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

Abstract

BackgroundTesting 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).ResultsWe 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).ConclusionsWe 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.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2427-4) contains supplementary material, which is available to authorized users.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

“…Gene expression profiles were extracted from the GSE83148 and GSE121248 data set, which was downloaded from the publicly available Gene Expression Omnibus database (GEO) 1 ( Clough and Barrett, 2016 ). GSE83148 ( Zhou et al, 2017 ; Li et al, 2018 ; Chen Z. et al, 2019 ) and GSE121248 ( Wang et al, 2007 ) are both based on the GPL570 ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). The title of the GSE83148 data set is “Expression data of HBV infected liver tissue.” All hepatitis samples were HBV infected, which was validated by positive HBsAg or serum HBV-DNA.…”