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Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis. / Waaijenborg, Sandra; Zwinderman, Aeilko H.

In: Bioinformatics (Oxford, England), Vol. 25, No. 21, 2009, p. 2764-2771.

Research output: Contribution to journal › Article › Academic › peer-review

Harvard

Waaijenborg, S & Zwinderman, AH 2009, 'Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis', Bioinformatics (Oxford, England), vol. 25, no. 21, pp. 2764-2771. https://doi.org/10.1093/bioinformatics/btp491

APA

Waaijenborg, S., & Zwinderman, A. H. (2009). Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis. Bioinformatics (Oxford, England), 25(21), 2764-2771. https://doi.org/10.1093/bioinformatics/btp491

Vancouver

Waaijenborg S, Zwinderman AH. Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis. Bioinformatics (Oxford, England). 2009;25(21):2764-2771. doi: 10.1093/bioinformatics/btp491

Author

Waaijenborg, Sandra ; Zwinderman, Aeilko H. / Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis. In: Bioinformatics (Oxford, England). 2009 ; Vol. 25, No. 21. pp. 2764-2771.

BibTeX

@article{c5d461c6e203420fa4e0ac4ab80ff1e6,
title = "Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis",
abstract = "MOTIVATION: Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patient's phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA can not be applied to such data. Moreover, the size of the data in genetic studies can be enormous thereby making the results difficult to interpret. RESULTS: We developed a penalized nonlinear canonical correlation analysis approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable via optimal scaling. Additionally sparse results were obtained by adapting softthresholding to this nonlinear version of the CCA. By means of simulation studies we show that our method is capable of extracting relevant variables out of high dimensional sets. We applied our method to a genetic dataset containing 144 patients with glial cancer. CONTACT: s.waaijenborg@amc.uva.nl",
author = "Sandra Waaijenborg and Zwinderman, {Aeilko H.}",
year = "2009",
doi = "10.1093/bioinformatics/btp491",
language = "English",
volume = "25",
pages = "2764--2771",
journal = "Bioinformatics (Oxford, England)",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "21",

}

RIS

TY - JOUR

T1 - Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis

AU - Waaijenborg, Sandra

AU - Zwinderman, Aeilko H.

PY - 2009

Y1 - 2009

N2 - MOTIVATION: Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patient's phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA can not be applied to such data. Moreover, the size of the data in genetic studies can be enormous thereby making the results difficult to interpret. RESULTS: We developed a penalized nonlinear canonical correlation analysis approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable via optimal scaling. Additionally sparse results were obtained by adapting softthresholding to this nonlinear version of the CCA. By means of simulation studies we show that our method is capable of extracting relevant variables out of high dimensional sets. We applied our method to a genetic dataset containing 144 patients with glial cancer. CONTACT: s.waaijenborg@amc.uva.nl

AB - MOTIVATION: Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patient's phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA can not be applied to such data. Moreover, the size of the data in genetic studies can be enormous thereby making the results difficult to interpret. RESULTS: We developed a penalized nonlinear canonical correlation analysis approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable via optimal scaling. Additionally sparse results were obtained by adapting softthresholding to this nonlinear version of the CCA. By means of simulation studies we show that our method is capable of extracting relevant variables out of high dimensional sets. We applied our method to a genetic dataset containing 144 patients with glial cancer. CONTACT: s.waaijenborg@amc.uva.nl

U2 - 10.1093/bioinformatics/btp491

DO - 10.1093/bioinformatics/btp491

M3 - Article

C2 - 19689958

VL - 25

SP - 2764

EP - 2771

JO - Bioinformatics (Oxford, England)

JF - Bioinformatics (Oxford, England)

SN - 1367-4803

IS - 21

ER -

ID: 933395