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Related Experiment Video

Updated: Jun 25, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable

Tarja Rajalahti¹, Reidar Arneberg, Ann C Kroksveen

¹Department of Clinical Medicine, University of Bergen, Bergen, Norway.

Analytical Chemistry

|February 21, 2009

Summary

The discriminating variable (DIVA) test and selectivity ratio (SR) plot offer new quantitative methods for identifying key differences in complex sample data. These tools enhance the interpretation of spectral and chromatographic profiles for improved group discrimination.

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Published on: November 29, 2024

Area of Science:

Chemometrics
Analytical Chemistry
Data Analysis

Background:

Identifying discriminating variables in complex spectral or chromatographic data is crucial for sample group analysis.
Existing methods may lack quantitative rigor or clear interpretability for variable importance.

Purpose of the Study:

To introduce and validate the discriminating variable (DIVA) test and selectivity ratio (SR) plot as quantitative tools for variable selection.
To objectively identify and rank variables that best discriminate between sample groups.

Main Methods:

Development of the selectivity ratio (SR) plot for visualizing variable importance.
Implementation of the nonparametric DIVA test to quantify discriminatory ability as probability for correct classification.
Application of SR plots and DIVA test to matrix-assisted laser desorption ionization (MALDI) mass spectrometry data of cerebrospinal fluid (CSF) samples.

Main Results:

SR plots visually highlight the most discriminating variables in spectral profiles.
The DIVA test provides a quantitative probability measure (mean correct classification rate) for SR intervals.
The approach successfully identified discriminating mass-to-charge (m/z) regions in MALDI-MS data, validated against established methods.

Conclusions:

The DIVA test and SR plot provide a robust, quantitative framework for variable selection in multivariate data analysis.
These tools enable objective threshold definition and selection of discriminating variables, improving data interpretation.
The validated approach enhances the analysis of complex spectral and chromatographic data, particularly in fields like metabolomics and proteomics.