A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data.

ABSTRACT:

Motivation

Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects.

Results

We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity o

SUBMITTER: Yang Z 

PROVIDER: S-EPMC5006236 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature