Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network of molecular reactions. Graph database technology is an effective tool for modeling highly connected data, hence Reactome’s relational database is imported in Neo4j to create one large interconnected graph. Instances are represented as nodes and relationships between nodes as edges.
The ReactomeGraph4R
package is an R interface for retrieving data with network
structure from the Reactome Graph
Database. There is another R package, ReactomeContentService4R
,
for querying specific bits of information from the
Reactome Database through the RESTful API in the Content Service.
ReactomeGraph4R
is built on the Neo4j driver neo4r
,
thus returned data are mainly same as those called by neo4r
but with a little modifications, and are in these two formats:
nodes
and
relationships
information that can be used for
visualizationThis package will allow you to interact with the data in Reactome’s
graph database in R, with the aim of minimizing the number of Neo4j Cypher
queries that the user will need to perform. For example, if you wanted
to retrieve any Reactome information associated with the hypothetical
identifier ‘123456789’, you can use
matchObject(id="123456789")
, which would be equivalent to
using the Cypher query
MATCH (rgp:ReferenceGeneProduct) WHERE rgp.identifier = "123456789" RETURN rgp
on the Reactome graph database.
Aside from performing basic Cypher queries and formatting the results
as R objects, the package also contains functionality that can not be
easily performed using Cypher. This includes finding hierarchical data
of an instance (for example what Reactions and Pathways a Protein is
found in), getting the entire Reaction sequence/context using
preceding/following relationships, what role a PhysicalEntity plays in
each of its associated Reactions (catalyst, regulator, input, etc.),
searching for research papers that are cited in Reactome’s curations,
and even displaying network data. Please read on to see detailed
instructions for the ReactomeGraph4R
package - it is a
flexible package with plenty of useful functionality for the prospective
R-Reactome user!
Follow this instruction
to download and setup the Reactome Graph Database, then install
ReactomeGraph4R
package.
There are two questions needed to be answered for Neo4j server
connection when loading the package. You can change the url port if it’s
not 7474
. And if the Neo4j authentication is required, the
username and password are same as the ones to log in your local Neo4j
database.
library(ReactomeGraph4R)
login()
## Is the url 'http://localhost:7474'? (Yes/no/cancel)
## Does Neo4J require authentication? (yes/No/cancel)
## Successfully connected to the local Reactome Graph Database v76!
The basic function matchObject
allows you to fetch
Reactome objects using:
id
: Reactome or non-Reactome identifier (e.g. UniProt
id)displayName
: display name of an objectschemaClass
: schema classproperty
: attributes of Reactome objectsrelationship
: relationship between two nodesMoreover, you could specify the argument
returnedAttributes
for retrieving only a few attributes of
the targeted object; species
for specific species; and
limit
for the number of returned objects. Note that this
function only returns “row” data.
The “id” input can be either non-Reactome or Reactome identifiers. If
you use a non-Reactome id, remember that you must also specify
databaseName
since the default one is “Reactome”. For
example, to get the Reactome instance associated with a circadian
rhythmic gene PER2:
# Retrieve the object of PER2 gene
# NOTE: if you're unsure which database to specify, you have to leave it as NULL
matchObject(id = "PER2", databaseName = NULL)
## $databaseObject
## schemaClass identifier databaseName displayName
## 1 ReferenceDNASequence PER2 COSMIC (genes) COSMIC (genes):PER2 PER2
## dbId geneName url
## 1 11509503 PER2 http://cancer.sanger.ac.uk/cosmic/gene/overview?ln=PER2
Now we know that the database name should be “COSMIC (genes)”! We can also try with a Reactome id “R-HSA-400219”:
matchObject(id = 'R-HSA-400219')
## $databaseObject
## schemaClass speciesName oldStId isInDisease releaseDate
## 1 Reaction Homo sapiens REACT_25088 FALSE 2010-09-21
## displayName stIdVersion dbId
## 1 Beta-TrCP1 binds phosphorylated PER proteins R-HSA-400219.1 400219
## name stId category isInferred
## 1 Beta-TrCP1 binds phosphorylated PER proteins R-HSA-400219 binding TRUE
For multiple ids, say you want to get more information for your
significantly enriched pathways, you can use function
multiObjects
. The speedUp
option can determine
to use the doParallel
method or not, details see ?multiObjects
.
