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

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Abstrakt

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.
OriginalsprogEngelsk
Artikelnummer20170011
TidsskriftJournal of Integrative Bioinformatics
Vol/bind14
Udgave nummer2
ISSN1613-4516
DOI
StatusUdgivet - 6. jun. 2017
Begivenhed13th Annual Meeting of the International Symposium on Integrative Bioinformatics - University of Southern Denmark, Campusvej 55 5230 Odense, Odense, Danmark
Varighed: 22. jun. 201724. jun. 2017
Konferencens nummer: 13
http://www.imbio.de/ib2017/program.php

Konference

Konference13th Annual Meeting of the International Symposium on Integrative Bioinformatics
Nummer13
LokationUniversity of Southern Denmark, Campusvej 55 5230 Odense
LandDanmark
ByOdense
Periode22/06/201724/06/2017
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