Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria

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Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria. / Rousu, Juho; Agranoff, Daniel D.; Sodeinde, Olugbemiro; Shawe-Taylor, John; Fernandez-Reyes, Delmiro.

In: PLoS computational biology, Vol. 9, No. 4, 1003018, 2013, p. 1-10.

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Rousu, Juho ; Agranoff, Daniel D. ; Sodeinde, Olugbemiro ; Shawe-Taylor, John ; Fernandez-Reyes, Delmiro. / Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria. In: PLoS computational biology. 2013 ; Vol. 9, No. 4. pp. 1-10.

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@article{4f60f0b1368b4c71ae729142865f2184,
title = "Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria",
abstract = "Biomarker discovery aims to find small subsets of relevant variables in ‘omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the ‘omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant ‘omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5–3{\%} of all ‘omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11{\%} in tuberculosis and up to 5{\%} in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive ‘omics measurement capabilities.",
author = "Juho Rousu and Agranoff, {Daniel D.} and Olugbemiro Sodeinde and John Shawe-Taylor and Delmiro Fernandez-Reyes",
note = "VK: algodan hiit",
year = "2013",
doi = "10.1371/journal.pcbi.1003018",
language = "English",
volume = "9",
pages = "1--10",
journal = "PLoS computational biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "4",

}

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TY - JOUR

T1 - Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria

AU - Rousu, Juho

AU - Agranoff, Daniel D.

AU - Sodeinde, Olugbemiro

AU - Shawe-Taylor, John

AU - Fernandez-Reyes, Delmiro

N1 - VK: algodan hiit

PY - 2013

Y1 - 2013

N2 - Biomarker discovery aims to find small subsets of relevant variables in ‘omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the ‘omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant ‘omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5–3% of all ‘omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive ‘omics measurement capabilities.

AB - Biomarker discovery aims to find small subsets of relevant variables in ‘omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the ‘omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant ‘omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5–3% of all ‘omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive ‘omics measurement capabilities.

U2 - 10.1371/journal.pcbi.1003018

DO - 10.1371/journal.pcbi.1003018

M3 - Article

VL - 9

SP - 1

EP - 10

JO - PLoS computational biology

JF - PLoS computational biology

SN - 1553-734X

IS - 4

M1 - 1003018

ER -

ID: 855836