# retrieve multiple objects
<- c('R-HSA-74158', 'R-HSA-1566977', 'R-HSA-3000157', 'R-HSA-3000178', 'R-HSA-216083')
ids multiObjects(ids)
Instances can also be fetched by their “displayNames”. Do note that spaces and symbols within the name are required. Here we focus on the complex SUMO1:TOP1 in nucleoplasm “SUMO1:TOP1 [nucleoplasm]” in C. elegans:
matchObject(displayName = "SUMO1:TOP1 [nucleoplasm]", species = "C. elegans")
## $databaseObject
## schemaClass speciesName isInDisease displayName
## 1 Complex Caenorhabditis elegans FALSE SUMO1:TOP1 [nucleoplasm]
## stIdVersion dbId name stId
## 1 R-CEL-4641301.1 10549504 SUMO1:TOP1 R-CEL-4641301
When retrieving instances belonging to one schema class, it’s better
specify the argument limit
as well for restricting the
number of returned instances. For all available schema classes see
Reactome Data Schema.
For instance, to get 5 “EntitySets” in human and then return their
display names and stId only:
# Get 5 instance in Class EntitySet and return displayName & stId
<- matchObject(schemaClass = "EntitySet", species = "human",
entity.set returnedAttributes = c("displayName", "stId"), limit = 5)
"databaseObject"]] # show as dataframe entity.set[[
By specifying the property
, nodes with the given
property (or properties), which are actually attributes/slots of
Reactome instances, could be returned. Let’s try to get instances that
are chimeric and are in disease.
# Get instances with conditions of properties that are stored in a list
matchObject(property = list(isChimeric = TRUE, isInDisease = TRUE), limit = 10)[["databaseObject"]]
The actual Cypher query for this command is
MATCH (n1)-[r:relationship]->(n2) RETURN n1,n2
,
therefore the n1
and n2
dataframes in the
returned list have the same number of rows, and every two rows with the
same index are connected with the given relationship.
# Get nodes connected with 'hasComponent'
matchObject(relationship = "hasComponent", limit = 3)
## $n1
## schemaClass speciesName isInDisease displayName
## 1 Complex Homo sapiens FALSE p-GFAP:EEF1A1 [lysosomal membrane]
## 2 Complex Homo sapiens FALSE p-GFAP:EEF1A1 [lysosomal membrane]
## 3 Complex Rattus norvegicus FALSE p-Gfap:Eef1a1 [lysosomal membrane]
## stIdVersion dbId name isChimeric stId
## 1 R-HSA-9626070.1 9626070 p-GFAP:EEF1A1 FALSE R-HSA-9626070
## 2 R-HSA-9626070.1 9626070 p-GFAP:EEF1A1 FALSE R-HSA-9626070
## 3 R-RNO-9626031.1 9626031 p-Gfap:Eef1a1 FALSE R-RNO-9626031
##
## $n2
## schemaClass speciesName isInDisease
## 1 EntityWithAccessionedSequence Homo sapiens FALSE
## 2 EntityWithAccessionedSequence Homo sapiens FALSE
## 3 EntityWithAccessionedSequence Rattus norvegicus FALSE
## displayName stIdVersion dbId name stId
## 1 p-GFAP [lysosomal membrane] R-HSA-9626054.1 9626054 p-GFAP R-HSA-9626054
## 2 EEF1A1 [lysosomal membrane] R-HSA-9626022.1 9626022 EEF1A1 R-HSA-9626022
## 3 p-Gfap [lysosomal membrane] R-RNO-9626029.1 9626029 p-Gfap R-RNO-9626029
## startCoordinate referenceType endCoordinate
## 1 1 ReferenceGeneProduct 432
## 2 1 ReferenceGeneProduct 462
## 3 1 ReferenceGeneProduct 430
These following functions in the MATCH family provide several commonly used cases that you might be interested in for Reactome data querying.
Reactome data are organized in a hierarchical way:
Pathway --> Reaction --> PhysicalEntity
, or sometimes
it might be
Pathway --> Reaction --> PhysicalEntity --> ReferenceEntity
where the PhysicalEntity has links to external database information via
the ReferenceEntity. You could retrieve the hierarchical data of a given
Event (Pathway or Reaction) or Entity
(PhysicalEntity or ReferenceEntity) using matchHierarchy
.
