Multi-block multivariate data integration: insights into asthma

Multi-omics data acquisition is becoming increasingly popular in applied sciences such as biomedicine. Due to the number of observed variables and the differential complexity of covariance structure of different omics platforms, multi-omics data integration remains a key challenge in systems-based studies. Multi-block multivariate modelling approaches, such as multiple-block orthogonal projections to latent structures (OnPLS), are capable of unbiased projection to latent feature space without relying on a priori knowledge of a variable’s biological importance, thus providing an opportunity to identify truly novel biology. However, these models are also mathematically complex and can be difficult to interpret. To improve interpretability of OnPLS, an associated multi-block variable influence on orthogonal projections (MB-VIOP) feature selection method has recently been developed to identify variables with the highest contribution to the model. Using an asthma cohort as a case study, I will demonstrate how methods such as OnPLS and MB-VIOP can be used to integrate multi-omics data, and how using intuitive visualisation strategies can improve interpretability, thus revealing novel biological insights.

Stacey Reinke

Dr Stacey Reinke

Senior Research Fellow, Centre for Integrative Metabolomics & Computational Biology, Edith Cowan University

Stacey Reinke is a Senior Research Fellow with the Centre for Integrative Metabolomics & Computational Biology at Edith Cowan University. She was awarded her PhD in Biochemistry in 2011 from the University of Alberta (Canada) and held a Postdoctoral Fellowship, from the Canadian Institutes of Health Research, at the Karolinska Institute (Sweden) before being recruited to Perth in 2016. During her PhD and subsequent postdoctoral training, Stacey developed considerable expertise in the area of clinical and biomedical metabolomics, including biochemistry, analytical chemistry, large-scale cohort studies, statistics, and data science. She has applied these skills to several biological contexts, including mitochondrial and central energy dysfunction, neonatal asphyxia, neuroinflammation, and respiratory disease. Her current research focus is two-fold: (1) applying metabolomics and systems biology approaches to inform disease mechanisms and biomarker discovery, and (2) enhancing the field of clinical and biomedical metabolomics through methodological advancement (high quality high-throughput workflows, quality assurance, data analysis).

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