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A bayesian mixture model for comparative spectral count data in shotgun proteomics

Reputable Mentor II
Reputable Mentor II
Booth JG, Eilertson KE, Olinares PD, Yu H.
Mol Cell Proteomics. 2011 Aug;10(8):M110.007203.
Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose. Building on some techniques that are now widely accepted in the microarray literature, we developed and implemented a new method using a Bayesian model to calculate posterior probabilities of differential abundance for thousands of proteins in a given experiment simultaneously. Our Bayesian model is shown to deliver uniformly superior performance when compared with several existing methods.
Department of Biological Statistics and Computational Biology, Cornell University, Comstock Hall, Ithaca, NY 14853, USA.
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‎10-15-2021 11:50 AM
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