Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data

TLDR

Advances in life‑science instrumentation now allow virtually unlimited multi‑platform data collection, enabling systems‑biology studies that integrate transcriptomic, metabolomic, and proteomic measurements through advanced statistical analysis. This study demonstrates that the O2PLS multivariate regression method can combine different ‘omics’ data types. O2PLS separates shared systematic variation from platform‑specific variation, and its application to a Populus tremula × Populus tremuloides short‑day experiment illustrates these benefits. The resulting models were validated and interpreted to reveal biologically relevant events, outperforming pairwise univariate correlation and principal component analysis.

Abstract

Summary The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This ‘systems biology’ approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining ‘omics’ types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform‐specific systematic variation. A study of Populus tremula × Populus tremuloides , investigating short‐day‐induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.