Bouhaddani e, Said, Uh, Won H, Jongbloed, Geurt, Hayward, Caroline, Klarić, Lucija, Kiełbasa, M. S, Houwing-Duistermaat, Jeanine (2018). “Integrating omics datasets with the OmicsPLS package.” BMC Bioinformatics, 19(1). ISSN 1471-2105, doi:10.1186/s12859-018-2371-3.

Corresponding BibTeX entry:

  @Article{,
    author = {el Bouhaddani and {Said} and {Uh} and Hae Won and
      {Jongbloed} and {Geurt} and {Hayward} and {Caroline} and {Klarić}
      and {Lucija} and {Kiełbasa} and Szymon M. and
      {Houwing-Duistermaat} and {Jeanine}},
    title = {Integrating omics datasets with the OmicsPLS package},
    journal = {BMC Bioinformatics},
    year = {2018},
    volume = {19},
    number = {1},
    abstract = {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. 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. We
      propose the OmicsPLS R package: a free and open-source
      implementation of O2PLS for statistical data integration.
      OmicsPLS is available at
      https://cran.r-project.org/package=OmicsPLS and can be installed
      in R via install.packages(“OmicsPLS”).},
    issn = {1471-2105},
    doi = {10.1186/s12859-018-2371-3},
  }