The fenr R package enables rapid functional enrichment analysis, typically completing in a fraction of a second, which makes it well-suited for interactive applications, such as Shiny apps. To accomplish this, fenr pre-downloads functional data (e.g., GO terms or KEGG pathways) and stores them in a format optimized for swift analysis of any arbitrary selection of features, including genes or proteins.
fenr 1.2.1
Functional enrichment analysis determines if specific biological functions or pathways are overrepresented in a set of features (e.g., genes, proteins). These sets often originate from differential expression analysis, while the functions and pathways are derived from databases such as GO, Reactome, or KEGG. In its simplest form, enrichment analysis employs Fisher’s test to evaluate if a given function is enriched in the selection. The null hypothesis asserts that the proportion of features annotated with that function is the same between selected and non-selected features.
Functional enrichment analysis requires downloading large datasets from the aforementioned databases before conducting the actual analysis. While downloading data is time-consuming, Fisher’s test can be performed rapidly. This package aims to separate these two steps, enabling fast enrichment analysis for various feature selections using a given database. It is specifically designed for interactive applications like Shiny. A small Shiny app, included in the package, demonstrates the usage of fenr.
Functional enrichment analysis should not be considered the ultimate answer in understanding biological systems. In many instances, it may not provide clear insights into biology. Specifically, when arbitrary groups of genes are selected, enrichment analysis only reveals the statistical overrepresentation of a functional term within the selection, which may not directly correspond to biological relevance. This package serves as a tool for data exploration; any conclusions drawn about biology require independent validation and further investigation.
fenr can be installed from Bioconductor by using:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("fenr")The input data for fenr consists of two tibbles or data frames (see figure below). One tibble contains functional term descriptions, while the other provides the term-to-feature mapping. Users can acquire these datasets using the convenient helper functions like fetch_*, which facilitate data retrieval from GO, KEGG, BioPlanet, or WikiPathways. Alternatively, users have the option to provide their own datasets. These tibbles are subsequently transformed into a fenr_terms object, specifically designed for efficient data retrieval and access. The resulting object is then employed by the functional_enrichment function to perform enrichment analysis for any chosen feature selection.