xSubneterSNPs | R Documentation

## Function to identify a gene network from an input network given a list of seed SNPs together with the significance level (e.g. GWAS reported p-values)

### Description

`xSubneterSNPs` is supposed to identify maximum-scoring gene subnetwork from an input graph with the node information on the significance (measured as p-values or fdr). To do so, it defines seed genes and their scores that take into account the distance to and the significance of input SNPs. It returns an object of class "igraph".

### Usage

    xSubneterSNPs(data, include.LD = NA, LD.customised = NULL, LD.r2 = 0.8,
    significance.threshold = 5e-05, score.cap = 10, distance.max = 2e+05,
    decay.kernel = c("slow", "linear", "rapid", "constant"),
    decay.exponent = 2, GR.SNP = c("dbSNP_GWAS", "dbSNP_Common"),
    GR.Gene = c("UCSC_knownGene", "UCSC_knownCanonical"),
    scoring.scheme = c("max", "sum", "sequential"),
    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 sinificance level for nodes (dbSNP). For this named vector, the element names are dbSNP ID (or in the format such as 'chr16:28525386'), 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 dbSNP, 2nd column for the significance level
`include.LD` | additional SNPs in LD with Lead SNPs are also included. By default, it is 'NA' to disable this option. Otherwise, LD SNPs will be included based on one or more of 26 populations and 5 super populations from 1000 Genomics Project data (phase 3). The population can be one of 5 super populations ("AFR", "AMR", "EAS", "EUR", "SAS"), or one of 26 populations ("ACB", "ASW", "BEB", "CDX", "CEU", "CHB", "CHS", "CLM", "ESN", "FIN", "GBR", "GIH", "GWD", "IBS", "ITU", "JPT", "KHV", "LWK", "MSL", "MXL", "PEL", "PJL", "PUR", "STU", "TSI", "YRI"). Explanations for population code can be found at http://www.1000genomes.org/faq/which-populations-are-part-your-study
`LD.customised` | a user-input matrix or data frame with 3 columns: 1st column for Lead SNPs, 2nd column for LD SNPs, and 3rd for LD r2 value. It is designed to allow the user analysing their precalcuated LD info. This customisation (if provided) has the high priority over built-in LD SNPs
`LD.r2` | the LD r2 value. By default, it is 0.8, meaning that SNPs in LD (r2>=0.8) with input SNPs will be considered as LD SNPs. It can be any value from 0.8 to 1
`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 SNPs into scores. If given, those SNPs below this are considered significant and thus scored positively. Instead, those above this are considered insigificant and thus receive no score
`score.cap` | the maximum score being capped. By default, it is set to 10. If NULL, no capping is applied
`distance.max` | the maximum distance between genes and SNPs. 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 SNPs per gene
`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'
`decay.exponent` | an integer specifying a decay exponent. By default, it sets to 2
`GR.SNP` | the genomic regions of SNPs. By default, it is 'dbSNP_GWAS', that is, SNPs from dbSNP (version 146) restricted to GWAS SNPs and their LD SNPs (hg19). It can be 'dbSNP_Common', that is, Common SNPs from dbSNP (version 146) plus GWAS SNPs and their LD SNPs (hg19). Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to dbSNP IDs. Then, tell "GR.SNP" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
`GR.Gene` | the genomic regions of genes. By default, it is 'UCSC_knownGene', that is, UCSC known genes (together with genomic locations) based on human genome assembly hg19. It can be 'UCSC_knownCanonical', that is, UCSC known canonical genes (together with genomic locations) based on human genome assembly hg19. Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to Gene Symbols. Then, tell "GR.Gene" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
`scoring.scheme` | the method used to calculate seed gene scores under a set of SNPs. 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: ∑_{i=1}{\frac{R_{i}}{i}}, where R_{i} is the i^{th} rank (in a descreasing order)
`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 ndoe attributes: significance, score, type

### Note

The algorithm identifying a gene subnetwork that is likely modulated by input SNPs and/or their LD SNPs includes two major steps. The first step is to use `xSNP2GeneScores` for defining and scoring nearby genes that are located within distance window of input and/or LD SNPs. The second step is to use `xSubneterGenes` for identifying a maximum-scoring gene subnetwork that contains as many highly scored genes as possible but a few less scored genes as linkers.

### See Also

`xSNP2GeneScores`, `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_dev/"

    # a) provide the seed SNPs with the weight info
    ## load ImmunoBase
    ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase',
    RData.location=RData.location)
    ## get lead SNPs reported in AS GWAS and their significance info (p-values)
    gr <- ImmunoBase$AS$variant
    data <- GenomicRanges::mcols(gr)[,c(1,3)]

    # b) perform network analysis
    # b1) find maximum-scoring subnet based on the given significance threshold
    subnet <- xSubneterSNPs(data=data, 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 <- xSubneterSNPs(data=data, 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="white-gray",
    RData.location=RData.location)

    ## End(Not run)