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Efficient Test for Nonlinear Dependence of Two Continuous Variables

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Book cover Applied Computational Genomics

Part of the book series: Translational Bioinformatics ((TRBIO,volume 13))

Abstract

A new method to test nonlinear dependence between two continuous variables (X and Y) is proposed. This is achieved by using continuous analysis of variance (CANOVA). The software is available at https://sourceforge.net/projects/canova. First, a neighborhood for each data point related to its X value was defined. Then, the variance of the Y value within the neighborhood was calculated. Last, permutations to evaluate the significance of the observed values within the neighborhood variance were conducted. To examine the strength of CANOVA compared to six other methods, extensive simulations were completed to examine the false-positive rates and statistical power. Both simulation and real datasets (kidney cancer RNA-seq data) were used. From these analyses, it was concluded that CANOVA is efficient as a method in testing nonlinear correlation and has several advantages for real data application.

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Authors and Affiliations

  1. Unit of Statistical Genomics, Division of Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA

    McKenzie Ritter & Yin Yao

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

    Yi Li, Yi Wang & Li Jin

Authors
  1. McKenzie Ritter
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  2. Yi Li
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  3. Yi Wang
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  4. Yin Yao
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  5. Li Jin
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Corresponding author

Correspondence to Li Jin .

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Editors and Affiliations

  1. Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

    Yin Yao

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Ritter, M., Li, Y., Wang, Y., Yao, Y., Jin, L. (2018). Efficient Test for Nonlinear Dependence of Two Continuous Variables. In: Yao, Y. (eds) Applied Computational Genomics. Translational Bioinformatics, vol 13. Springer, Singapore. https://doi.org/10.1007/978-981-13-1071-3_8

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