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Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet‐Based Functional Mixed Models
Author(s) -
Morris Jeffrey S.,
Brown Philip J.,
Herrick Richard C.,
Baggerly Keith A.,
Coombes Kevin R.
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00895.x
Subject(s) - bayesian probability , wavelet , covariate , computer science , false discovery rate , bayesian inference , nonparametric statistics , random effects model , inference , statistical inference , data mining , frequentist inference , pattern recognition (psychology) , algorithm , statistics , artificial intelligence , mathematics , machine learning , chemistry , meta analysis , medicine , biochemistry , gene
Summary In this article, we apply the recently developed Bayesian wavelet‐based functional mixed model methodology to analyze MALDI‐TOF mass spectrometry proteomic data. 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 model the effects of multiple factors simultaneously, 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. For example, this provides a straightforward way to account for systematic block and batch effects that characterize these data. From the model output, we identify spectral regions that are differentially expressed across experimental conditions, in a way that takes both statistical and clinical significance into account and controls the Bayesian false discovery rate to a prespecified level. We apply this method to two cancer studies.