Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Qihua Tan; Mads Thomassen; Mark Burton; Kristian Fredløv Mose; Klaus Ejner Andersen; Jacob v. B. Hjelmborg; Torben A Kruse

doi:10.1515/jib-2017-0011

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Qihua Tan^*, Mads Thomassen, Mark Burton, Kristian Fredløv Mose, Klaus Ejner Andersen, Jacob v. B. Hjelmborg, Torben A Kruse

^*Kontaktforfatter

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › peer review

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Abstract

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Originalsprog	Engelsk
Artikelnummer	20170011
Tidsskrift	Journal of Integrative Bioinformatics
Vol/bind	14
Udgave nummer	2
ISSN	1613-4516
DOI	https://doi.org/10.1515/jib-2017-0011
Status	Udgivet - 6. jun. 2017
Begivenhed	13th Annual Meeting of the International Symposium on Integrative Bioinformatics - University of Southern Denmark, Campusvej 55 5230 Odense, Odense, Danmark Varighed: 22. jun. 2017 → 24. jun. 2017 Konferencens nummer: 13 http://www.imbio.de/ib2017/program.php

Konference

Konference	13th Annual Meeting of the International Symposium on Integrative Bioinformatics
Nummer	13
Lokation	University of Southern Denmark, Campusvej 55 5230 Odense
Land/Område	Danmark
By	Odense
Periode	22/06/2017 → 24/06/2017
Internetadresse	http://www.imbio.de/ib2017/program.php

Dokumenter og links

10.1515/jib-2017-0011Licens: CC BY-NC-ND

[Journal of Integrative Bioinformatics] Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course DataForlagets udgivne version, 753 KBLicens: CC BY-NC-ND

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

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title = "Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data",

abstract = "Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.",

keywords = " gene expression microarray generalized correlation coefficient; time-course, generalized correlation coefficient, time-coarse, gene expression microarray, time-course",

author = "Qihua Tan and Mads Thomassen and Mark Burton and Mose, {Kristian Fredl{\o}v} and Andersen, {Klaus Ejner} and Hjelmborg, {Jacob v. B.} and Kruse, {Torben A}",

year = "2017",

month = jun,

day = "6",

doi = "10.1515/jib-2017-0011",

language = "English",

volume = "14",

journal = "Journal of Integrative Bioinformatics",

issn = "1613-4516",

publisher = "Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.)",

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T1 - Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

AU - Tan, Qihua

AU - Thomassen, Mads

AU - Burton, Mark

AU - Mose, Kristian Fredløv

AU - Andersen, Klaus Ejner

AU - Hjelmborg, Jacob v. B.

AU - Kruse, Torben A

N1 - Conference code: 13

PY - 2017/6/6

Y1 - 2017/6/6

N2 - Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

AB - Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

KW - gene expression microarray generalized correlation coefficient; time-course

KW - generalized correlation coefficient

KW - time-coarse

KW - gene expression microarray

KW - time-course

U2 - 10.1515/jib-2017-0011

DO - 10.1515/jib-2017-0011

M3 - Conference article

C2 - 28753536

SN - 1613-4516

VL - 14

JO - Journal of Integrative Bioinformatics

JF - Journal of Integrative Bioinformatics

IS - 2

M1 - 20170011

T2 - 13th Annual Meeting of the International Symposium on Integrative Bioinformatics

Y2 - 22 June 2017 through 24 June 2017

ER -

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Abstract

Konference

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater