A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study

BMC Bioinformatics

Research

Robert G. Nelson¹  Jiang He²  Ana C. Ricardo³  Jeffery C. Fink⁴  Hernan Rincon-Choles⁵  Brian Kwan⁶  Loki Natarajan⁶  Karen Messer⁶  Daniel Montemayor⁷  Hongping Ye⁷  Kumar Sharma⁷  Chi-yuan Hsu⁸  Tobias Fuhrer⁹  Minya Pu^1,10  Jing Zhang^1,10  Vallabh O. Shah^1,11 

[1] Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA;Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine and Tulane University Translational Science Institute,, New Orleans, LA, USA;Department of Medicine, University of Illinois, Chicago, IL, USA;Department of Medicine, University of Maryland, Baltimore School of Medicine, Baltimore, MD, USA;Department of Nephrology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA;Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA;Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA;Division of Nephrology, Department of Medicine, University of Texas Health San Antonio, San Antonio, TX, USA;Center for Renal Precision Medicine, University of Texas Health San Antonio, San Antonio, TX, USA;Division of Nephrology, University of California, San Francisco School of Medicine, San Francisco, CA, USA;Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland;Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA;University of New Mexico Health Sciences Center, Albuquerque, NM, USA;

关键词: Biomarker;    Classification;    Feature selection;    Kidney disease;    Metabolomics;    Order statistics;    Ranking algorithm;   

DOI  :  10.1186/s12859-023-05171-w

 received in 2022-01-31, accepted in 2023-02-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests.ResultsOur simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models.ConclusionsWe extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

【 授权许可】

CC BY   
© The Author(s) 2023

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