Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm.
Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm.
复制标题
DOI：
10.1371/journal.pone.0176278
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Fridley BL
中科院分区：
文献类型：
--
作者：
Chalise P;Fridley BL
对高通量组学数据的综合分析，如DNA甲基化、DNA拷贝数改变、mRNA和蛋白质表达水平，为理解人类疾病的分子基础创造了前所未有的机会。特别是，综合分析已经成为癌症研究的基石，以确定给定癌症中的分子亚型。由于具有相似形态学特征的恶性肿瘤已显示出表现出完全不同的分子谱，因此对使用多组学数据来鉴定疾病的新分子亚型存在显著兴趣，这可能影响治疗决策。因此，我们开发了intNMF，一种基于非负矩阵分解的疾病亚型分类综合方法。所提出的方法进行多个高维分子数据的综合聚类，在一个单一的综合分析，利用跨多个生物水平上评估同一个人的信息。由于intNMF不假设数据的任何分布形式，它具有明显的优势，比其他基于模型的聚类方法，需要特定的分布假设。intNMF的应用说明使用模拟和真实的数据从癌症基因组图谱（TCGA）。
Integrative analyses of high-throughput ‘omic data, such as DNA methylation, DNA copy number alteration, mRNA and protein expression levels, have created unprecedented opportunities to understand the molecular basis of human disease. In particular, integrative analyses have been the cornerstone in the study of cancer to determine molecular subtypes within a given cancer. As malignant tumors with similar morphological characteristics have been shown to exhibit entirely different molecular profiles, there has been significant interest in using multiple ‘omic data for the identification of novel molecular subtypes of disease, which could impact treatment decisions. Therefore, we have developed intNMF, an integrative approach for disease subtype classification based on non-negative matrix factorization. The proposed approach carries out integrative clustering of multiple high dimensional molecular data in a single comprehensive analysis utilizing the information across multiple biological levels assessed on the same individual. As intNMF does not assume any distributional form for the data, it has obvious advantages over other model based clustering methods which require specific distributional assumptions. Application of intNMF is illustrated using both simulated and real data from The Cancer Genome Atlas (TCGA).