Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range–targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data. The methodology is likelihood-free, using approximate Bayesian computation implemented via a Markov chain Monte Carlo procedure and a kernel-based Optimal Bayesian Classifier. Extensive experimental results demonstrate that the proposed method outperforms classical methods such as linear discriminant analysis and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these.
Comparing consensus Monte Carlo strategies for distributed Bayesian computation
