The CX format is supposed to be flexible, so that custom aspects can be defined by the user. However, the functions provided by the RCX package cannot cover those extensions. To work with those aspects in R, it is necessary to implement functions extending the RCX to support custom aspects. In the following, we will explore how custom aspects can be implemented and integrated in the RCX model.
For demonstration purposes we here define our own custom aspect for keeping the network provenance.
While similar, this is not the deprecated provenanceHistory aspect from previous CX versions!
In this example, the JSON structure should look like this:
{
"networkProvenance": [
{
"@id": 1,
"time": 1445437740,
"action": "created",
"nodes": [1, 2, 3, 4, 5, 6],
"source": "https://www.ndexbio.org/viewer/networks/66a902f5-2022-11e9-bb6a-0ac135e8bacf"
},
{
"@id": 2,
"time": 1445437770,
"action": "merged",
"nodes": [7, 8, 9, 10],
"source": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33075"
},
{
"@id": 3,
"time": 1445437799,
"action": "filtered",
"nodes": [],
"comment": "Some manual filtering was performed"
}
]
}The network provenance structure consists of following properties:
@id: (required) and unique ID used in this aspecttime: (required) timestamp in seconds since JAN 01 1970. (UTC)action: (required) what was donenodes: (required) list of node IDs, can be emptysource: (optional) where new data came from in this stepcomment: (optional) some commentary on the performed actionIt is quite a simple aspect, but it will show the single steps needed to adapt this aspect to the RCX model.
First, load the RCX library:
library(RCX)Following the naming convention of the package, we define a simple function to create the aspect in R:
createNetworkProvenance <- function(
id = NULL,
time,
action,
nodes,
source = NULL,
comment = NULL){
## generate id if not provided
if(is.null(id)){
id = 0:(length(name) -1)
}
## create aspect with default values
res = data.frame(
id = id,
time = time,
action = action,
nodes = NA,
source = NA,
comment = NA,
stringsAsFactors=FALSE, check.names=FALSE
)
## add nodes
if(!is.list(nodes)) nodes <- list(nodes)
res$nodes = nodes
## add source if provided
if(!is.null(source)){
res$source <- source
}
## add comment if provided
if(!is.null(comment)){
res$comment <- comment
}
## add a class name
class(res) <- append("NetworkProvenanceAspect", class(res))
return(res)
}Since this is only for demonstration purposes no checks or validations of the data is included. In practice, all data should be checked to avoid mistakes. Also for all following functions validation of the data has been omitted.
Now that we can create objects of our own aspect, let’s do it:
networkProvenance <- createNetworkProvenance(
id = c(1,2,3),
time = c(1445437740, 1445437770, 1445437799),
action = c("created", "merged", "filtered"),
nodes = list(
c(1, 2, 3, 4, 5, 6),
c(7, 8, 9, 10),
c()
),
source = c(
"https://www.ndexbio.org/viewer/networks/66a902f5-2022-11e9-bb6a-0ac135e8bacf",
"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33075",
NA
),
comment = c(NA, NA, "Some manual filtering was performed")
)
networkProvenance## id time action nodes
## 1 1 1445437740 created 1, 2, 3, 4, 5, 6
## 2 2 1445437770 merged 7, 8, 9, 10
## 3 3 1445437799 filtered NULL
## source
## 1 https://www.ndexbio.org/viewer/networks/66a902f5-2022-11e9-bb6a-0ac135e8bacf
## 2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33075
## 3 <NA>
## comment
## 1 <NA>
## 2 <NA>
## 3 Some manual filtering was performedFor updating the aspect, as well as the RCX object with this aspect, the RCX package uses method dispatch and follows a convention for naming the functions update<accession name>.
For example for rcx$nodes the update function must be named updateNodes!
Firstly, we create for networkProvenance a generic function following this convention:
updateNetworkProvenance <- function(x, aspect) {
UseMethod("updateNetworkProvenance", x)
}The first argument must always be either an RCX object or a network provenance aspect, the second a network provenance aspect.
Now let’s add a method, that merges two network provenance aspects:
updateNetworkProvenance.NetworkProvenanceAspect <- function(x, aspect) {
res <- plyr::rbind.fill(x, aspect)
if (!"NetworkProvenanceAspect" %in% class(res)) {
class(res) <- append("NetworkProvenanceAspect", class(res))
}
return(res)
}To test this method, we split our previous example into two parts and merge them.
If everything works, we should get the same aspect object as we got from createNetworkProvenance.
## Split the original example
## Create first part
np1 <- createNetworkProvenance(
id = c(1,2),
time = c(1445437740, 1445437770),
action = c("created", "merged"),
nodes = list(
c(1, 2, 3, 4, 5, 6),
c(7, 8, 9, 10)
),
source = c(
"https://www.ndexbio.org/viewer/networks/66a902f5-2022-11e9-bb6a-0ac135e8bacf",
"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33075"
)
)
## Create second part
np2 <- createNetworkProvenance(
id = 3,
time = 1445437799,
action = "filtered",
nodes = c(),
comment = "Some manual filtering was performed"
)
## Merge the parts
networkProvenance <- updateNetworkProvenance(np1, np2)
networkProvenance## id time action nodes
## 1 1 1445437740 created 1, 2, 3, 4, 5, 6
## 2 2 1445437770 merged 7, 8, 9, 10
## 3 3 1445437799 filtered NULL
## source
## 1 https://www.ndexbio.org/viewer/networks/66a902f5-2022-11e9-bb6a-0ac135e8bacf
## 2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33075
## 3 <NA>
## comment
## 1 <NA>
## 2 <NA>
## 3 Some manual filtering was performedNow that we have the method for two network provenance aspects, we can create another method to merge an RCX object with a network provenance aspects. For this, we simply can use the previous method in this one to merge the two network provenance aspects:
updateNetworkProvenance.RCX <- function(x, aspect) {
rcxAspect <- x$networkProvenance
if (!is.null(rcxAspect)) {
aspect <- updateNetworkProvenance(rcxAspect, aspect)
}
x$networkProvenance <- aspect
x <- updateMetaData(x)
return(x)
}So if we now update the RCX object with the network provenance parts one by one, we should end up with a combine one in the RCX.
## Prepare an RCX object
rcx <- createRCX(
nodes = createNodes(name = LETTERS[1:10]),
edges = createEdges(
source = c(1, 2),
target = c(2, 3)
)
)
## Add the first part of network provenance
rcx <- updateNetworkProvenance(rcx, np1)
## Add the second part
rcx <- updateNetworkProvenance(rcx, np2)
rcx## [[metaData]] = Meta-data:
## name version idCounter elementCount consistencyGroup
## 1 nodes 1.0 9 10 1
## 2 edges 1.0 1 2 1
##
## [[nodes]] = Nodes:
## id name
## 1 0 A
## 2 1 B
## 3 2 C
## 4 3 D
## 5 4 E
## 6 5 F
## 7 6 G
## 8 7 H
## 9 8 I
## 10 9 J
##
## [[edges]] = Edges:
## id source target
## 1 0 1 2
## 2 1 2 3
##
## [[networkProvenance]] = id time action nodes
## 1 1 1445437740 created 1, 2, 3, 4, 5, 6
## 2 2 1445437770 merged 7, 8, 9, 10
## 3 3 1445437799 filtered NULL
## source
## 1 https://www.ndexbio.org/viewer/networks/66a902f5-2022-11e9-bb6a-0ac135e8bacf
## 2 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33075
## 3 <NA>
## comment
## 1 <NA>
## 2 <NA>
## 3 Some manual filtering was performedAs we see, this works as well, but there is one problem: The meta-data does not contain the aspect yet.
To include it, we have to provide information about the relation between accession name (networkProvenance) and the class of the aspect (NetworkProvenanceAspect).
This information is kept in the aspectClasses vector.
To update this vector with a new aspect, we can use the updateAspectClasses function:
aspectClasses## rcx metaData
## "RCX" "MetaDataAspect"
## nodes edges
## "NodesAspect" "EdgesAspect"
## nodeAttributes edgeAttributes
## "NodeAttributesAspect" "EdgeAttributesAspect"
## networkAttributes cartesianLayout
## "NetworkAttributesAspect" "CartesianLayoutAspect"
## cyGroups cyVisualProperties
## "CyGroupsAspect" "CyVisualPropertiesAspect"
## cyHiddenAttributes cyNetworkRelations
## "CyHiddenAttributesAspect" "CyNetworkRelationsAspect"
## cySubNetworks cyTableColumn
## "CySubNetworksAspect" "CyTableColumnAspect"aspectClasses <- updateAspectClasses(
aspectClasses,
c(networkProvenance="NetworkProvenanceAspect")
)
aspectClasses## rcx metaData
## "RCX" "MetaDataAspect"
## nodes edges
## "NodesAspect" "EdgesAspect"
## nodeAttributes edgeAttributes
## "NodeAttributesAspect" "EdgeAttributesAspect"
## networkAttributes cartesianLayout
## "NetworkAttributesAspect" "CartesianLayoutAspect"
## cyGroups cyVisualProperties
## "CyGroupsAspect" "CyVisualPropertiesAspect"
## cyHiddenAttributes cyNetworkRelations
## "CyHiddenAttributesAspect" "CyNetworkRelationsAspect"
## cySubNetworks cyTableColumn
## "CySubNetworksAspect" "CyTableColumnAspect"
## networkProvenance
## "NetworkProvenanceAspect"The meta-data can be updated manually using the updateMetaData function.
However, to get the updated aspectClass to work, we have to provide it to the update function:
rcx <- updateMetaData(rcx)
rcx$metaData## Meta-data:
## name version idCounter elementCount consistencyGroup
## 1 nodes 1.0 9 10 1
## 2 edges 1.0 1 2 1rcx <- updateMetaData(rcx, aspectClasses = aspectClasses)
rcx$metaData## Meta-data:
## name version idCounter elementCount consistencyGroup
## 1 nodes 1.0 9 10 1
## 2 edges 1.0 1 2 1
## 3 networkProvenance 1.0 NA 3 1Now the meta data is updated with our custom aspect. To automatically update the meta-data when the provenance history is updated, we can add it to our update method for the RCX object:
updateNetworkProvenance.RCX <- function(x, aspect) {
rcxAspect <- x$networkProvenance
if (!is.null(rcxAspect)) {
aspect <- updateNetworkProvenance(rcxAspect, aspect)
}
x$networkProvenance <- aspect
x <- updateMetaData(x, aspectClasses = aspectClasses)
return(x)
}The idCounter of the meta-data is not updated yet, although this aspect contains an ID.
To enable this, we have to implement two methods:
hasIds.NetworkProvenanceAspect <- function(aspect) {
return(TRUE)
}
idProperty.NetworkProvenanceAspect <- function(aspect) {
return("id")
}The first one simply tells that the network provenance aspect has IDs, the second one returns the property, that holds the id.
When we now update the meta-data, the idCounter is updated as well.
rcx <- updateMetaData(rcx, aspectClasses = aspectClasses)
rcx$metaData## Meta-data:
## name version idCounter elementCount consistencyGroup
## 1 nodes 1.0 9 10 1
## 2 edges 1.0 1 2 1
## 3 networkProvenance 1.0 3 3 1Alternatively, we could specify the timestamp as id, and subsequently omit the id column in general.
idProperty.NetworkProvenanceAspect <- function(aspect) {
return("time")
}
rcx <- updateMetaData(rcx, aspectClasses = aspectClasses)
rcx$metaData## Meta-data:
## name version idCounter elementCount consistencyGroup
## 1 nodes 1.0 9 10 1
## 2 edges 1.0 1 2 1
## 3 networkProvenance 1.0 1445437799 3 1This might be useful in some cases, but for this example we stick to id as the dedicated column providing the IDs in our aspect.
idProperty.NetworkProvenanceAspect <- function(aspect) {
return("id")
}Additionally, we can provide a method to determine to which other aspects our custom aspect is referring, and by which other aspects are referred. In our case this would be the nodes aspect with its IDs. This later could be used in the validation.
refersTo.NetworkProvenanceAspect <- function(aspect) {
nodes <- aspectClasses["nodes"]
names(nodes) <- NULL
result <- c(nodes = nodes)
return(result)
}
refersTo(rcx$edges)## source target
## "NodesAspect" "NodesAspect"refersTo(rcx$networkProvenance)## nodes
## "NodesAspect"referredBy(rcx)## $NodesAspect
## [1] "EdgesAspect"referredBy(rcx, aspectClasses)## $NodesAspect
## [1] "EdgesAspect" "NetworkProvenanceAspect"To be consistent with the other aspects of the RCX package, we can provide a custom print method, that adds the aspect name before printing:
print.NetworkProvenanceAspect <- function(x, ...) {
cat("Network provenance:\n")
class(x) <- class(x)[!class(x) %in%
"NetworkProvenanceAspect"]
print(x, ...)
