Integrative Analysis of -Omics Data: A Method Comparison

Abstract

Technological improvements have shifted the focus from data generation to data analysis. The availability of huge amounts of data like transcriptomics, protemics and metabolomics raise new questions concerning suitable integrative analysis methods. We compare three integrative analysis techniques (co-inertia analysis, generalized singular value decomposition and integrative biclustering) by applying them to gene and protein abundance data from six life cycle stages of Plasmodium falciparum. We create a network view of the GO terms associated to cell cycle stages by all three methods.