Efficient test for nonlinear dependence of two continuous variables.

BMC Bioinformatics 2015 Aug 19;16:260. Epub 2015 Aug 19.

Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200433, China.

Background: Testing dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a new way of testing nonlinear dependence between two continuous variables (X and Y).

Results: We addressed this research question by using CANOVA (continuous analysis of variance, software available at https://sourceforge.net/projects/canova/). In the CANOVA framework, we first defined a neighborhood for each data point related to its X value, and then calculated the variance of the Y value within the neighborhood. Finally, we performed permutations to evaluate the significance of the observed values within the neighborhood variance. To evaluate the strength of CANOVA compared to six other methods, we performed extensive simulations to explore the relationship between methods and compared the false positive rates and statistical power using both simulated and real datasets (kidney cancer RNA-seq dataset).

Conclusions: We concluded that CANOVA is an efficient method for testing nonlinear correlation with several advantages in real data applications.

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Source
http://bmcbioinformatics.biomedcentral.com/articles/10.1186/
Publisher Site
http://dx.doi.org/10.1186/s12859-015-0697-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539721PMC
August 2015
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