Morris, J.S. and Brown, P.J. and Herrick, R.C. and Baggerly, K.A. and Coombes, K.R. (2008) Bayesian analysis mass spectrometry proteomic data using wavelet-based functional mixed models. BIOMETRICS, 64 (2). pp. 479-489. ISSN 0006-341X.
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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.
|Uncontrolled keywords:||Bayesian analysis; false discovery rate; functional data analysis; functional mixed models; mass spectrometry; proteomics|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science
|Depositing User:||Philip J Brown|
|Date Deposited:||07 Jul 2008 10:58|
|Last Modified:||05 Oct 2012 12:02|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/8188 (The current URI for this page, for reference purposes)|
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