XGR (version 1.0.7)

xSubneterGenes: Function to identify a subnetwork from an input network and the signficance level imposed on its nodes

Description

xSubneterGenes is supposed to identify maximum-scoring subnetwork from an input graph with the node information on the significance (measured as p-values or fdr). It returns an object of class "igraph".

Usage

xSubneterGenes(data, network = c("STRING_highest", "STRING_high",
"STRING_medium", "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"), network.customised = NULL, seed.genes = T,
subnet.significance = 0.01, subnet.size = NULL, verbose = T,
RData.location = "http://galahad.well.ox.ac.uk/bigdata")

Arguments

data

a named input vector containing the significance level for nodes (gene symbols). For this named vector, the element names are gene symbols, the element values for the significance level (measured as p-value or fdr). Alternatively, it can be a matrix or data frame with two columns: 1st column for gene symbols, 2nd column for the significance level

network

the built-in network. Currently two sources of network information are supported: the STRING database (version 10) and the Pathways 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), and "STRING_medium" for interactions with medium confidence (confidence scores>=400). 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 addtion 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

network.customised

an object of class "igraph". By default, it is NULL. It is designed to allow the user analysing their customised network data that are not listed in the above argument 'network'. This customisation (if provided) has the high priority over built-in network

seed.genes

logical to indicate whether the identified network is restricted to seed genes (ie input genes with the signficant level). By default, it sets to true

subnet.significance

the given significance threshold. By default, it is set to NULL, meaning there is no constraint on nodes/genes. If given, those nodes/genes with p-values below this are considered significant and thus scored positively. Instead, those p-values above this given significance threshold are considered insigificant and thus scored negatively

subnet.size

the desired number of nodes constrained to the resulting subnet. It is not nulll, a wide range of significance thresholds will be scanned to find the optimal significance threshold leading to the desired number of nodes in the resulting subnet. Notably, the given significance threshold will be overwritten by this option

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

a subgraph with a maximum score, an object of class "igraph"

See Also

xRDataLoader

Examples

## Not run: 
# # Load the XGR package and specify the location of built-in data
# library(XGR)
# RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev/"
# 
# # a) provide the input nodes/genes with the significance info
# ## load human genes
# org.Hs.eg <- xRDataLoader(RData='org.Hs.eg',
# RData.location=RData.location)
# sig <- rbeta(500, shape1=0.5, shape2=1)
# data <- data.frame(symbols=org.Hs.eg$gene_info$Symbol[1:500], sig)
# 
# # b) perform network analysis
# # b1) find maximum-scoring subnet based on the given significance threshold
# subnet <- xSubneterGenes(data=data, network="STRING_high",
# subnet.significance=0.01, RData.location=RData.location)
# # b2) find maximum-scoring subnet with the desired node number=50
# subnet <- xSubneterGenes(data=data, network="STRING_high",
# subnet.size=50, RData.location=RData.location)
# 
# # c) save subnet results to the files called 'subnet_edges.txt' and 'subnet_nodes.txt'
# output <- igraph::get.data.frame(subnet, what="edges")
# utils::write.table(output, file="subnet_edges.txt", sep="\t",
# row.names=FALSE)
# output <- igraph::get.data.frame(subnet, what="vertices")
# utils::write.table(output, file="subnet_nodes.txt", sep="\t",
# row.names=FALSE)
# 
# # d) visualise the identified subnet
# ## do visualisation with nodes colored according to the significance (you provide)
# xVisNet(g=subnet, pattern=-log10(as.numeric(V(subnet)$significance)),
# vertex.shape="sphere", colormap="wyr")
# ## do visualisation with nodes colored according to transformed scores
# xVisNet(g=subnet, pattern=as.numeric(V(subnet)$score),
# vertex.shape="sphere")
# 
# # e) visualise the identified subnet as a circos plot
# library(RCircos)
# xCircos(g=subnet, entity="Gene", colormap="white-gray",
# RData.location=RData.location)
# ## End(Not run)