Differential protein expression and peak selection in mass spectrometry data by binary discriminant analysis

TL;DR

This paper presents a simple, computationally efficient binary discriminant analysis method for identifying differentially expressed proteins in mass spectrometry data, improving biomarker discovery in clinical diagnostics.

Contribution

It introduces a novel data-adaptive thresholding and ranking approach for differential protein expression analysis applicable to multi-group settings, implemented in the R package binda.

Findings

Achieves prediction accuracy comparable to random forests on large-scale data

Identifies biologically relevant, statistically predictive marker peaks in cancer studies

Provides an accessible R package for widespread use

Abstract

Motivation: Proteomic mass spectrometry analysis is becoming routine in clinical diagnostics, for example to monitor cancer biomarkers using blood samples. However, differential proteomics and identification of peaks relevant for class separation remains challenging. Results: Here, we introduce a simple yet effective approach for identifying differentially expressed proteins using binary discriminant analysis. This approach works by data-adaptive thresholding of protein expression values and subsequent ranking of the dichotomized features using a relative entropy measure. Our framework may be viewed as a generalization of the `peak probability contrast' approach of Tibshirani et al. (2004) and can be applied both in the two-group and the multi-group setting. Our approach is computationally inexpensive and shows in the analysis of a large-scale drug discovery test data set equivalent prediction accuracy as a random forest. Furthermore, we were able to identify in the analysis of mass spectrometry data from a pancreas cancer study biological relevant and statistically predictive marker peaks unrecognized in the original study. Availability: The methodology for binary discriminant analysis is implemented in the R package binda, which is freely available under the GNU General Public License (version 3 or later) from CRAN at URL http://cran.r-project.org/web/packages/binda/ . R scripts reproducing all described analyzes are available from the web page http://strimmerlab.org/software/binda/ .