xDefineNet: Function to define a gene network

In Pi: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level

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",
guid = NULL
)

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
guid	a valid (5-character) Global Unique IDentifier for an OSF project. 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

xRDataLoader

Examples

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)

Pi documentation built on Nov. 26, 2020, 2:01 a.m.