posted on 2015-12-17, 06:13authored byMathieu Lavallée-Adam, Navin Rauniyar, Daniel
B. McClatchy, John R. Yates
The majority of large-scale
proteomics quantification methods yield
long lists of quantified proteins that are often difficult to interpret
and poorly reproduced. Computational approaches are required to analyze
such intricate quantitative proteomics data sets. We propose a statistical
approach to computationally identify protein sets (e.g., Gene Ontology
(GO) terms) that are significantly enriched with abundant proteins
with reproducible quantification measurements across a set of replicates.
To this end, we developed PSEA-Quant, a protein set enrichment analysis
algorithm for label-free and label-based protein quantification data
sets. It offers an alternative approach to classic GO analyses, models
protein annotation biases, and allows the analysis of samples originating
from a single condition, unlike analogous approaches such as GSEA
and PSEA. We demonstrate that PSEA-Quant produces results complementary
to GO analyses. We also show that PSEA-Quant provides valuable information
about the biological processes involved in cystic fibrosis using label-free
protein quantification of a cell line expressing a CFTR mutant. Finally,
PSEA-Quant highlights the differences in the mechanisms taking place
in the human, rat, and mouse brain frontal cortices based on tandem
mass tag quantification. Our approach, which is available online,
will thus improve the analysis of proteomics quantification data sets
by providing meaningful biological insights.