xDefineNet | R Documentation

## Function to define a gene network

### Description

`xDefineNet` is supposed to define a gene network sourced from the STRING database or the Pathway Commons database. It returns an object of class "igraph".

### Usage

    xDefineNet(network = c("STRING_highest", "STRING_high",
    "STRING_medium",
    "STRING_low", "PCommonsUN_high", "PCommonsUN_medium",
    "PCommonsDN_high",
    "PCommonsDN_medium", "PCommonsDN_Reactome", "PCommonsDN_KEGG",
    "PCommonsDN_HumanCyc", "PCommonsDN_PID", "PCommonsDN_PANTHER",
    "PCommonsDN_ReconX", "PCommonsDN_TRANSFAC", "PCommonsDN_PhosphoSite",
    "PCommonsDN_CTD", "KEGG", "KEGG_metabolism", "KEGG_genetic",
    "KEGG_environmental", "KEGG_cellular", "KEGG_organismal",
    "KEGG_disease",
    "REACTOME", "TRRUST"), STRING.only = c(NA, "neighborhood_score",
    "fusion_score", "cooccurence_score", "coexpression_score",
    "experimental_score", "database_score", "textmining_score")[1],
    weighted = FALSE, verbose = TRUE,
    RData.location = "http://galahad.well.ox.ac.uk/bigdata")

### Arguments

`network`

the built-in network. Currently two sources of network information are supported: the STRING database (version 10) and the Pathway Commons database (version 7). STRING is a meta-integration of undirect interactions from the functional aspect, while Pathways Commons mainly contains both undirect and direct interactions from the physical/pathway aspect. Both have scores to control the confidence of interactions. Therefore, the user can choose the different quality of the interactions. In STRING, "STRING_highest" indicates interactions with highest confidence (confidence scores>=900), "STRING_high" for interactions with high confidence (confidence scores>=700), "STRING_medium" for interactions with medium confidence (confidence scores>=400), and "STRING_low" for interactions with low confidence (confidence scores>=150). For undirect/physical interactions from Pathways Commons, "PCommonsUN_high" indicates undirect interactions with high confidence (supported with the PubMed references plus at least 2 different sources), "PCommonsUN_medium" for undirect interactions with medium confidence (supported with the PubMed references). For direct (pathway-merged) interactions from Pathways Commons, "PCommonsDN_high" indicates direct interactions with high confidence (supported with the PubMed references plus at least 2 different sources), and "PCommonsUN_medium" for direct interactions with medium confidence (supported with the PubMed references). In addition to pooled version of pathways from all data sources, the user can also choose the pathway-merged network from individual sources, that is, "PCommonsDN_Reactome" for those from Reactome, "PCommonsDN_KEGG" for those from KEGG, "PCommonsDN_HumanCyc" for those from HumanCyc, "PCommonsDN_PID" for those froom PID, "PCommonsDN_PANTHER" for those from PANTHER, "PCommonsDN_ReconX" for those from ReconX, "PCommonsDN_TRANSFAC" for those from TRANSFAC, "PCommonsDN_PhosphoSite" for those from PhosphoSite, and "PCommonsDN_CTD" for those from CTD. For direct (pathway-merged) interactions sourced from KEGG, it can be 'KEGG' for all, 'KEGG_metabolism' for pathways grouped into 'Metabolism', 'KEGG_genetic' for 'Genetic Information Processing' pathways, 'KEGG_environmental' for 'Environmental Information Processing' pathways, 'KEGG_cellular' for 'Cellular Processes' pathways, 'KEGG_organismal' for 'Organismal Systems' pathways, and 'KEGG_disease' for 'Human Diseases' pathways. 'REACTOME' for protein-protein interactions derived from Reactome pathways. 'TRRUST' for TRRUST curated TF-target relations

`STRING.only`

the further restriction of STRING by interaction type. If NA, no such restriction. Otherwide, it can be one or more of "neighborhood_score","fusion_score","cooccurence_score","coexpression_score","experimental_score","database_score","textmining_score". Useful options are c("experimental_score","database_score"): only experimental data (extracted from BIND, DIP, GRID, HPRD, IntAct, MINT, and PID) and curated data (extracted from Biocarta, BioCyc, GO, KEGG, and Reactome) are used

`weighted` | logical to indicate whether edge weights should be considered. By default, it sets to false. If true, it only works for the network from the STRING database
`verbose` | logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
`RData.location` | the characters to tell the location of built-in RData files. See `xRDataLoader` for details

### Value

an object of class "igraph"

### Note

The input graph will treat as an unweighted graph if there is no 'weight' edge attribute associated with

### See Also

`xCombineNet`

### Examples

    ## Not run:
    # Load the library
    library(XGR)

    ## End(Not run)

    RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
    ## Not run:
    # STRING (high quality)
    g <- xDefineNet(network="STRING_high", RData.location=RData.location)
    # STRING (high quality), with edges weighted
    g <- xDefineNet(network="STRING_high", weighted=T,
    RData.location=RData.location)
    # STRING (high quality), only edges sourced from experimental or curated data
    g <- xDefineNet(network="STRING_high",
    STRING.only=c("experimental_score","database_score"),
    RData.location=RData.location)

    # Pathway Commons
    g <- xDefineNet(network="PCommonsDN_medium",
    RData.location=RData.location)

    # KEGG (all)
    g <- xDefineNet(network="KEGG", RData.location=RData.location)
    # KEGG ('Organismal Systems')
    g <- xDefineNet(network="KEGG_organismal",
    RData.location=RData.location)

    ## End(Not run)