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.

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, 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 = FALSE, show_coexpression = FALSE, show_ligand_receptor = FALSE, show_regulatory = FALSE, show_unknown_function = TRUE, show_prognostic = TRUE, show_subcell_comp = FALSE, show_synleth = FALSE, show_fitness = TRUE, show_complex = TRUE, show_domain = FALSE)

See detailed descriptions of all options here Note that not all modules of the report are enabled by default. The user can enable or disable specific modules by setting the corresponding show_ arguments to TRUE or FALSE.

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, 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")