出进一步的系统级见解，但也带来了计算和生物学方面的挑战。在这里，我们回顾了多组学聚类的算法，并讨论了应用这些算法的关键问题。我们的综述涵盖了专门为组学数据开发的方法，以及机器学习社区中为联合聚类多种数据类型而开发的通用多视图方法。此外，我们使用 TCGA 的癌症数据进行了广泛的基准测试，涵盖了十种不同的癌症类型，首次对领先的多组学和多视图聚类算法进行了系统比较。结果突出了使用单组学与多组学、聚类策略的选择、通用多视图方法的有效性以及使用近似 p 值来衡量解决方案质量等方面的关键问题。由于多组学数据的使用越来越多，我们预计这些问题将对该领域的未来发展非常重要。
