Integrative exploratory analysis of two or more genomic datasets

Abstract

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.