Efficient test for nonlinear dependence of two continuous variables.
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.
Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200433, China. godspeed.wang@gmail.com.
Yi
Y
Wang
Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200433, China. liyistat@gmail.com.
Yi
Y
Li
Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. hongbao.cao@nih.gov.
Hongbao
H
Cao
Human Genetics Center, School of Public Health, University of Texas Houston Health Sciences Center, Houston, TX, USA. momiao.xiong@gmail.com.
Momiao
M
Xiong
Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. yin.yao@nih.gov.|Division of Intramural Research Program, National Institute of Mental Health, National Institute of Health, Porter Bldg, Room 3A100, Bethesda, MD, 20892, USA. yin.yao@nih.gov.
Yin Yao
YY
Shugart
Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200433, China. lijin@fudan.edu.cn.|School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200433, China. lijin@fudan.edu.cn.
Li
L
Jin
D006801:Humans; D007680:Kidney Neoplasms / Q000235:genetics / Q000473:pathology; D012313:RNA / Q000032:analysis / Q000378:metabolism; D017423:Sequence Analysis, RNA; D012984:Software
D012313:RNA