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

Research output: Contribution to journalConference articleResearchpeer-review

197 Downloads (Pure)

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.
Original languageEnglish
Article number20170011
JournalJournal of Integrative Bioinformatics
Volume14
Issue number2
ISSN1613-4516
DOIs
Publication statusPublished - 6. Jun 2017
Event13th Annual Meeting of the International Symposium on Integrative Bioinformatics - University of Southern Denmark, Campusvej 55 5230 Odense, Odense, Denmark
Duration: 22. Jun 201724. Jun 2017
Conference number: 13
http://www.imbio.de/ib2017/program.php

Conference

Conference13th Annual Meeting of the International Symposium on Integrative Bioinformatics
Number13
LocationUniversity of Southern Denmark, Campusvej 55 5230 Odense
CountryDenmark
CityOdense
Period22/06/201724/06/2017
Internet address

Keywords

  • gene expression microarray generalized correlation coefficient; time-course
  • generalized correlation coefficient
  • time-coarse
  • gene expression microarray
  • time-course

Fingerprint Dive into the research topics of 'Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data'. Together they form a unique fingerprint.

  • Cite this