Nguyen Hung, Tran Duc, Tran Bang, Roy Monikrishna, Cassell Adam, Dascalu Sergiu, Draghici Sorin, Nguyen Tin
Department of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.
Department of Computer Science, Wayne State University, Detroit, MI, United States.
Front Oncol. 2021 Oct 20;11:725133. doi: 10.3389/fonc.2021.725133. eCollection 2021.
Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. The treatment options, as well as treatment success, are highly dependent on the correct subtyping of individual patients. With the advancement of high-throughput platforms, we have the opportunity to differentiate among cancer subtypes from a holistic perspective that takes into consideration phenomena at different molecular levels (mRNA, methylation, etc.). This demands powerful integrative methods to leverage large multi-omics datasets for a better subtyping. Here we introduce Subtyping Multi-omics using a Randomized Transformation (SMRT), a new method for multi-omics integration and cancer subtyping. SMRT offers the following advantages over existing approaches: (i) the scalable analysis pipeline allows researchers to integrate multi-omics data and analyze hundreds of thousands of samples in minutes, (ii) the ability to integrate data types with different numbers of patients, (iii) the ability to analyze un-matched data of different types, and (iv) the ability to offer users a convenient data analysis pipeline through a web application. We also improve the efficiency of our ensemble-based, perturbation clustering to support analysis on machines with memory constraints. In an extensive analysis, we compare SMRT with eight state-of-the-art subtyping methods using 37 TCGA and two METABRIC datasets comprising a total of almost 12,000 patient samples from 28 different types of cancer. We also performed a number of simulation studies. We demonstrate that SMRT outperforms other methods in identifying subtypes with significantly different survival profiles. In addition, SMRT is extremely fast, being able to analyze hundreds of thousands of samples in minutes. The web application is available at http://SMRT.tinnguyen-lab.com. The R package will be deposited to CRAN as part of our PINSPlus software suite.
癌症是一个统称，涵盖了一系列病症，从快速生长且致命的疾病到进展缓慢、致死可能性低或延迟的惰性病变。治疗方案以及治疗成功率高度依赖于对个体患者的正确亚型分类。随着高通量平台的发展，我们有机会从整体角度区分癌症亚型，该角度考虑了不同分子水平（mRNA、甲基化等）的现象。这需要强大的整合方法来利用大型多组学数据集进行更好的亚型分类。在此，我们介绍一种使用随机变换进行多组学亚型分类的方法（SMRT），这是一种用于多组学整合和癌症亚型分类的新方法。与现有方法相比，SMRT具有以下优势：（i）可扩展的分析流程使研究人员能够整合多组学数据，并在数分钟内分析数十万样本；（ii）能够整合不同患者数量的数据类型；（iii）能够分析不同类型的不匹配数据；（iv）能够通过网络应用为用户提供便捷的数据分析流程。我们还提高了基于集成的扰动聚类的效率，以支持在内存受限的机器上进行分析。在广泛的分析中，我们使用37个TCGA数据集和两个METABRIC数据集（共包含来自28种不同癌症的近12,000个患者样本），将SMRT与八种最先进的亚型分类方法进行了比较。我们还进行了一些模拟研究。我们证明，在识别具有显著不同生存特征的亚型方面，SMRT优于其他方法。此外，SMRT速度极快，能够在数分钟内分析数十万样本。网络应用可在http://SMRT.tinnguyen-lab.com获取。R包将作为我们PINSPlus软件套件的一部分存入CRAN。
