Integrating omics datasets with the OmicsPLS package

Said el Bouhaddani*, Hae-Won Uh, Geurt Jongbloed, Caroline Hayward, Lucija Klarić, Szymon M. Kielbasa, Jeanine Houwing-Duistermaat

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)
58 Downloads (Pure)


Background: With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. Results: We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. Conclusions: We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at can be installed in R via install.packages("OmicsPLS").

Original languageEnglish
Article number371
Pages (from-to)1-9
Number of pages9
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 2018


  • Data-specific variation
  • Joint principal components
  • O2PLS
  • Omics data integration
  • R package


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