ArticleBMC bioinformatics2015
Efficient test for nonlinear dependence of two continuous variables.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions.ArXiv · 2025Article
- An Introduction to Network Analysis in Plant Biology.Plant direct · 2025Article
- Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.Scientific reports · 2023Article
- Testing Nonlinearity with Rényi and Tsallis Mutual Information with an Application in the EKC Hypothesis.Entropy (Basel, Switzerland) · 2022Article
- In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes.BMC genomics · 2022Article
- Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality.Entropy (Basel, Switzerland) · 2021Article
- knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable.BMC bioinformatics · 2018Article
- A standardized fold change method for microarray differential expression analysis used to reveal genes involved in acute rejection in murine allograft models.FEBS open bio · 2018Article
- Bagging Nearest-Neighbor Prediction independence Test: an efficient method for nonlinear dependence of two continuous variables.Scientific reports · 2017Article
- DTW-MIC Coexpression Networks from Time-Course Data.PloS one · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundTesting 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).
resultsWe 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).
conclusionsWe concluded that CANOVA is an efficient method for testing nonlinear correlation with several advantages in real data applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.