knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable
Abstract Background Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1). Results We addre...
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doaj-1002694f0f94455595d5ebdd1fb698e72020-11-25T01:45:04ZengBMCBMC Bioinformatics1471-21052018-11-0119111210.1186/s12859-018-2427-4knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variableYi Li0Xiaoyu Liu1Yanyun Ma2Yi Wang3Weichen Zhou4Meng Hao5Zhenghong Yuan6Jie Liu7Momiao Xiong8Yin Yao Shugart9Jiucun Wang10Li Jin11Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan UniversityMinistry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan UniversityMinistry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan UniversityMinistry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan UniversityState Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan UniversityMinistry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan UniversityShanghai Public Health Clinical Center, Fudan UniversityKey Laboratory of Medical Molecular Virology of MOE/MOH, Shanghai Medical School, Fudan UniversityHuman Genetics Center, School of Public Health, University of Texas Houston Health Sciences CenterUnit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of HealthSix Industrial Research Institute, Fudan UniversitySix Industrial Research Institute, Fudan UniversityAbstract Background Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1). Results We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). Conclusions We concluded that knnAUC is an efficient R package to test non-linear dependence between one continuous variable and one binary dependent variable especially in computational biology area.http://link.springer.com/article/10.1186/s12859-018-2427-4Open sourceR packageNonlinear dependenceOne continuous variableOne binary dependent variableAUC |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Li Xiaoyu Liu Yanyun Ma Yi Wang Weichen Zhou Meng Hao Zhenghong Yuan Jie Liu Momiao Xiong Yin Yao Shugart Jiucun Wang Li Jin |
spellingShingle |
Yi Li Xiaoyu Liu Yanyun Ma Yi Wang Weichen Zhou Meng Hao Zhenghong Yuan Jie Liu Momiao Xiong Yin Yao Shugart Jiucun Wang Li Jin knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable BMC Bioinformatics Open source R package Nonlinear dependence One continuous variable One binary dependent variable AUC |
author_facet |
Yi Li Xiaoyu Liu Yanyun Ma Yi Wang Weichen Zhou Meng Hao Zhenghong Yuan Jie Liu Momiao Xiong Yin Yao Shugart Jiucun Wang Li Jin |
author_sort |
Yi Li |
title |
knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable |
title_short |
knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable |
title_full |
knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable |
title_fullStr |
knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable |
title_full_unstemmed |
knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable |
title_sort |
knnauc: an open-source r package for detecting nonlinear dependence between one continuous variable and one binary variable |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2018-11-01 |
description |
Abstract Background Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1). Results We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). Conclusions We concluded that knnAUC is an efficient R package to test non-linear dependence between one continuous variable and one binary dependent variable especially in computational biology area. |
topic |
Open source R package Nonlinear dependence One continuous variable One binary dependent variable AUC |
url |
http://link.springer.com/article/10.1186/s12859-018-2427-4 |
work_keys_str_mv |
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