In this example, we’ll take a look at a RNA sequence (PhysicalEntity)
“POU5F1 mRNA [cytosol]” with stable identifier “R-HSA-500358”:
# Get hierarchy data of R-HSA-500358
<- matchHierarchy(id = "R-HSA-500358", type = "row")
pou5f1.hierarchy str(pou5f1.hierarchy, max.level = 1)
## List of 4
## $ physicalEntity:'data.frame': 1 obs. of 12 variables:
## $ event :'data.frame': 3 obs. of 13 variables:
## $ upperevent :'data.frame': 2 obs. of 16 variables:
## $ relationships :'data.frame': 7 obs. of 9 variables:
The RNA sequence we specified is in the physicalEntity
dataframe of the result list. It’s directly connected with those Events
in the event
dataframe, which are then connected with
Events in the upperevent
. Relationships between all these
objects are in relationship
dataframe:
# select essential columns to show
$relationships[,c(2,4,5,7,8)] pou5f1.hierarchy
This method can find all ReactionLikeEvents (RLEs) connected with a given Pathway by the relationship “hasEvent”. Additionally, the input can be a RLE, the result would be Pathway(s) linked via “hasEvent” together with other RLEs linked with the Pathways(s). Here we focus on a RLE “OAS1 oligomerizes” with identifier “R-HSA-8983688”.
# Find Reactions connected with R-HSA-8983688
<- matchReactionsInPathway(event.id = "R-HSA-8983688", type = "row") rle
str(rle, max.level = 1)
## List of 4
## $ reactionLikeEvent :'data.frame': 1 obs. of 12 variables:
## $ pathway :'data.frame': 1 obs. of 14 variables:
## $ otherReactionLikeEvent:'data.frame': 14 obs. of 12 variables:
## $ relationships :'data.frame': 15 obs. of 9 variables:
# The one in reactionLikeEvent is what we search for
$reactionLikeEvent rle
# Take a look at the connected Pathway
$pathway rle
otherReactionLikeEvent
are RLEs other than “OAS1
oligomerizes” connected with Pathway “OAS antiviral response”.
# Show displayNames of other RLEs
$otherReactionLikeEvent[["displayName"]] rle
## [1] "OAS1 binds viral dsRNA"
## [2] "RNASEL cleaves viral ssRNA"
## [3] "OAS2 binds viral dsRNA"
## [4] "RNASEL cleaves cellular ssRNA"
## [5] "OAS2 produces oligoadenylates"
## [6] "ABCE1 binds RNASEL"
## [7] "PDE12 cleaves 2'-5' oligoadenylates "
## [8] "Viral 2',5'-PDE cleaves 2'-5' oligoadenylates "
## [9] "OAS3 binds viral dsRNA"
## [10] "RNASEL binds 2'-5' oligoadenylate"
## [11] "OAS3 produces oligoadenylates"
## [12] "OAS1 produces oligoadenylates"
## [13] "OASL binds DDX58"
## [14] "OAS2 dimerizes"
The contect of these Events can actually be visualized in R using the
exportImage
function from the
ReactomeContentService4R
package! And it looks the same as
that in Pathway
Browser. To get the pathway diagram of Pathway “OAS antiviral
response” (stId: R-HSA-8983711) that we just retrieved, and highlight
the RLE (stId: R-HSA-8983688) that we specified:
library(ReactomeContentService4R)
## Connecting...welcome to Reactome v82!
# Export pathway diagram of "OAS antiviral response"
exportImage(id = "R-HSA-8983711", output = "diagram",
sel = "R-HSA-8983688", format = "png", quality = 8)
With the diagram shown above, we can see that the Reaction
highlighted in blue is in the middle of a Reaction cascade, with other
RLEs immediately preceding and following it. In order to know what these
preceding and following Reactions are, we can use function
matchPrecedingAndFollowingEvents
to find RLEs linked via
“precedingEvent”. The argument depth
is used to describe
the “variable length relationships”, the default value is 1
(i.e. immediately connected); or you can set
all.depth = TRUE
for retrieving the whole context. Details
see ?matchPrecedingAndFollowingEvents
.
# Retrieve RLE context with depth = 2
<- matchPrecedingAndFollowingEvents(event.id = "R-HSA-8983688", depth = 2, type = "row")
rle.context str(rle.context, max.level = 1)
## List of 4
## $ precedingEvent:'data.frame': 1 obs. of 12 variables:
## $ event :'data.frame': 1 obs. of 12 variables:
## $ followingEvent:'data.frame': 2 obs. of 12 variables:
## $ relationships :'data.frame': 3 obs. of 9 variables:
Usually we query data in a way like parent to child
(parent) --> (child)
, where we provide information about
the parent. But with the Graph Database, we are able to search in a
reverse direction that is child to parent
(parent) <-- (child)
with child’s information only. This
“child-to-parent” relationship is called Referral. You
could carry out the referral fetching by matchReferrals
that supports Classes “Event”, “PhysicalEntity”, “Regulation”,
“CatalystActivity”, “ReferenceEntity”, “Interaction”,
“AbstractModifiedResidue”. Depth related arguments could also be
specified here. More details sees ?matchReferrals
.
