---
title: "2.E: STRING & rbioapi"
author: "Moosa Rezwani"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette:
    toc: true
    anchor_sections: true
    number_sections: true
    self_contained: true
    dev: png
    encoding: 'UTF-8'

vignette: >
  %\VignetteIndexEntry{2.E: STRING & rbioapi}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r message=FALSE, include=FALSE, results="hide", setup, echo=FALSE}
knitr::opts_chunk$set(echo = TRUE,
                      eval = TRUE,
                      message = FALSE,
                      warning = FALSE,
                      collapse = TRUE,
                      tidy = FALSE,
                      cache = FALSE,
                      dev = "png",
                      comment = "#>")
library(rbioapi)
rba_options(timeout = 600)
```

------------------------------------------------------------------------

# Introduction {#introduction .heading2}

STRING is a comprehensive database of protein-protein interactions (PPI) that in version 11.0, covers 24,584,628 proteins from 5,090 organisms. Directly quoting from their paper:

> The STRING database aims to collect, score and integrate all publicly available sources of protein--protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions.
>
> (source: Szklarczyk, Damian, et al. "STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets." *Nucleic acids research* 47.D1 (2019): D607-D613. )

------------------------------------------------------------------------

# Note about species argument {#note-about-species-argument .heading2}

You can find an argument named "species" in every rbioapi STRING function. Providing the species argument is not mandatory, but it has been recommended in STRING API's documentation to always specify the species. An exception is when your input proteins' vector length is more than 100; In such cases, the species argument is required. Otherwise, calling the function without providing the species will produce an ERROR.

------------------------------------------------------------------------

# Map your IDs to STRING IDs {#map-your-ids-to-string-ids .heading2}

Although STRING API resources will handle and recognize a variety of identifiers, it is recommended that you first map your IDs to STRING IDs before using them in other rbioapi STRING functions.

```{r rba_string_map_ids}
## 1 We create a variable with our genes' NCBI IDs
proteins <- c("p53", "BRCA1", "cdk2", "Q99835", "CDC42","CDK1","KIF23",
              "PLK1","RAC2","RACGAP1","RHOA","RHOB", "PHF14", "RBM3")
## 2 Now we map our protein IDs
proteins_mapped <- rba_string_map_ids(ids = proteins,
                                      species = 9606)
## 3 Using str(), we inspect what the function returns:
# str(proteins_mapped)

## 4 What we need and will use for the rest of this vignette is the `stringId` column:
proteins_mapped <- proteins_mapped$stringId
```

------------------------------------------------------------------------

# Get interaction network of a protein set {#get-interaction-network-of-a-protein-set .heading2}

You can retrieve a list of interactions that the proteins in your set have with each other along with the STRING annotations of each interaction. You may filter the results by using required_score and network_type arguments.

Refer to the 'values' section `rba_string_interactions_network` function's manual for information on the returned columns.

```{r rba_string_interactions_network}
int_net <- rba_string_interactions_network(ids = proteins_mapped,
                                          species = 9606,
                                          required_score = 500)
```

```{r rba_string_interactions_network_results, echo=FALSE}
DT::datatable(data = int_net,
              options = list(scrollX = TRUE, 
                             paging = TRUE,
                             fixedHeader = TRUE,
                             keys = TRUE,
                             pageLength = 5))
```

------------------------------------------------------------------------

# Get interaction partners of a protein set {#get-interaction-partners-of-a-protein-set .heading2}

In the last example, we only obtained the interaction which our proteins have among themselves, what if we wanted to get a list of every protein which interact with our protein(s)?

To do that, we can use `rba_string_interaction_partners`:

```{r rba_string_interaction_partners}
## Although we provide only one protein ID here (CD40 protein), you can provide a vector of proteins as the input
int_partners <- rba_string_interaction_partners(ids = "9606.ENSP00000361359",
                                               species = 9606,
                                               required_score = 900)
```

```{r rba_string_interaction_partners_restults, echo=FALSE}
DT::datatable(data = int_partners,
              options = list(scrollX = TRUE, 
                             paging = TRUE,
                             fixedHeader = TRUE,
                             keys = TRUE,
                             pageLength = 5))
```

------------------------------------------------------------------------

# Get network image of a protein set {#get-network-image-of-a-protein-set .heading2}

Let's go back to the interaction network. As you must have seen in the STRING webpages, STRING plots the interaction network of your proteins with many customizations available. You can also do that with STRING API services. `rba_string_network_image` function is very flexible and you have a variety of options; Refer to the function's manual.

