# Fast functional enrichment Maintainer: Marek Gierlinski (<M.Gierlinski@dundee.ac.uk>) This R package provides a fast and efficient method for functional enrichment analysis, optimized for speed and designed for use in interactive applications, such as *Shiny* apps. ## Quick example To run a *Shiny* app demonstration of `fenr` directly from GitHub, enter the following command in your R console: ``` shiny::runGitHub("bartongroup/fenr-shiny-example") ``` ## Installation `fenr` can be installed using ``` if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("fenr") ``` ## Usage The initial step involves downloading functional term data. `fenr` supports data downloads from *Gene Ontology*, *Reactome*, *KEGG*, and *WikiPathways*. Custom ontologies can also be used, provided they are converted into an appropriate format (refer to the `prepare_for_enrichment` function for more information). The command below downloads functional terms and gene mapping from Gene Ontology (GO) for yeast: ``` go <- fetch_go(species = "sgd") ``` This command returns a list with two tibbles containing term information (`term_id` and `term_name`) and gene-term mapping (`term_id` and `gene_symbol`). We convert this data into an object suitable for fast functional enrichment. `exmpl_all` is an example of gene background provided by the package, which contains a vector with gene symbols related to all detections in an experiment. ``` data(exmpl_all, exmpl_sel) go_terms <- prepare_for_enrichment(go$terms, go$mapping, exmpl_all, feature_name = "gene_symbol") ``` The `go_terms` object is a data structure containing all mappings in a quickly accessible form. From this point on, you can use go_terms to perform multiple functional enrichments on various gene selections. For example, if `exmpl_all` is a vector with all background gene symbols and `exmpl_sel` is a vector with genes of interest (both provided by the package), you can perform functional enrichment analysis using: ``` enr <- functional_enrichment(exmpl_all, exmpl_sel, go_terms) ``` The result is a tibble: ``` # A tibble: 51 × 10 term_id term_name N_with n_with_sel n_expect enrichment odds_ratio ids p_value p_adjust <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> 1 GO:1905356 regulation of snRNA pseudouridine synth… 2 2 0.01 333 Inf TOR2… 8.61e- 6 5.49e- 5 2 GO:0031929 TOR signaling 19 18 0.06 315 41800 TOR2… 0 0 3 GO:0031931 TORC1 complex 11 10 0.03 302 6330 TOR2… 0 0 4 GO:0001558 regulation of cell growth 9 5 0.03 185 544 KOG1… 1.84e-11 2.34e-10 5 GO:0031932 TORC2 complex 15 7 0.05 155 435 TOR2… 4.66e-15 7.93e-14 6 GO:0016242 negative regulation of macroautophagy 9 3 0.03 111 193 KSP1… 1.95e- 6 1.42e- 5 7 GO:0043666 regulation of phosphoprotein phosphatas… 13 4 0.04 102 182 SAP1… 4.24e- 8 4.33e- 7 8 GO:0030950 establishment or maintenance of actin c… 15 4 0.05 88.7 149 TOR2… 8.07e- 8 6.86e- 7 9 GO:0031930 mitochondria-nucleus signaling pathway 9 2 0.03 73.9 105 TOR1… 3.06e- 4 1.3 e- 3 10 GO:0010507 negative regulation of autophagy 12 2 0.04 55.4 73.2 TOR2… 5.58e- 4 2.03e- 3 # ℹ 41 more rows ``` The columns are as follows - `N_with`: The number of features (genes) associated with this term in the background of all genes. - `n_with_sel`: The number of features associated with this term in the selection. - `n_expect`: The expected number of features associated with this term under the null hypothesis (terms are randomly distributed). - `enrichment`: The ratio of observed to expected. - `odds_ratio`: The effect size, represented by the odds ratio from the contingency table. - `ids`: The identifiers of features with the term in the selection. - `p_value`: The raw p-value from the hypergeometric distribution. - `p_adjust`: The p-value adjusted for multiple tests using the Benjamini-Hochberg approach. # Interactive Example A small Shiny app is included in the package to demonstrate the usage of `fenr` in an interactive environment. All time-consuming data loading and preparation tasks are performed before the app is launched. ``` data(yeast_de) term_data <- fetch_terms_for_example(yeast_de) ``` `yeast_de` is the result of differential expression (using `edgeR`) on a subset of 6+6 replicates from [Gierlinski et al. (2015)](https://academic.oup.com/bioinformatics/article/31/22/3625/240923). The function `fetch_terms_for_example` uses `fetch_*` functions from `fenr` to download and process data from *GO*, *Reactome* and *KEGG*. You can view the step-by-step process by examining the function code on [GitHub](https://github.com/bartongroup/fenr/blob/main/R/iteractive_example.R). The object `term_data` is a named list of `fenr_terms` objects, one for each ontology. After completing the slow tasks, you can start the Shiny app by running: ``` enrichment_interactive(yeast_de, term_data) ``` To quickly see how `fenr` works an example can be loaded directly from GitHub: ``` shiny::runGitHub("bartongroup/fenr-shiny-example") ```