We would look at a Regulation “Negative gene expression regulation by ’EGR2 [nucleoplasm]” with dbId “6810147”:
# Find referrals of the given Regulation
matchReferrals(id = 6810147, type = "row")
## $Regulation
## schemaClass
## 1 NegativeGeneExpressionRegulation
## displayName stIdVersion
## 1 Negative gene expression regulation by 'EGR2 [nucleoplasm]' R-HSA-6810147.1
## dbId stId
## 1 6810147 R-HSA-6810147
##
## $databaseObject
## schemaClass displayName stIdVersion dbId stId
## 1 BlackBoxEvent HOXB1 gene is transcribed R-HSA-5617454.3 5617454 R-HSA-5617454
## speciesName isInDisease releaseDate name isChimeric
## 1 Homo sapiens FALSE 2015-12-15 HOXB1 gene is transcribed FALSE
## category isInferred
## 1 omitted TRUE
##
## $relationships
## neo4jId type startNode.neo4jId startNode.dbId startNode.schemaClass
## 1 5505941 regulatedBy 1330001 5617454 BlackBoxEvent
## endNode.neo4jId endNode.dbId endNode.schemaClass properties
## 1 1330002 6810147 NegativeGeneExpressionRegulation 1, 4
The dbId of endNode (endNode.dbId
in
$relationships
) is exactly the dbId we just specified.
Interactions of a PhysicalEntity (PE) could be retrieved by
matchInteractors
. This method begins with finding the
ReferenceEntity matched with the PE, then get the Interactions having
“interactor” relationship with the ReferenceEntity. For example, to get
interactions of “FANCM [nucleoplasm]” with stable id “R-HSA-419535”:
# Retrieve interaction data of the given PE
<- matchInteractors(pe.id = "R-HSA-419535") interactors
str(interactors, max.level = 1)
## List of 4
## $ physicalEntity :'data.frame': 1 obs. of 12 variables:
## $ referenceEntity:'data.frame': 1 obs. of 17 variables:
## $ interaction :'data.frame': 7 obs. of 8 variables:
## $ relationships :'data.frame': 8 obs. of 9 variables:
$interaction interactors
The roles of PhysicalEntities include “input”, “output”, “regulator”,
“catalyst”, which are represented as relationships “input” ,“output”,
“regulatedBy”, “catalystActivity” respectively. Therefore, we could
retrieve instances that are possibly connected with the given
PhysicalEntity via these relationships, and see the exact role(s) from
the existing relationships. We’ll take a look at a Polymer “HSBP1
oligomer [cytosol]” and input it into matchPEroles
. Either
id
or displayName
could be specified.
# Find possible roles of the given PE
<- matchPEroles(pe.displayName = "HSBP1 oligomer [cytosol]") roles
str(roles, max.level = 1)
## List of 3
## $ physicalEntity:'data.frame': 6 obs. of 10 variables:
## $ databaseObject:'data.frame': 6 obs. of 12 variables:
## $ relationships :'data.frame': 6 obs. of 9 variables:
# get the roles (relationships type)
unique(roles$relationships$type)
## [1] "output"
Diseases related to a PhysicalEntity or an Event could be found using
function matchDisease
. In reverse, you can also get
PhysicalEntities/Events associated with a Disease.
# Fetch Reactome instances associated with 'neuropathy' in human
matchDiseases(displayName = "neuropathy", species = "human", type = "row")
## $disease
## schemaClass identifier synonym databaseName displayName dbId
## 1 Disease 870 peripheral neuropathy DOID neuropathy 9635395
## name definition
## 1 neuropathy A nervous system disease that is located_in nerves or nerve cells.
## url
## 1 https://www.ebi.ac.uk/ols/ontologies/doid/terms?obo_id=DOID:870
##
## $databaseObject
## schemaClass displayName dbId name
## 1 ChemicalDrug pralidoxime [extracellular region] 9635003 pralidoxime
## isInDisease stIdVersion stId
## 1 TRUE R-ALL-9635003.1 R-ALL-9635003
##
## $relationships
## neo4jId type startNode.neo4jId startNode.dbId startNode.schemaClass
## 1 1031748 disease 263887 9635003 ChemicalDrug
## endNode.neo4jId endNode.dbId endNode.schemaClass properties
## 1 263890 9635395 Disease 1, 0
Given the PubMed id or the title for a paper, Reactome instances
related to this paper could be found by matchPaperObjects
.
The DatabaseObjects are connected with the LiteratureReference
(i.e. paper) via “literatureReference” relationship. Let’s try with a
paper “Aggresomes: a cellular response to misfolded proteins”.
# fetch objects by paper title
matchPaperObjects(displayName = "Aggresomes: a cellular response to misfolded proteins", type = "row")
## $literatureReference
## volume schemaClass journal pages year
## 1 143 LiteratureReference J. Cell Biol. 1883-98 1998
## displayName dbId
## 1 Aggresomes: a cellular response to misfolded proteins 9646681
## pubMedIdentifier title
## 1 9864362 Aggresomes: a cellular response to misfolded proteins
##
## $databaseObject
## schemaClass displayName dbId
## 1 Reaction PolyUb-Misfolded proteins bind vimentin to form aggresome 9646679
## speciesName isInDisease releaseDate stIdVersion
## 1 Homo sapiens FALSE 2019-12-10 R-HSA-9646679.2
## name isChimeric
## 1 PolyUb-Misfolded proteins bind vimentin to form aggresome FALSE
## stId category isInferred
## 1 R-HSA-9646679 binding FALSE
##
## $relationships
## neo4jId type startNode.neo4jId startNode.dbId
## 1 46368 literatureReference 12675 9646679
## startNode.schemaClass endNode.neo4jId endNode.dbId endNode.schemaClass
## 1 Reaction 12676 9646681 LiteratureReference
## properties
## 1 1, 0
The ability to view network graphs is definitely a big advantage of a graph database. Fortunately, R has developed into a powerful tool for network analysis. There are a number of R packages targeted network analysis and visualization, therefore we are able to get a graph just like the one in the Neo4j server, and even to set more visualization options!
Don’t forget that results can also be returned in the “graph” format, which are used to create the network visualization in R! This comprehensive tutorial - Network visualization with R (Ognyanova, K., 2019) - walks through each step on the creation of network graphs in R.
Here we will show a couple of examples to generate an interactive network graph after retrieving the specific Reactome graph data. Let’s say we want to visualize the hierarchical data of a ReferenceEntity “UniProt:P33992 MCM5”.
First install and load the following packages.
# install packages
<- c("stringr", "visNetwork", "networkD3", "wesanderson")
list.pkg <- list.pkg[!(list.pkg %in% installed.packages()[ ,"Package"])]
new.pkg if (length(new.pkg)) {
install.packages(new.pkg, repos = "https://cloud.r-project.org/")
}# load
invisible(suppressPackageStartupMessages(lapply(list.pkg, library, character.only = TRUE)))
We will try the visNetwork
package which visualizes networks using vis.js
javascript library.
# Get graph output data
<- matchHierarchy(displayName = "UniProt:P33992 MCM5", databaseName = "UniProt", type = "graph") graph
<- graph[["relationships"]]
relationships <- graph[["nodes"]]
nodes <- unnestListCol(df = nodes, column = "properties") # unnest the 'properties' column of lists
nodes head(nodes); head(relationships)
# Transform into visNetwork format for nodes & edges
<- data.frame(id = nodes$id,
vis.nodes label = str_trunc(nodes$displayName, 20), # truncate the long names
group = nodes$schemaClass,
title = paste0("<p><b>", nodes$schemaClass, "</b><br>",
"dbId: ", nodes$dbId, "<br>", nodes$displayName, "</p>"))
<- data.frame(from = relationships$startNode,
vis.edges to = relationships$endNode,
label = relationships$type,
font.size = 16,
font.color = 'steelblue')
head(vis.nodes); head(vis.edges)
We are going to change the visual parameters of nodes and edges by
adding them as columns in the dataframes. More customizations see the
visNetwork
documentation or
?vignette("Introduction-to-visNetwork")
.
