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Integrating omics datasets with the OmicsPLS package. / el Bouhaddani, Said; Uh, Hae-Won; Jongbloed, Geurt; Hayward, Caroline; Klarić, Lucija ; Kielbasa, Szymon M.; Houwing-Duistermaat, Jeanine.

In: BMC Bioinformatics, Vol. 19, No. 1, 371, 2018, p. 1-9.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

el Bouhaddani, S, Uh, H-W, Jongbloed, G, Hayward, C, Klarić, L, Kielbasa, SM & Houwing-Duistermaat, J 2018, 'Integrating omics datasets with the OmicsPLS package' BMC Bioinformatics, vol. 19, no. 1, 371, pp. 1-9. https://doi.org/10.1186/s12859-018-2371-3

APA

el Bouhaddani, S., Uh, H-W., Jongbloed, G., Hayward, C., Klarić, L., Kielbasa, S. M., & Houwing-Duistermaat, J. (2018). Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics, 19(1), 1-9. [371]. https://doi.org/10.1186/s12859-018-2371-3

Vancouver

el Bouhaddani S, Uh H-W, Jongbloed G, Hayward C, Klarić L, Kielbasa SM et al. Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics. 2018;19(1):1-9. 371. https://doi.org/10.1186/s12859-018-2371-3

Author

el Bouhaddani, Said ; Uh, Hae-Won ; Jongbloed, Geurt ; Hayward, Caroline ; Klarić, Lucija ; Kielbasa, Szymon M. ; Houwing-Duistermaat, Jeanine. / Integrating omics datasets with the OmicsPLS package. In: BMC Bioinformatics. 2018 ; Vol. 19, No. 1. pp. 1-9.

BibTeX

@article{b21a279d8b9441d58710f9e5225f443f,
title = "Integrating omics datasets with the OmicsPLS package",
abstract = "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 https://cran.r-project.org/package=OmicsPLSand can be installed in R via install.packages({"}OmicsPLS{"}).",
keywords = "Data-specific variation, Joint principal components, O2PLS, Omics data integration, R package",
author = "{el Bouhaddani}, Said and Hae-Won Uh and Geurt Jongbloed and Caroline Hayward and Lucija Klarić and Kielbasa, {Szymon M.} and Jeanine Houwing-Duistermaat",
year = "2018",
doi = "10.1186/s12859-018-2371-3",
language = "English",
volume = "19",
pages = "1--9",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",
number = "1",

}

RIS

TY - JOUR

T1 - Integrating omics datasets with the OmicsPLS package

AU - el Bouhaddani, Said

AU - Uh, Hae-Won

AU - Jongbloed, Geurt

AU - Hayward, Caroline

AU - Klarić, Lucija

AU - Kielbasa, Szymon M.

AU - Houwing-Duistermaat, Jeanine

PY - 2018

Y1 - 2018

N2 - 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 https://cran.r-project.org/package=OmicsPLSand can be installed in R via install.packages("OmicsPLS").

AB - 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 https://cran.r-project.org/package=OmicsPLSand can be installed in R via install.packages("OmicsPLS").

KW - Data-specific variation

KW - Joint principal components

KW - O2PLS

KW - Omics data integration

KW - R package

UR - http://www.scopus.com/inward/record.url?scp=85054611735&partnerID=8YFLogxK

U2 - 10.1186/s12859-018-2371-3

DO - 10.1186/s12859-018-2371-3

M3 - Article

VL - 19

SP - 1

EP - 9

JO - BMC Bioinformatics

T2 - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - 1

M1 - 371

ER -

ID: 47248751