  Gene set analysis, a popular approach for analyzing high-throughput gene
expression data, aims to identify sets of related genes that show significantly
enriched or depleted expression patterns between different conditions. In the
last years, a multitude of methods and corresponding tools have been developed
for this task. However, clear guidance is lacking: choosing the right method is
the first hurdle a researcher is confronted with. No less challenging than
overcoming this so-called method uncertainty is the procedure of preprocessing,
from knowing which steps are required to selecting a corresponding approach
from the plethora of valid options to create the accepted input object (data
preprocessing uncertainty), with clear guidance again being scarce. Here, we
provide a practical guide through all steps required to conduct gene set
analysis, beginning with a concise overview of a selection of established
methods, including GSEA and DAVID. We thereby lay a special focus on reviewing
and explaining the necessary preprocessing steps for each method under
consideration (e.g. the necessity of a transformation of the RNA-Seq data)-an
essential aspect that is typically paid only limited attention to in both
existing reviews and applications. To raise awareness of the spectrum of
uncertainties, our review is accompanied by an extensive overview of the
literature on valid approaches for each step and illustrative R code
demonstrating the complex analysis pipelines. It ends with a discussion and
recommendations to both users and developers to ensure that the results of gene
set analysis are, despite the above-mentioned uncertainties, replicable and
transparent.
