running.Rmd

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output: rmarkdown::html_vignette
title: Running
vignette: >
  %\VignetteEngine{knitr::knitr}
  %\VignetteIndexEntry{Running}
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---

## 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](https://googledrive.tidyverse.org/) R package

<br>

### 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](https://sigven.github.io/oncoEnrichR/reference/onco_enrich.html)

<br>

### 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

<br>

## 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](https://www.ncbi.nlm.nih.gov/pubmed/25452129)). 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](https://doi.org/10.5281/zenodo.8051153).

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")`