Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization.

ABSTRACT: Identification of novel molecular subtypes of disease using multi-source 'omics data is an active area of on-going research. Integrative clustering is a powerful approach to identify latent subtype structure inherent in the data sets accounting for both between and within data correlations. We propose a new integrative network-based clustering method using the non-negative matrix factorization, nNMF, for clustering multiple types of interrelated datasets assayed on same tumor-samples. nNMF utilizes the consensus matrices generated using the non-negative matrix factorization (NMF) algorithm on each type of data as networks among the patient samples. The multiple networks are then combined, and a comprehensive network structure is created optimizing the strengths of the relationships. A spec

SUBMITTER: Chalise P 

PROVIDER: S-EPMC7078030 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature