Bayesian Analysis of Mass Spectrometry Proteomics Data using Wavelet Based Functional Mixed Models
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Abstract:
In this paper, we analyze MALDI-TOF mass spectrometry proteomics data using Bayesian wavelet-based functional mixed models. By modeling mass spectra as functions, this approach avoids reliance on peak detection methods. The flexibility of this framework in modeling nonparametric fixed and random effect functions enables it to simultaneously model the effects of multiple factors, allowing one to perform inference on multiple factors of interest using the same model fit, while adjusting for clinical or experimental covariates that may affect both the intensities and locations of peaks in the spectra. From the model output, we identify spectral regions that are differentially expressed across experimental conditions, while controlling the Bayesian FDR, in a way that takes both statistical and clinical significance into account. We apply this method to two cancer studies.
Subject Area:
Computational Biology/Bioinformatics
Suggested Citation:
Jeffrey S. Morris, Philip J. Brown, Richard C. Herrick, Keith A. Baggerly, and Kevin R. Coombes, "Bayesian Analysis of Mass Spectrometry Proteomics Data using Wavelet Based Functional Mixed Models" (March 2006). UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series. Working Paper 22.
http://www.bepress.com/mdandersonbiostat/paper22