Print Email Facebook Twitter Evaluation of O2PLS in Omics data integration Title Evaluation of O2PLS in Omics data integration Author el Bouhaddani, S. (Leiden University Medical Center) Houwing-Duistermaat, Jeanine (Leiden University Medical Center) Salo, Perttu (National Institute for Health and Welfare) Perola, Marcus (National Institute for Health and Welfare) Jongbloed, G. (TU Delft Delft Institute of Applied Mathematics) Uh, Hae-Won (Leiden University Medical Center) Date 2016 Abstract BackgroundRapid 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.ResultsA 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.ConclusionsSimulations 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. Subject Integration of Omics dataDimension reductionLatent variable regressionO2PLS To reference this document use: http://resolver.tudelft.nl/uuid:88eefdf5-a209-4340-9ff9-5588180193b4 DOI https://doi.org/10.1186/s12859-015-0854-z ISSN 1471-2105 Source BMC Bioinformatics, 17 (Suppl 2) (11), 118-132 Event Bringing Maths to Life, 2016-10-27 → 2016-10-29, Naples, Italy Part of collection Institutional Repository Document type journal article Rights © 2016 S. el Bouhaddani, Jeanine Houwing-Duistermaat, Perttu Salo, Marcus Perola, G. Jongbloed, Hae-Won Uh Files PDF 1901157.pdf 7.83 MB Close viewer /islandora/object/uuid:88eefdf5-a209-4340-9ff9-5588180193b4/datastream/OBJ/view