Journal
BIOMETRICS
Volume 64, Issue 2, Pages 479-489Publisher
WILEY
DOI: 10.1111/j.1541-0420.2007.00895.x
Keywords
Bayesian analysis; false discovery rate; functional data analysis; functional mixed models; mass spectrometry; proteomics
Funding
- NCI NIH HHS [CA-107304, R01 CA107304-04A1, R01 CA107304] Funding Source: Medline
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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.
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