Applications of Multivariate Statistical and Data Mining Analyses to the Search for Biomarkers of Sensorineural Hearing Loss, Tinnitus, and Vestibular Dysfunction.

Paul F Smith,Yiwen Zheng

doi:10.3389/fneur.2021.627294

Paul F Smith, Yiwen Zheng

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https://doi.org/10.3389/fneur.2021.627294

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Disorders of sensory systems, as with most disorders of the nervous system, usually involve the interaction of multiple variables to cause some change, and yet often basic sensory neuroscience data are analyzed using univariate statistical analyses only. The exclusive use of univariate statistical procedures, analyzing one variable at a time, may limit the potential of studies to determine how interactions between variables may, as a network, determine a particular result. The use of multivariate statistical and data mining methods provides the opportunity to analyse many variables together, in order to appreciate how they may function as a system of interacting variables, and how this system or network may change as a result of sensory disorders such as sensorineural hearing loss, tinnitus or different types of vestibular dysfunction. Here we provide an overview of the potential applications of multivariate statistical and data mining techniques, such as principal component and factor analysis, cluster analysis, multiple linear regression, random forest regression, linear discriminant analysis, support vector machines, random forest classification, Bayesian classification, and orthogonal partial least squares discriminant analysis, to the study of auditory and vestibular dysfunction, with an emphasis on classification analytic methods that may be used in the search for biomarkers of disease.

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Experimental phenomena in neuroscience often involve the complex, sometimes non-linear interaction, of multiple variables
We have used multivariate statistical analysis (MVA) and data mining methods to explore the way that combinations of variables can account for neurochemical and behavioral changes following the loss of vestibular function [3,4,5,6, 15, 72, 73] and auditory function [e.g., [35, 65]]
MVAs and data mining methods have been used to predict the progression of patients from one neurological disorder to another [e.g., [9, 12]] and the probability that the early adolescent use of Cannabis can lead to the development of psychotic symptoms in later life [e.g., [74]]

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Introduction

Experimental phenomena in neuroscience often involve the complex, sometimes non-linear interaction, of multiple variables. In many areas of sensory neuroscience in general, univariate statistical analyses have been used almost exclusively This approach neglects the fact that changes may occur at the level of the interaction within a network or system of variables, that cannot be detected in any individual variable alone [1,2,3,4,5,6] (see Figure 1 for an example). In situations in which there are a large number of variables, for example, gene microarray, proteomic and metabolomic data, and more recently, medical diagnostics, multivariate statistical analyses and data mining approaches have been increasingly employed in order to understand the complex interactions that can occur between systems of variables, as well as to avoid increasing the type I error rate [e.g., [6, 8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]]

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