A statistical Testing Procedure for Validating Class Labels

TL;DR

This paper introduces a non-parametric statistical testing method to validate class labels, particularly in proteomics, effectively identifying mislabeled instances while controlling error rates without distributional assumptions.

Contribution

It presents a novel, distribution-free testing procedure for label validation that controls error probabilities and is effective even with high mislabeling rates.

Findings

Maintains high specificity with up to 25% mislabeled instances

Effectively reduces mislabeled instances in proteomics workflows

Controls Type I and II error probabilities simultaneously

Abstract

Motivated by an open problem of validating protein identities in label-free shotgun proteomics work-flows, we present a testing procedure to validate class/protein labels using available measurements across instances/peptides. More generally, we present a solution to the problem of identifying instances that are deemed, based on some distance (or quasi-distance) measure, as outliers relative to the subset of instances assigned to the same class. The proposed procedure is non-parametric and requires no specific distributional assumption on the measured distances. The only assumption underlying the testing procedure is that measured distances between instances within the same class are stochastically smaller than measured distances between instances from different classes. The test is shown to simultaneously control the Type I and Type II error probabilities whilst also controlling the overall error probability of the repeated testing invoked in the validation procedure of initial class labeling. The theoretical results are supplemented with results from an extensive numerical study, simulating a typical setup for labeling validation in proteomics work-flow applications. These results illustrate the applicability and viability of our method. Even with up to 25% of instances mislabeled, our testing procedure maintains a high specificity and greatly reduces the proportion of mislabeled instances.