OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma

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.