Multimodel Pathway Enrichment Methods for Functional Evaluation of Expression Regulation

Functional analysis of quantitative expression data is
becoming common practice within the proteomics and transcriptomics
fields; however, a gold standard for this type of analysis has yet
not emerged. To grasp the systemic changes in biological systems,
efficient and robust methods are needed for data analysis following
expression regulation experiments. We discuss several conceptual and
practical challenges potentially hindering the emergence of such methods
and present a novel method, called FEvER, that utilizes two enrichment
models in parallel. We also present analysis of three disparate differential
expression data sets using our method and compare our results to other
established methods. With many useful features such as pathway hierarchy
overview, we believe the FEvER method and its software implementation
will provide a useful tool for peers in the field of proteomics. Furthermore,
we show that the method is also applicable to other types of expression
data.