}There are also further function, like summary and countElements that could be adjusted, but for this example they are not needed and work analogously.
summary.NetworkProvenanceAspect <- function(object, ...) {
...
}
countElements.NetworkProvenanceAspect <- function(x) {
...
}It is always a good idea to provide functions to validate the correctness of the data.
Therefore, we implement the validate method for our aspect.
What to evaluate in this method is up to the user, but the more checks and information provided, the more it helps other users to track down errors.
validate.NetworkProvenanceAspect = function(x, verbose=TRUE){
if(verbose) cat("Checking Network Provenance Aspect:\n")
test = all(! is.na(x$id))
if(verbose) cat(paste0("- Column (id) doesn't contain any NA values...",
ifelse(test, "OK", "FAIL"),
"\n"))
pass = test
test = length(x$id) == length(unique(x$id))
if(verbose) cat(paste0("- Column (id) contains only unique values...",
ifelse(test, "OK", "FAIL"),
"\n"))
pass = pass & test
if(verbose) cat(paste0(">> Network Provenance Aspect: ",
ifelse(test, "OK", "FAIL"),
"\n"))
invisible(pass)
}
validate(rcx, verbose = TRUE)## Checking Nodes Aspect:
## - Is object of class "NodesAspect"...OK
## - All required columns present (id)...OK
## - Column (id) doesn't contain any NA values...OK
## - At least one node present...OK
## - Column (id) contains only unique values...OK
## - Column (id) only contains numeric values...OK
## - Column (id) only contains positive (>=0) values...OK
## - No merge artefacts present (i.e. column with old ids: oldId)...OK
## - Only allowed columns present (id, name, represents)...OK
## >> Nodes Aspect: OK
##
## Checking Edges Aspect:
## - Is object of class "EdgesAspect"...OK
## - All required columns present (id, source, target)...OK
## - Column (id) doesn't contain any NA values...OK
## - Column (id) contains only unique values...OK
## - Column (id) only contains numeric values...OK
## - Column (id) only contains positive (>=0) values...OK
## - Column (source) doesn't contain any NA values...OK
## - Column (source) only contains numeric values...OK
## - Column (source) only contains positive (>=0) values...OK
## - Column (target) doesn't contain any NA values...OK
## - Column (target) only contains numeric values...OK
## - Column (target) only contains positive (>=0) values...OK
## - No merge artefacts present (i.e. column with old ids: oldId)...OK
## - Only allowed columns present (id, source, target, name, interaction)...OK
## >> Edges Aspect: OK
##
## Checking Custom Aspects:
## Checking Network Provenance Aspect:
## - Column (id) doesn't contain any NA values...OK
## - Column (id) contains only unique values...OK
## >> Network Provenance Aspect: OK
## >> Cytoscape Table Column Aspect: OK
##
## Checking RCX:
## - Is object of class "RCX"...OK
## - nodes aspect is present...OK
## - Validate nodes aspect...OK
## - Validate edges aspect...OK
## - Reference aspect (nodes) present and correct...OK
## - All id references exist (EdgesAspect$source ids in NodesAspect$id)...OK
## - All id references exist (EdgesAspect$target ids in NodesAspect$id)...OK
## >> RCX: OKWith this method, not only our custom aspect is evaluated solely, the method is also called when the whole RCX object is evaluated. Therefore, if validation for the aspect fails, also the validation of the RCX object fails.
Since we have our aspect data model already created in R, let’s start with the conversion to CX in JSON format.
To allow the aspect to be processed, we have to provide a method to take over this part.
