Optimization of nearest-neighbor feature selection depends on the number of samples and features, the type of statistical effect, the feature scoring algorithm, and class imbalance. We recently reported a fixed-k for Nearest-neighbor Projected-Distance Regression (NPDR) that addresses each of these parameters, except for class imbalance. To remedy this, we parameterize our NPDR fixed-k by the minority class size (minority-class-k). We also introduce a class-adaptive fixed-k (hit-miss-k) to improve performance of Relief-based algorithms on imbalanced data. In addition, we present two optimization methods, including constrained variable-wise optimized k (VWOK) and a fixed-k derived with principal components analysis (kPCA), both of which are adaptive to class imbalance. Using simulated data, we show that our methods significantly improve feature detection across a variety of nearest-neighbor feature scoring metrics, and we demonstrate superior performance in comparison to random forest and ridge regression using consensus-nested cross-validation (cnCV) for feature selection. We applied cnCV to RNASeq expression data from a study of Major Depressive Disorder (MDD) using NPDR with minority-class-k, random forest, and cnCV-ridge regression for gene importance. Pathway analysis showed that NPDR with minority-class-k alone detected genes with clear relevance to MDD, suggesting that our new fixed-k formula is an effective rule-of-thumb.

SUMMARY: Machine learning feature selection methods are needed to detect complex interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWAS, gene expression, eQTL and structural/functional neuroimage studies for case-control or continuous outcomes. In addition, many machine learning methods have limited ability to address the issues of controlling false discoveries and adjusting for covariates. To address these challenges, we develop a new feature selection technique called Nearest-neighbor Projected-Distance Regression (NPDR) that calculates the importance of each predictor using generalized linear model regression of distances between nearest-neighbor pairs projected onto the predictor dimension. NPDR captures the underlying interaction structure of data using nearest-neighbors in high dimensions, handles both dichotomous and continuous outcomes and predictor data types, statistically corrects for covariates, and permits statistical inference and penalized regression. We use realistic simulations with interactions and other effects to show that NPDR has better precision-recall than standard Relief-based feature selection and random forest importance, with the additional benefit of covariate adjustment and multiple testing correction. Using RNA-Seq data from a study of major depressive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due to confounding. We apply NPDR to eQTL data to identify potentially interacting variants that regulate transcripts associated with MDD and demonstrate NPDR's utility for GWAS and continuous outcomes. AVAILABILITY AND IMPLEMENTATION: Available at: https://insilico.github.io/npdr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.