Biological Network Module-Based Model for the Analysis of Differential Expression in Shotgun Proteomics

Protein
differential expression analysis plays an important role
in the understanding of molecular mechanisms as well as the pathogenesis
of complex diseases. With the rapid development of mass spectrometry,
shotgun proteomics using spectral counts has become a prevailing method
for the quantitative analysis of complex protein mixtures. Existing
methods in differential proteomics expression typically carry out
analysis at the single-protein level. However, it is well-known that
proteins interact with each other when they function in biological
processes. In this study, focusing on biological network modules,
we proposed a negative binomial generalized linear model for differential
expression analysis of spectral count data in shotgun proteomics.
In order to show the efficacy of the model in protein expression analysis
at the level of protein modules, we conducted two simulation studies
using synthetic data sets generated from theoretical distribution
of count data and a real data set with shuffled counts. Then, we applied
our method to a colorectal cancer data set and a nonsmall cell lung
cancer data set. When compared with single-protein analysis methods,
the results showed that module-based statistical model which takes
account of the interactions among proteins led to more effective identification
of subtle but coordinated changes at the systems level.