Parallel deterministic annealing clustering and its application to LC-MS data analysis

We present a scalable parallel deterministic annealing formalism for clustering with cutoffs and position-dependent variances. We apply it to the “peak matching" problem of the precise identification of the common LC-MS peaks across a cohort of multiple biological samples in proteomic biomarker discovery. We reliably and automatically find tens of thousands of clusters starting with a single one that is split recursively as distance resolution is sharpened. We parallelize the algorithm and compare unconstrained and trimmed clusters using data from a human tuberculosis cohort.