| Title: | Tidy, Integrated Gene Annotation |
|---|---|
| Description: | A framework for intuitive, multi-source gene and protein annotation, with a focus on integrating functional genomics with disease and drug data for translational insights. Methods used include g:Profiler (Raudvere et al. (2019) <doi:10.1093/nar/gkz369>), biomaRt (Durinck et al. (2009) <doi:10.1038/nprot.2009.97>), and the Open Targets Platform (Koscielny et al. (2017) <doi:10.1093/nar/gkw1055>). |
| Authors: | MD. Arshad [aut, cre] |
| Maintainer: | MD. Arshad <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.1 |
| Built: | 2026-05-28 08:27:22 UTC |
| Source: | https://github.com/sulkysubject37/annotar |
Augments an annotaR object with disease association data from the OpenTargets platform.
add_disease_links(annotaR_object, score_threshold = 0.5)add_disease_links(annotaR_object, score_threshold = 0.5)
annotaR_object |
A tibble, typically from |
score_threshold |
Minimum association score (from 0 to 1) to include. Defaults to 0.5. |
A new tibble with the original data joined with disease association columns (disease_name, association_score).
annotaR(c("TP53", "EGFR")) %>% add_disease_links(score_threshold = 0.8)annotaR(c("TP53", "EGFR")) %>% add_disease_links(score_threshold = 0.8)
Augments an annotaR object with known drug/compound data from the OpenTargets platform. This includes the drug name, type, mechanism of action, and clinical trial phase.
add_drug_links(annotaR_object)add_drug_links(annotaR_object)
annotaR_object |
A tibble, typically from |
A new tibble with the original data joined with drug association columns (e.g., drug_name, drug_type, mechanism_of_action, phase).
annotaR(c("EGFR", "BRAF")) %>% add_drug_links()annotaR(c("EGFR", "BRAF")) %>% add_drug_links()
Augments an annotaR object with functional enrichment data from g:Profiler. It performs a Gene Ontology (GO) analysis on the gene list and joins the results.
add_go_terms(annotaR_object, organism = "hsapiens", sources = c("GO:BP"), ...)add_go_terms(annotaR_object, organism = "hsapiens", sources = c("GO:BP"), ...)
annotaR_object |
A tibble, typically the output of |
organism |
The organism name to use for the query (e.g., "hsapiens").
Passed to |
sources |
A vector of data sources to query. Defaults to GO Biological
Process. See |
... |
Additional parameters passed on to |
A new tibble with the original 'gene' column joined with functional annotation columns (e.g., term_id, term_name, p_value, source).
annotaR(c("TP53", "EGFR")) %>% add_go_terms()annotaR(c("TP53", "EGFR")) %>% add_go_terms()
Initializes the annotation pipeline by creating a tibble from a character vector of gene symbols. This is the entry point for a typical annotaR workflow.
annotaR(genes)annotaR(genes)
genes |
A character vector of HGNC gene symbols (e.g., c("TP53", "BRCA1")). |
A tibble with a single column 'gene', ready to be used in downstream annotation functions.
my_genes <- c("TP53", "EGFR", "BRCA1") annotaR(my_genes)my_genes <- c("TP53", "EGFR", "BRCA1") annotaR(my_genes)
Creates a publication-ready dot plot from the results of an
add_go_terms() call. The plot shows the top enriched terms, with dot
size representing the number of genes and color representing the p-value.
plot_enrichment_dotplot( annotaR_object, n_terms = 20, title = "Top GO Enrichment Results" )plot_enrichment_dotplot( annotaR_object, n_terms = 20, title = "Top GO Enrichment Results" )
annotaR_object |
An object processed by |
n_terms |
The maximum number of top terms to display, ordered by p-value. Defaults to 20. |
title |
The title of the plot. |
A ggplot object.
# Create a dummy annotaR object with enrichment data annotated_data <- tibble::tibble( gene = c("TP53", "TP53", "EGFR"), term_name = c("Cell cycle", "Apoptosis", "Cell cycle"), p_value = c(0.001, 0.005, 0.001), source = "GO:BP", intersection = "TP53,EGFR" ) plot_enrichment_dotplot(annotated_data)# Create a dummy annotaR object with enrichment data annotated_data <- tibble::tibble( gene = c("TP53", "TP53", "EGFR"), term_name = c("Cell cycle", "Apoptosis", "Cell cycle"), p_value = c(0.001, 0.005, 0.001), source = "GO:BP", intersection = "TP53,EGFR" ) plot_enrichment_dotplot(annotated_data)