Bayesian Analysis of Mass Spectrometry Proteomics Data using Wavelet Based Functional Mixed Models

摘要

In this paper, we analyze MALDI-TOF mass spectrometry proteomic 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 non-parametric 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. 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.