Integrative clustering methods for high-dimensional molecular data

• Published in: Translational cancer research Vol. 3; no. 3; pp. 202 - 216
• Main Authors: Chalise, Prabhakar, Koestler, Devin C, Bimali, Milan, Yu, Qing, Fridley, Brooke L
• Format: Journal Article
• Language: English
• Published: China 01.06.2014
• Subjects: cophenetic correlation; mixture models; latent models; non-negative matrix factorization; Consensus clustering
• ISSN: 2218-676X; 2219-6803
• DOI: 10.3978/j.issn.2218-676X.2014.06.03

High-throughput 'omic' data, such as gene expression, DNA methylation, DNA copy number, has played an instrumental role in furthering our understanding of the molecular basis in states of human health and disease. As cells with similar morphological characteristics can exhibit entirely different molecular profiles and because of the potential that these discrepancies might further our understanding of patient-level variability in clinical outcomes, there is significant interest in the use of high-throughput 'omic' data for the identification of novel molecular subtypes of a disease. While numerous clustering methods have been proposed for identifying of molecular subtypes, most were developed for single "omic' data types and may not be appropriate when more than one 'omic' data type are collected on study subjects. Given that complex diseases, such as cancer, arise as a result of genomic, epigenomic, transcriptomic, and proteomic alterations, integrative clustering methods for the simultaneous clustering of multiple 'omic' data types have great potential to aid in molecular subtype discovery. Traditionally, ad hoc manual data integration has been performed using the results obtained from the clustering of individual 'omic' data types on the same set of patient samples. However, such methods often result in inconsistent assignment of subjects to the molecular cancer subtypes. Recently, several methods have been proposed in the literature that offers a rigorous framework for the simultaneous integration of multiple 'omic' data types in a single comprehensive analysis. In this paper, we present a systematic review of existing integrative clustering methods.