ArticleJournal of integrative bioinformatics2017
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Article in Journal of integrative bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deletion ofJID innovations : skin science from molecules to population health · 2021Article
- Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins.Frontiers in genetics · 2021Article
- Generalized correlation coefficient for genome-wide association analysis of cognitive ability in twins.Aging · 2020Article
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7 authors.
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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.
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