XGR (version 1.1.4)

xGR2xNet: Function to identify a gene network from an input network given a list of genomic regions

Description

xGR2xNet is supposed to identify maximum-scoring gene subnetwork from an input graph with the node information (genomic regions with or without the significance). To do so, it defines seed genes and their scores that take into account the distance to and the significance of input genomic regions (GR). It returns an object of class "igraph".

Usage

xGR2xNet(data, significance.threshold = NULL, score.cap = NULL,
build.conversion = c(NA, "hg38.to.hg19", "hg18.to.hg19"),
crosslink = c("genehancer", "PCHiC_combined", "GTEx_V6p_combined",
"nearby"), crosslink.customised = NULL, cdf.function = c("original",
"empirical"), scoring.scheme = c("max", "sum", "sequential"),
nearby.distance.max = 50000, nearby.decay.kernel = c("rapid", "slow",
"linear", "constant"), nearby.decay.exponent = 2,
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"), network.customised = NULL, seed.genes = T,
subnet.significance = 5e-05, 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 genomic regions (GR). For this named vector, the element names are GR, in the format of 'chrN:start-end', where N is either 1-22 or X, start (or end) is genomic positional number; for example, 'chr1:13-20', 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 GR, 2nd column for the significance level. Also supported is the input with GR only (without the significance level)

significance.threshold

the given significance threshold. By default, it is set to NULL, meaning there is no constraint on the significance level when transforming the significance level of GR into scores. If given, those GR below this are considered significant and thus scored positively. Instead, those above this are considered insignificant and thus receive no score

score.cap

the maximum score being capped. By default, it is set to NULL, meaning that no capping is applied

build.conversion

the conversion from one genome build to another. The conversions supported are "hg38.to.hg19" and "hg18.to.hg19". By default it is NA (no need to do so)

crosslink

the built-in crosslink info with a score quantifying the link of a GR to a gene. See xGR2xGenes for details

crosslink.customised

the crosslink info with a score quantifying the link of a GR to a gene. A user-input matrix or data frame with 4 columns: 1st column for genomic regions (formatted as "chr:start-end", genome build 19), 2nd column for Genes, 3rd for crosslink score (crosslinking a genomic region to a gene, such as -log10 significance level), and 4th for contexts (optional; if nor provided, it will be added as 'C'). Alternatively, it can be a file containing these 4 columns. Required, otherwise it will return NULL

cdf.function

a character specifying how to transform the input crosslink score. It can be one of 'original' (no such transformation), and 'empirical' for looking at empirical Cumulative Distribution Function (cdf; as such it is converted into pvalue-like values [0,1])

scoring.scheme

the method used to calculate seed gene scores under a set of GR (also over Contexts if many). It can be one of "sum" for adding up, "max" for the maximum, and "sequential" for the sequential weighting. The sequential weighting is done via: \(\sum_{i=1}{\frac{R_{i}}{i}}\), where \(R_{i}\) is the \(i^{th}\) rank (in a descreasing order)

nearby.distance.max

the maximum distance between genes and GR. Only those genes no far way from this distance will be considered as seed genes. This parameter will influence the distance-component weights calculated for nearby GR per gene

nearby.decay.kernel

a character specifying a decay kernel function. It can be one of 'slow' for slow decay, 'linear' for linear decay, and 'rapid' for rapid decay. If no distance weight is used, please select 'constant'

nearby.decay.exponent

a numeric specifying a decay exponent. By default, it sets to 2

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

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 nearby genes that are located within defined distance window centred on lead or LD SNPs). 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". It has graph attributes (evidence, gp_evidence) and node attributes (significance, score).

See Also

xGR2xGeneScores, xSubneterGenes

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/"

# a) provide the seed SNPs with the significance info
data(ImmunoBase)
## only AS GWAS SNPs and their significance info (p-values)
df <- as.data.frame(ImmunoBase$AS$variant, row.names=NULL)
GR <- paste0(df$seqnames,':',df$start,'-',df$end)
data <- cbind(GR=GR, Sig=df$Pvalue)

# b) perform network analysis
# b1) find maximum-scoring subnet based on the given significance threshold
subnet <- xGR2xNet(data=data, crosslink="genehancer",
network="STRING_high", seed.genes=F, subnet.significance=0.01,
RData.location=RData.location)
# b2) find maximum-scoring subnet with the desired node number=30
subnet <- xGR2xNet(data=data, crosslink="genehancer",
network="STRING_high", seed.genes=F, subnet.size=30,
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
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="orange-darkred", ideogram=F,
entity.label.side="out", chr.exclude=NULL,
RData.location=RData.location)
# }