Set-based test procedures for the functional analysis of protein lists from differential analysis

Abstract

The analysis of most high-throughput proteomics experiments involves the selection of differentially expressed proteins or peptides between two different sets of samples, e.g., from two experimental groups. As a result, a large list of selected features is reported, typically sorted by a measure for the expression fold change and a p-value from a statistical test. The biological interpretation of such a list is usually difficult since the features can typically be assigned to a large variety of biological classes. To facilitate the biological interpretation, set-based procedures focus on the analysis of feature subsets that all belong to the same biological class (e.g., same cellular component, biological process, molecular function, or pathway). Setbased procedures can roughly be divided into “enrichment methods” and “global test procedures,” where the first involve all features of an experiment and the second only those features of a particular set. In this chapter we detail the working principle of these kind of statistical methods and describe how features can be classified into molecular subsets. We illustrate the use of the methods on a data example from a proteomics Parkinson study.