Parallel Deterministic Annealing Clustering and its Application to LC-MS Data Analysis

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Provided by: Indiana University
Topic: Big Data
Format: PDF
The authors present a scalable parallel deterministic annealing formalism for clustering with cutoffs and position dependent variances. They 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. They find reliably and automatically tens of thousands of clusters starting with a single one that is split recursively as distance resolution is sharpened. They parallelize the algorithm and compare unconstrained and trimmed clusters using data from a human tuberculosis cohort.
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