Title

OnPLS-based multi-block data integration: A multivariate approach to interrogating biological interactions in asthma

Document Type

Journal Article

Publication Title

Analytical chemistry

ISSN

1520-6882

Volume

90

Issue

22

First Page

13400

Last Page

13408

PubMed ID

30335973

Publisher

ACS Publications

School

School of Science / Centre for Integrative Metabolomics and Computational Biology

Comments

Originally published as:

Reinke, S. N., Galindo-Prieto, B., Skotare, T., Broadhurst, D. I., Singhania, A., Horowitz, D., ... & Wheelock, C. E. (2018). OnPLS-based multi-block data integration: a multivariate approach to interrogating biological interactions in asthma. Analytical Chemistry, 90(22).13400–13408.

Original article available here.

Abstract

Integration of multiomics data remains a key challenge in fulfilling the potential of comprehensive systems biology. Multiple-block orthogonal projections to latent structures (OnPLS) is a projection method that simultaneously models multiple data matrices, reducing feature space without relying on a priori biological knowledge. In order to improve the interpretability of OnPLS models, the associated multi-block variable influence on orthogonal projections (MB-VIOP) method is used to identify variables with the highest contribution to the model. This study combined OnPLS and MB-VIOP with interactive visualization methods to interrogate an exemplar multiomics study, using a subset of 22 individuals from an asthma cohort. Joint data structure in six data blocks was assessed: transcriptomics; metabolomics; targeted assays for sphingolipids, oxylipins, and fatty acids; and a clinical block including lung function, immune cell differentials, and cytokines. The model identified seven components, two of which had contributions from all blocks (globally joint structure) and five that had contributions from two to five blocks (locally joint structure). Components 1 and 2 were the most informative, identifying differences between healthy controls and asthmatics and a disease-sex interaction, respectively. The interactions between features selected by MB-VIOP were visualized using chord plots, yielding putative novel insights into asthma disease pathogenesis, the effects of asthma treatment, and biological roles of uncharacterized genes. For example, the gene ATP6 V1G1, which has been implicated in osteoporosis, correlated with metabolites that are dysregulated by inhaled corticoid steroids (ICS), providing insight into the mechanisms underlying bone density loss in asthma patients taking ICS. These results show the potential for OnPLS, combined with MB-VIOP variable selection and interaction visualization techniques, to generate hypotheses from multiomics studies and inform biology.

DOI

10.1021/acs.analchem.8b03205

Share

 
COinS