```{r rba_string_network_image_ex1, fig.show='hide'}
## Example 1:
graph_1 <- rba_string_network_image(ids = proteins_mapped,
                                   image_format = "image",
                                   species = 9606,
                                   save_image = FALSE,
                                   required_score = 500,
                                   network_flavor = "confidence")
```

```{r rba_string_network_image_ex1_image, echo=FALSE, fig.cap="Network images - Example 1", fig.align='center'}
grid::grid.raster(graph_1, just = "center")
```

```{r rba_string_network_image_ex2, fig.show='hide'}
## Example 2:
graph_2 <- rba_string_network_image(ids = proteins_mapped,
                                   image_format = "image",
                                   species = 9606,
                                   save_image = FALSE,
                                   required_score = 500,
                                   add_color_nodes = 5,
                                   add_white_nodes = 5,
                                   network_flavor = "actions")
```

```{r rba_string_network_image_ex2_image, echo=FALSE, fig.cap="Network images - Example 2", fig.align='center'}
grid::grid.raster(graph_2, just = "center")
```

------------------------------------------------------------------------

# Enrichment using STRING {#enrichment-using-string .heading2}

STRING let you perform two types of enrichments. Refer to [STRING's paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531103/ "STRING v9.1: protein-protein interaction networks, with increased coverage and integration") for more information.

## Functional enrichment {#functional-enrichment .heading3}

The first type is the conventional type, which statistically tests your provided gene sets against some sets of annotation. Currently, STRING supports Gene Ontology, KEGG pathways, UniProt Keywords, PubMed publications, Pfam domains, InterPro domains, and SMART domains. ([source](https://version11.string-db.org/help/api/#getting-functional-enrichment "STRING API - Getting functional enrichment")).

```{r rba_string_enrichment}
enriched <- rba_string_enrichment(ids = proteins_mapped,
                                 species = 9606)
```

```{r rba_string_enrichment_restults, echo=FALSE}
DT::datatable(data = enriched,
              options = list(scrollX = TRUE, 
                             paging = TRUE,
                             fixedHeader = TRUE,
                             keys = TRUE,
                             pageLength = 5))
```

## Protein-protein interaction enrichment {#protein-protein-interaction-enrichment .heading3}

Even without incorporating annotation data, STRING can calculate if your proteins are functionally related. Briefly, STRING accomplishes this by comparing the interactions' distribution in your protein-set to the interactions' distribution in the proteome. Read [STRING's paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531103/#__sec4title "STRING v9.1: protein-protein interaction networks, with increased coverage and integration") for more information.

```{r rba_string_enrichment_ppi}
rba_string_enrichment_ppi(ids = proteins_mapped,
                          species = 9606)
```

------------------------------------------------------------------------

# Get functional annotations {#get-functional-annotations .heading2}

As you have seen above, STRING maps the proteins to multiple annotation sources. You can obtain any annotation associated with your proteins without performing enrichment analysis and retrieving just the significant portion.

```{r rba_string_annotations, eval=FALSE}
annotations <- rba_string_annotations(ids = "9606.ENSP00000269305",
                                     species = 9606)

## This function returns large results, so the results was not shown in this vignette.
```

------------------------------------------------------------------------

# See also in Functions' manuals {#see-also-in-functions-manuals .heading2}

Some rbioapi STRING functions were not covered in this vignette, please check their manuals:

-   `rba_string_homology_intra()`

-   `rba_string_homology_inter()`

-   `rba_string_version()`

# Session info {#session-info .heading2}

```{r sessionInfo, echo=FALSE}
sessionInfo()
```
