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

Jinyu Chen,Shihua Zhang

doi:10.3389/fgene.2018.00194

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 differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.

Highlights

Biological systems are very complex, consisting of diverse components interacting with each other cooperatively
We develop a unified Matrix Integration Analysis (MIA) package, implementing these four methods as a set of MATLAB functions, to facilitate their adoption, promotion and evaluation (Figure 1)
We present a MATLAB package, MIA, to conduct integrative and modular analysis for multi-dimensional genomic data across the same sets of samples as well as prior network knowledge to decode the relationships among different levels of cellular activities

Summary

Introduction

Biological systems are very complex, consisting of diverse components interacting with each other cooperatively. The Cancer Genome Atlas (TCGA) project (The Cancer Genome Atlas Research Network, 2008) provides copy number variation, DNA methylation, microRNA and gene expression profiles for the same set of tumor samples. It is an essential and valuable task to develop tools that can explore combinatorial relationships among multiple layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), which implements four methods for modular and integrative analysis. The four methods are extensions of two classical techniques—non-negative matrix factorization (NMF) and partial least squares (PLS)

Methods

Results

Conclusion