Spectral Index for Assessment of Differential Protein Expression in Shotgun Proteomics

Detecting differentially expressed proteins is a key goal of proteomics.
We describe a label-free method, the spectral index, for analyzing
relative protein abundance in large-scale data sets derived from biological
samples by shotgun proteomics. The spectral index is comprised of
two biochemically plausible features: relative protein abundance (assessed
by spectral counts) and the number of samples within a group with
detectable peptides. We combined the spectral index with permutation
analysis to establish confidence intervals for assessing differential
protein expression in bronchoalveolar lavage fluid from cystic fibrosis
and control subjects. Significant differences in protein abundance
determined by the spectral index agreed well with independent biochemical
measurements. When used to analyze simulated data sets, the spectral
index outperformed four other statistical tests (Student’s t-test, G-test, Bayesian t-test, and Significance Analysis of Microarrays) by correctly identifying
the largest number of differentially expressed proteins. Correspondence
analysis and functional annotation analysis indicated that the spectral
index improves the identification of enriched proteins corresponding
to clinical phenotypes. The spectral index is easily implemented and
statistically robust, and its results are readily interpreted graphically.
Therefore, it should be useful for biomarker discovery and comparisons
of protein expression between normal and disease states.