NDLI: A nonparametric approach to detect nonlinear correlation in gene expression.

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A nonparametric approach to detect nonlinear correlation in gene expression.

Content Provider	Semantic Scholar
Author	Mueller, Peter Rohrer, Bärbel
Copyright Year	2010
Abstract	We propose a distribution-free approach to detect nonlinear relationships by reporting local correlation. The effect of our proposed method is analogous to piece-wise linear approximation although the method does not utilize any linear dependency. The proposed metric, maximum local correlation, was applied to both simulated cases and expression microarray data comparing the rd mouse with age-matched control animals. The rd mouse is an animal model (with a mutation for the gene Pde6b) for photoreceptor degeneration. Using simulated data, we show that maximum local correlation detects nonlinear association, which could not be detected using other correlation measures. In the microarray study, our proposed method detects nonlinear association between the expression levels of different genes, which could not be detected using the conventional linear methods. The simulation dataset, microarray expression data, and the Nonparametric Nonlinear Correlation (NNC) software library, implemented in Matlab, are included as part of the online supplemental materials.
Starting Page	552
Ending Page	568
Page Count	17
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.stat.tamu.edu/~carroll/ftp/2010.papers.directory/Chen_as_published.pdf
PubMed reference number	20877445v1
Volume Number	19
Issue Number	3
Journal	Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
Language	English
Access Restriction	Open
Subject Keyword	Age related macular degeneration Gene Expression Mutation Silo (dataset) emotional dependency
Content Type	Text
Resource Type	Article

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