In sigven/oncoEnrichR: Cancer-dedicated gene set interpretation

Key functions

oncoEnrichR performs its operations through the following three functions:

1. `oncoEnrichR::load_db()`

Loads the underlying annotation data repository for oncoEnrichR, and saves it to a local cache directory. Utilizing the googledrive R package

2. `oncoEnrichR::onco_enrich()`

Consists of two main processing steps:

1) Takes an input/query list of human gene/protein identifiers (e.g. UniProt accession, RefSeq/Ensembl transcript identifer etc.) as input and conducts uniform identifier conversion

2) Performs extensive annotation, enrichment and membership analyses of the query set against underlying data sources on cancer-relevant properties of human genes and their interrelationships.
Technically, the method returns a list object with all contents of the analyses performed. The specific arguments/options and default values are outlined below:

r onco_enrich( query = NULL, oeDB = NULL, query_id_type = "symbol", ignore_id_err = TRUE, html_floating_toc = T, html_report_theme = "default", project_title = "_Project title_", project_owner = "_Project owner_", project_description = "_Project description_", bgset = NULL, bgset_id_type = "symbol", bgset_description = "All protein-coding genes", enrichment_p_value_cutoff = 0.05, enrichment_p_value_adj = "BH", enrichment_q_value_cutoff = 0.2, enrichment_min_geneset_size = 10, enrichment_max_geneset_size = 500, enrichment_plot_num_terms = 20, enrichment_simplify_go = TRUE, subcellcomp_min_confidence = 3, subcellcomp_min_channels = 1, subcellcomp_show_cytosol = FALSE, regulatory_min_confidence = "D", fitness_max_score = -2, ppi_add_nodes = 30, ppi_string_min_score = 0.9, ppi_string_network_type = "functional", ppi_biogrid_min_evidence = 3, ppi_node_shadow = TRUE, ppi_show_drugs = TRUE, ppi_show_isolated_nodes = FALSE, show_ppi = TRUE, show_disease = TRUE, show_top_diseases_only = TRUE, show_cancer_hallmarks = TRUE, show_drug = TRUE, show_enrichment = TRUE, show_aberration = TRUE, show_coexpression = TRUE, show_cell_tissue = FALSE, show_ligand_receptor = TRUE, show_regulatory = TRUE, show_unknown_function = TRUE, show_prognostic = TRUE, show_subcell_comp = TRUE, show_synleth = TRUE, show_fitness = TRUE, show_complex = TRUE, show_domain = TRUE)

See detailed descriptions of all options here

3. `oncoEnrichR::write()`

Consists of two main processing steps:

1) Transformation of the raw analysis results returned by oncoEnrichR::onco_enrich() into various visualizations and interactive tables

2) Assembly and generation of the final analysis report through
- A) a structured and interactive oncoEnrichR HTML report
- B) a multisheet Excel workbook

Example run

A target list of n = 134 high-confidence interacting proteins with the c-MYC oncoprotein were previously identified through BioID protein proximity assay in standard cell culture and in tumor xenografts (Dingar et al., J Proteomics, 2015). We ran this target list through the oncoEnrichR analysis workflow using the following configurations for the onco_enrich method:

project_title = "cMYC_BioID_screen"
project_owner = "Raught et al."

and produced the following HTML report with results.

Below are R commands provided to reproduce the example output. NOTE: Replace "LOCAL_FOLDER" with a directory on your local computer:

library(oncoEnrichR)
myc_interact_targets <- read.csv(system.file("extdata","myc_data.csv", package = "oncoEnrichR"), stringsAsFactors = F)
oeDB <- oncoEnrichR::load_db(cache_dir = "LOCAL_FOLDER")
myc_report <- oncoEnrichR::onco_enrich(query = myc_interact_targets$symbol, oeDB = oeDB, show_cell_tissue = T, project_title = "cMYC_BioID_screen", project_owner = "Raught et al.")
oncoEnrichR::write(report = myc_report, oeDB = oeDB, file = "LOCAL_FOLDER/myc_report_oncoenrichr.html", format = "html")
oncoEnrichR::write(report = myc_report, oeDB = oeDB, file = "LOCAL_FOLDER/myc_report_oncoenrichr.xlsx", format = "excel")