Evaluation of O2PLS in Omics data integration

Journal article (2016)

Authors

S. el Bouhaddani Leiden University Medical Center
Jeanine Houwing-Duistermaat Leiden University Medical Center
Perttu Salo National Institute for Health and Welfare
Marcus Perola National Institute for Health and Welfare
G. Jongbloed Delft Institute of Applied Mathematics
Hae Won Uh Leiden University Medical Center
Department
Delft Institute of Applied Mathematics
Copyright: © 2016 S. el Bouhaddani, Jeanine Houwing-Duistermaat, Perttu Salo, Marcus Perola, G. Jongbloed, Hae-Won Uh
DOI: https://doi.org/10.1186/s12859-015-0854-z
More Info
expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Department

Delft Institute of Applied Mathematics

Abstract

Background
Rapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.
Results
A simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret.
Conclusions
Simulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.

Files

1901157.pdf
(pdf | 7.83 Mb)
Unknown license