# nodes parameters
## get palette colors with package 'wesanderson'
<- as.character(wes_palette(n = length(unique(vis.nodes$group)), name = "Darjeeling2"))
node.colors names(node.colors) <- levels(factor(vis.nodes$group))
## NOTE: don't use `str_replace_all` here since 'TopLevelPathway' & 'Pathway' share the string 'Pathway'
$color.background <- node.colors[as.numeric(factor(vis.nodes$group))] # node color
vis.nodes$color.border <- "lightgray"
vis.nodes## highlight the instance we specified
$color.border[vis.nodes$label == "UniProt:P33992 MCM5"] <- "pink"
vis.nodes$color.highlight.border <- "darkred"
vis.nodes$borderWidth <- 2 # Node border width
vis.nodes
# edges parameters
$width <- 1.2 # line width
vis.edges<- as.character(wes_palette(n = length(unique(vis.edges$label)), name = "FantasticFox1"))
edges.colors names(edges.colors) <- unique(vis.edges$label)
$color <- str_replace_all(vis.edges$label, edges.colors) # line color
vis.edges$arrows <- "to" # arrows: 'from', 'to', or 'middle'
vis.edges$smooth <- TRUE # should the edges be curved?
vis.edges
# height & width of the plot can be set here
<- visNetwork(vis.nodes, vis.edges, main = "The hierarchy of protein MCM5",
visnet height = "500px", width = "100%")
visnet
Add a drop-down menu:
# Rename column name 'group' to 'Class' for displaying in the window
names(visnet[["x"]][["nodes"]]) <- gsub("group", "Class", names(visnet[["x"]][["nodes"]]))
visOptions(visnet, highlightNearest = TRUE, selectedBy = "Class")
We can also take a look at another package networkD3
,
which generates network graphs using D3
javascript library.
# the node ids MUST be numeric, and start from 0
<- as.character(as.numeric(factor(nodes$id)) - 1)
nodes.idx names(nodes.idx) <- nodes$id
# transform into networkD3 format
<- data.frame(source = as.numeric(str_replace_all(relationships$startNode, nodes.idx)),
d3.edges target = as.numeric(str_replace_all(relationships$endNode, nodes.idx)),
label = relationships$type)
<- d3.edges[order(d3.edges$source), ]
d3.edges
<- cbind(idx=as.numeric(nodes.idx), nodes)
d3.nodes <- d3.nodes[order(d3.nodes$idx), ] # the order MUST be consistent with the 'source'
d3.nodes
forceNetwork(Links = d3.edges, Nodes = d3.nodes, Source="source", Target="target",
NodeID = "displayName", Group = "schemaClass", Value = "label",
linkColour = "#afafaf", fontSize = 12, zoom = TRUE, legend = TRUE,
Nodesize = 15, opacity = 0.9, charge = -50)
To modify the forceNetwork graph, one can execute custom javascript
code with the htmlwidgets
R
package, but it won’t be discussed here.
sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] wesanderson_0.3.6 networkD3_0.4
## [3] visNetwork_2.1.0 stringr_1.4.0
## [5] ReactomeContentService4R_1.6.0 ReactomeGraph4R_1.6.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.2 xfun_0.31 bslib_0.3.1 purrr_0.3.4
## [5] vctrs_0.4.1 generics_0.1.3 htmltools_0.5.2 yaml_2.3.5
## [9] utf8_1.2.2 rlang_1.0.4 jquerylib_0.1.4 later_1.3.0
## [13] pillar_1.7.0 glue_1.6.2 DBI_1.1.3 foreach_1.5.2
## [17] lifecycle_1.0.1 htmlwidgets_1.5.4 codetools_0.2-18 evaluate_0.15
## [21] knitr_1.39 fastmap_1.1.0 doParallel_1.0.17 httpuv_1.6.5
## [25] curl_4.3.2 parallel_4.2.1 fansi_1.0.3 highr_0.9
## [29] Rcpp_1.0.9 xtable_1.8-4 promises_1.2.0.1 magick_2.7.3
## [33] jsonlite_1.8.0 mime_0.12 png_0.1-7 digest_0.6.29
## [37] stringi_1.7.8 dplyr_1.0.9 shiny_1.7.1 getPass_0.2-2
## [41] cli_3.3.0 tools_4.2.1 magrittr_2.0.3 sass_0.4.1
## [45] tibble_3.1.7 crayon_1.5.1 tidyr_1.2.0 pkgconfig_2.0.3
## [49] ellipsis_0.3.2 data.table_1.14.2 attempt_0.3.1 neo4r_0.1.1
## [53] assertthat_0.2.1 rmarkdown_2.14 httr_1.4.3 rstudioapi_0.13
## [57] iterators_1.0.14 R6_2.5.1 igraph_1.3.5 compiler_4.2.1