Navigating through ‘omics data: a multivariate perspective

Kim-Anh Lê Cao⁰
(0) The University of Melbourne

Abstract
Technological improvements have allowed for the collection of data from different molecular compartments (e.g. gene expression, protein abundance) resulting in multiple ‘omics data from the same set of biospecimens or individuals (e.g. transcriptomics, proteomics). We propose to adopt a systems biology holistic approach by statistically integrating data from these multi-omics. Such an approach provides improved biological insights compared with traditional single omics analyses, as it allows to take into account interactions between omics layers.

Integrating data include numerous challenges – data are complex and large, each with few samples (< 50) and many molecules (> 10,000), and generated using different technologies. In this talk I will give a broad overview of the different methods we have developed for multivariate statistical data integration. I will illustrate these approaches to explore and integrate different multi-omics studies, from bulk to single cell resolution. Across all these studies, our main goal is to identify a signature composed of biological markers of different types to characterise a specific phenotype or disease status, and thus better understand the underlying molecular mechanisms of a biological system.

Our methods are based on dimension reduction matrix factorisation techniques and implemented in the R package mixOmics.