In the RCX package, the aspect class specific methods of rcxToJson convert the aspect to a JSON object.
rcxToJson.NetworkProvenanceAspect <- function(aspect, verbose = FALSE, ...) {
if (verbose)
cat("Convert network provenance to JSON...")
## rename id to @id
colnames(aspect) <- gsub("id", "\\@id", colnames(aspect))
## convert to json
json <- jsonlite::toJSON(aspect, pretty = TRUE)
## empty nodes are converted to 'nodes': {}, the simplest fix is just
## replacing it
json <- gsub("\"nodes\": \\{\\},", "\"nodes\": \\[\\],", json)
## add the aspect name
json <- paste0("{\"networkProvenance\":", json, "}")
if (verbose)
cat("done!\n")
return(json)
}
cat(rcxToJson(rcx$networkProvenance))## {"networkProvenance":[
## {
## "@id": 1,
## "time": 1445437740,
## "action": "created",
## "nodes": [1, 2, 3, 4, 5, 6],
## "source": "https://www.ndexbio.org/viewer/networks/66a902f5-2022-11e9-bb6a-0ac135e8bacf"
## },
## {
## "@id": 2,
## "time": 1445437770,
## "action": "merged",
## "nodes": [7, 8, 9, 10],
## "source": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33075"
## },
## {
## "@id": 3,
## "time": 1445437799,
## "action": "filtered",
## "nodes": [],
## "comment": "Some manual filtering was performed"
## }
## ]}The rcxToJson functions are used in the toCX and writeCX functions for converting and saving the RCX.
So let’s write the RCX to a file we can use later for reading.
tempCX <- tempfile(fileext = ".cx")
writeCX(rcx, tempCX)## [1] "/tmp/Rtmp5nJXWO/file91e374a47a231.cx"Similarly to rcxToJson there exists a method for doing the reverse.
The readCX function combines several steps, that can be performed individually:
readJSON which simply read the JSON from fileparseJSON which parsed the JSON text to JSON data (list of lists)processCX takes the JSON data and calls for each aspect the jsonToRCXThe correct aspect can be accessed in the jsonData list, which then has to be processed and a created object of the aspect returned.
jsonToRCX.networkProvenance <- function(jsonData, verbose) {
if (verbose)
cat("Parsing network provenance...")
data <- jsonData$networkProvenance
ids <- sapply(
data, function(d) {
d$`@id`
}
)
time <- sapply(
data, function(d) {
d$time
}
)
action <- sapply(
data, function(d) {
d$action
}
)
nodes <- sapply(
data, function(d) {
d$nodes
}
)
source <- sapply(
data, function(d) {
d$source
}
)
comment <- sapply(
data, function(d) {
d$comment
}
)
if (verbose)
cat("create aspect...")
result <- createNetworkProvenance(
id = ids, time = time, action = action, nodes = nodes, source = source,
comment = comment
)
if (verbose)
cat("done!\n")
return(result)
}
rcxParsed <- readCX(tempCX, aspectClasses = aspectClasses)However, the meta-data has to be updated again manually with the aspectClasses available:
rcxParsed <- updateMetaData(rcxParsed, aspectClasses = aspectClasses)
rcxParsed$metaData## Meta-data:
## name version idCounter elementCount consistencyGroup
## 1 nodes 1.0 9 10 1
## 2 edges 1.0 1 2 1
## 3 networkProvenance 1.0 3 3 1So we have successfully converted our custom aspect between CX and RCX!
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] base64enc_0.1-3 RCX_1.0.1 knitr_1.40 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 bookdown_0.29 digest_0.6.29
## [4] plyr_1.8.7 R6_2.5.1 jsonlite_1.8.2
## [7] formatR_1.12 magrittr_2.0.3 evaluate_0.16
## [10] stringi_1.7.8 cachem_1.0.6 rlang_1.0.6
## [13] cli_3.4.1 jquerylib_0.1.4 bslib_0.4.0
## [16] rmarkdown_2.16 tools_4.2.1 stringr_1.4.1
## [19] xfun_0.33 yaml_2.3.5 fastmap_1.1.0
## [22] compiler_4.2.1 BiocManager_1.30.18 htmltools_0.5.3
## [25] sass_0.4.2