Ian Dryden
Department of Statistics
University of South Carolina
Mixed Effect Modeling of Proteomic Mass-Spectrometry Data
Statistical methodology for the analysis of proteomic mass-spectrometry data
is proposed using multilevel modeling. Each high-dimensional spectrum is
represented using a near-orthogonal low dimensional basis of Gaussian
functions. Multivariate mixed effect models are proposed in the lower
dimensional space. In particular, differences between groups are
investigated using fixed effect parameters, and individual variability of
spectra is modeled using random effects. A deterministic peak fitting
algorithm provides initial estimates of the near-orthogonal Gaussian basis,
and the estimates are updated using a two-stage iterative method. The
multilevel model is fitted using a parallel procedure for computational
convenience. The methodology is applied to proteomic mass-spectrometry data
from serum samples from melanoma patients categorized as Stage I or Stage
IV, and significant locations of peaks are identified. Finally comparisons
with other methods, including simple feature-based statistics and more
complicated Bayesian Markov chain Monte Carlo inference are also made.
This is joint work with William Browne (University of Bristol, UK) and
Kelly Handley (University of Birmingham, UK)
Back to Colloquium Series