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Bayesian analysis mass spectrometry proteomic data using wavelet-based functional mixed models

Morris, Jeffrey S., Brown, Philip J., Herrick, Richard C., Baggerly, Keith A., Coombes, Kevin R. (2008) Bayesian analysis mass spectrometry proteomic data using wavelet-based functional mixed models. Biometrics, 64 (2). pp. 479-489. ISSN 0006-341X. (doi:10.1111/j.1541-0420.2007.00895.x) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:8188)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1111/j.1541-0420.2007.00895.x

Abstract

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.

Item Type: Article
DOI/Identification number: 10.1111/j.1541-0420.2007.00895.x
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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Philip Brown
Date Deposited: 07 Jul 2008 10:58 UTC
Last Modified: 16 Nov 2021 09:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/8188 (The current URI for this page, for reference purposes)

University of Kent Author Information

Brown, Philip J..

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