ABSTRACT
Advances in technology have allowed science to evolve from experiments that measure the expression of only one or very few genes to experiments that quantify the expression of many hundreds or even thousands of genes at the same time. Most omic experiments result in large lists of genes that are relevant for a specific phenotype or condition. Functional enrichment analysis is one strategy devoted to pinpoint functional associations. This chapter describes the most used annotation sources, such as Gene Ontologies, as only functions previously characterized can be assessed within the data. It also reviews the main types of functional enrichment analysis: Singular Enrichment Analysis, Modular Enrichment Analysis and Gene Set Enrichment Analysis. This chapter discusses the statistics behind each analysis as well as their assumptions, advantages and limitations. This chapter also reviews the statistical methods applied to adjust the p-value since each analysis requires multiple non completely independent tests that are done simultaneously. This chapter contains a table listing the available tools that carry out any kind of functional enrichment analysis, along with the implemented statistical methods and multiple testing corrections as well as the available annotations. Finally, this chapter discusses the main limitations of this type of analysis.
