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knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary

Yi Li1,2,3, Xiaoyu Liu1,3, Yanyun Ma1,2,3

  • 1Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China.

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|November 24, 2018
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Summary
This summary is machine-generated.

This study introduces knnAUC, an R package for detecting nonlinear relationships between continuous and binary variables. It offers an efficient method for statistical analysis, particularly in computational biology.

Keywords:
AUCAssociation analysisNonlinear dependenceOne binary dependent variableOne continuous variableOpen sourceR package

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Area of Science:

  • Statistics
  • Computational Biology
  • Bioinformatics

Background:

  • Testing variable dependence is fundamental in statistics.
  • Developed an open-source R package, knnAUC, to detect nonlinear dependence.
  • Focuses on one continuous variable (X) and one binary dependent variable (Y).

Purpose of the Study:

  • Introduce the knnAUC R package for detecting nonlinear dependence.
  • Evaluate the performance of knnAUC against other statistical methods.
  • Highlight its utility in computational biology applications.

Main Methods:

  • Utilized a k-nearest neighbors (KNN) algorithm within the knnAUC framework.
  • Resampled data into training and testing sets.
  • Calculated the Area Under the Curve (AUC) estimator to test for dependence (AUC > 0.5).

Main Results:

  • knnAUC demonstrated efficiency in detecting nonlinear dependence.
  • Compared knnAUC with seven other methods using simulations and real datasets.
  • Evaluated false positive rates and statistical power across methods.

Conclusions:

  • knnAUC is an efficient R package for testing nonlinear dependence between continuous and binary variables.
  • The package is particularly useful in the computational biology domain.
  • knnAUC provides a valuable tool for statistical analysis in bioinformatics.