MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis

Frontiers in Genetics

Minsu Kim¹  Inuk Jung²  Sungmin Rhee³  Sun Kim⁴  Sangsoo Lim⁴ 

[1] Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, United States;Department of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea;Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, South Korea;

关键词: feature selection;    tensor decomposition;    cancer;    multi-omics;    integrative analysis;   

DOI  :  10.3389/fgene.2021.682841

来源: DOAJ

【 摘 要 】

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

【 授权许可】

Unknown