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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.

SUBMITTER: Tan Q

PROVIDER: S-EPMC6042830 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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

Tan Qihua Q Thomassen Mads M Burton Mark M Mose Kristian Fredløv KF Andersen Klaus Ejner KE Hjelmborg Jacob J Kruse Torben T

Journal of integrative bioinformatics 20170606 2

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 ...[more]

PMID: 28753536

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