ReactomeGraph4R: an R interface for the Reactome Graph Database

Local Reactome Graph Database setup

1. Get Docker: https://docs.docker.com/get-docker
2. Download and install the current Reactome Graph Database:

mkdir -p $(pwd)/neo4j/data/databases

# download the db file
wget https://reactome.org/download/current/reactome.graphdb.tgz -P $(pwd)/neo4j/data/databases

# extract
tar -zxvf $(pwd)/neo4j/data/databases/reactome.graphdb.tgz -C $(pwd)/neo4j/data/databases

# run on docker
docker run --name reactome_graph_db -p 7687:7687 -p 7474:7474 -e NEO4J_dbms_allow__upgrade=true -e NEO4J_AUTH=none -v $(pwd)/neo4j/data:/data neo4j:3.5.19

For re-downloading a new release file, remember to remove (docker rm) the old container if using the same container name, and do note the owner and group of the directories. You can also list all containers by docker ps -a.

3. Launch Neo4j through browser:

http://localhost:7474

If you want to set a password, you can remove NEO4J_AUTH=none in the docker run command. The default username and password are both neo4j, after login you will be prompted to change the new password.

Try to explore the Graph Database and query data using Cypher, e.g. getting the database version:

MATCH (dbi:DBInfo) RETURN dbi.version

More details see this tutorial.

The next time you run the Graph Database, you can just start running docker and execute:

docker start reactome_graph_db

Installation

# from Bioconductor (BioC >= 3.13)
if (!requireNamespace("BiocManager")) {
  install.packages("BiocManager")
}
BiocManager::install("ReactomeGraph4R")

# or from GitHub:
devtools::install_github("reactome/ReactomeGraph4R")

Examples

Read the complementary vignette.

Loading and connecting

Load the package

library(ReactomeGraph4R)
login()

The login() is a required step that allows you to connect to your local Neo4j server, by answering two questions:

• Is the url 'http://localhost:7474'? (Yes/no/cancel)
• Does Neo4J require authentication? (Yes/no/cancel)

Results are in these two formats:

• "row": a list of results in dataframes (default)
• "graph": a graph object with nodes and relationships information

Row data

Using Reactome id:

matchPrecedingAndFollowingEvents(event.id = "R-HSA-8983688", type = "row")
#> Retrieving immediately connected instances... Specify depth-related arguments for more depths
#> $precedingEvent
#>   schemaClass  speciesName isInDisease releaseDate            displayName     stIdVersion    dbId                   name isChimeric          stId
#> 1    Reaction Homo sapiens       FALSE  2018-09-12 OAS1 binds viral dsRNA R-HSA-8983671.1 8983671 OAS1 binds viral dsRNA      FALSE R-HSA-8983671
#>   category isInferred
#> 1  binding      FALSE
#> 
#> $event
#>   schemaClass  speciesName isInDisease releaseDate       displayName     stIdVersion    dbId              name isChimeric          stId category
#> 1    Reaction Homo sapiens       FALSE  2018-09-12 OAS1 oligomerizes R-HSA-8983688.1 8983688 OAS1 oligomerizes      FALSE R-HSA-8983688  binding
#>   isInferred
#> 1      FALSE
#> 
#> $followingEvent
#>      schemaClass  speciesName isInDisease releaseDate                   displayName     stIdVersion    dbId                          name isChimeric
#> 1 Polymerisation Homo sapiens       FALSE  2018-09-12 OAS1 produces oligoadenylates R-HSA-8983680.1 8983680 OAS1 produces oligoadenylates      FALSE
#>            stId   category isInferred
#> 1 R-HSA-8983680 transition      FALSE
#> 
#> $relationships
#>   neo4jId           type startNode.neo4jId startNode.dbId startNode.schemaClass endNode.neo4jId endNode.dbId endNode.schemaClass properties
#> 1 7685968 precedingEvent           1808570        8983680        Polymerisation         1808457      8983688            Reaction       1, 0
#> 2 7685746 precedingEvent           1808457        8983688              Reaction         1808512      8983671            Reaction       1, 0

Using non-Reactome id:

row <- matchHierarchy(id = "P33992", databaseName = "UniProt", type = "row")
str(row, max.level = 1)
#> List of 5
#>  $ referenceEntity:'data.frame':	1 obs. of  17 variables:
#>  $ physicalEntity :'data.frame':	1 obs. of  12 variables:
#>  $ event          :'data.frame':	1 obs. of  12 variables:
#>  $ upperevent     :'data.frame':	6 obs. of  18 variables:
#>  $ relationships  :'data.frame':	8 obs. of  9 variables:

row looks like the returned result of the last command.

Graph data

Every graph result contains two dataframes - "nodes" and "relationships".

graph <- matchHierarchy(id = "P33992", databaseName = "UniProt", type = "graph")
str(graph, max.level = 2)
#> List of 2
#>  $ nodes        :'data.frame':	9 obs. of  3 variables:
#>   ..$ id        : chr [1:9] "422883" "90375" "422833" "90376" ...
#>   ..$ label     :List of 9
#>   ..$ properties:List of 9
#>  $ relationships:'data.frame':	8 obs. of  5 variables:
#>   ..$ id        : chr [1:8] "1700863" "1701159" "360682" "1813950" ...
#>   ..$ type      : chr [1:8] "output" "hasEvent" "referenceEntity" "hasEvent" ...
#>   ..$ startNode : chr [1:8] "422833" "422883" "90375" "449402" ...
#>   ..$ endNode   : chr [1:8] "90375" "422833" "90376" "422883" ...
#>   ..$ properties:List of 8

graph could then be converted to objects in network visualization packages. The following is an example to get a visNetwork object from graph.

Extract nodes and edges from our result:

relationships <- graph[["relationships"]]
nodes <- graph[["nodes"]]
nodes <- unnestListCol(df = nodes, column = "properties")

Then select some columns:

library(stringr)
vis.nodes <- data.frame(id = nodes$id,
                        label = str_trunc(nodes$displayName, 20),
                        group = nodes$schemaClass,
                        title = paste0("<p><b>", nodes$schemaClass, "</b><br>",
                                "dbId: ", nodes$dbId, "<br>", nodes$displayName, "</p>"))

vis.edges <- data.frame(from = relationships$startNode,
                        to = relationships$endNode,
                        label = relationships$type,
                        font.size = 16,
                        font.color = 'steelblue')

Add parameters for nodes:

library(wesanderson) # for color palette

node.colors <- as.character(wes_palette(n = length(unique(vis.nodes$group)), name = "Darjeeling2"))
names(node.colors) <- levels(factor(vis.nodes$group))
vis.nodes$color.background <- node.colors[as.numeric(factor(vis.nodes$group))]
vis.nodes$color.border <- "lightgray"
vis.nodes$color.border[vis.nodes$label == "UniProt:P33992 MCM5"] <- "pink"
vis.nodes$color.highlight.border <- "darkred"
vis.nodes$borderWidth <- 2 # Node border width

Add parameters for edges:

vis.edges$width <- 1.2
edges.colors <- as.character(wes_palette(n = length(unique(vis.edges$label)), name = "FantasticFox1"))
names(edges.colors) <- unique(vis.edges$label)
vis.edges$color <- str_replace_all(vis.edges$label, edges.colors)
vis.edges$arrows <- "to"
vis.edges$smooth <- TRUE

library(visNetwork)
visNetwork(vis.nodes, vis.edges, main = "The hierarchy of protein MCM5",
           height = "500px", width = "100%")

Find the interactive graphs in the vignette!

Feedback

We'd love to hear your feedback! Feel free to contribute any new features and/or open an issue on GitHub.