Bayesian methods for proteomic biomarker development

• An introduction to Bayesian inference is provided.
• The advantages of using a Bayesian framework are discussed.
• The literature on Bayesian models in proteomics is reviewed.
• New and emerging Bayesian methods from the statistical literature are discussed.
• A tutorial with R code to run Bayesian models for a proteomic experiment is given.

The advent of liquid chromatography mass spectrometry has seen a dramatic increase in the amount of data derived from proteomic biomarker discovery. These experiments have seemingly identified many potential candidate biomarkers. Frustratingly, very few of these candidates have been evaluated and validated sufficiently such that that they have progressed to the stage of routine clinical use. It is becoming apparent that the statistical methods used to evaluate the performance of new candidate biomarkers are a major limitation in their development. Bayesian methods offer some advantages over traditional statistical and machine learning methods. In particular they can incorporate external information into current experiments so as to guide biomarker selection. Further, they can be more robust to over-fitting than other approaches, especially when the number of samples used for discovery is relatively small.In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.

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