Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial

Classification of Samples with Order-Restricted Discriminant Rules

In recent years, mass spectrometry techniques have helped proteomics to become a powerful tool for the early diagnosis of cancer, as they help to discover protein profiles specific to each pathological state. One of the questions where proteomics is giving useful practical results is that of classifying patients into one of the possible severity levels of an illness, based on some features measured on the patient. This classification is usually made using one of the many discrimination procedures available in statistical literature. We present in this chapter recently developed restricted discriminant rules that use additional information in terms of orderings on the means, and we illustrate how to apply them to mass spectrometry data using R package dawai. Specifically, we use proteomic prostate cancer data, and we describe all steps needed, including data preprocessing and feature extraction, to build a discriminant rule that classifies samples in one of several disease stages, thus helping diagnosis. The restricted discriminant rules are compared with some standard classifiers that do not take into account the additional information, showing better performance in terms of error rates.