Practical Evaluation of Different Omics Data Integration Methods

Abstract

Identification of meaningful connections from different types of omics data sets is extremely important in computational biology and system biology. Integration of multi-omics data is the first essential step for data analysis, but it is also challenging for the systems biologists to correctly integrate different data together. Practical comparison of different omics data integration methods can provide biomedical researchers a clear view of how to select appropriate methods and tools to integrate and analyze different multi-omics datasets. Here we illustrate two widely used R-based omic data integration tools: mixOmics and STATegRa, to analyze different types of omics data sets and evaluate their performance.