Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns.

ABSTRACT: The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the di

SUBMITTER: Chen J 

PROVIDER: S-EPMC5992392 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature