xSubneterGR | R Documentation

## Function to identify a gene network from an input network given a list of genomic regions together with the significance level

### Description

`xSubneterGR` 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 genomic regions (GR). It returns an object of class "igraph".

### Usage

    xSubneterGR(data, significance.threshold = 5e-05, score.cap = 10,
    build.conversion = c(NA, "hg38.to.hg19", "hg18.to.hg19"),
    distance.max = 50000, decay.kernel = c("slow", "linear", "rapid",
    "constant"), decay.exponent = 2, 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,
    test.permutation = F, num.permutation = 100, respect = c("none",
    "degree"), aggregateBy = c("Ztransform", "fishers", "logistic",
    "orderStatistic"), verbose = T, silent = F,
    RData.location = "http://galahad.well.ox.ac.uk/bigdata")

### Arguments

`data` | a named input vector containing the sinificance 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.
`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 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
`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)
`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
`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` | a numeric specifying a decay exponent. By default, it sets to 2
`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 GR. 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
`test.permutation` | logical to indicate whether the permutation test is perform to estimate the significance of identified network with the same number of nodes. By default, it sets to false
`num.permutation` | the number of permutations generating the null distribution of the identified network
`respect` | how to respect nodes to be sampled. It can be one of 'none' (randomly sampling) and 'degree' (degree-preserving sampling)
`aggregateBy` | the aggregate method used to aggregate edge confidence p-values. It can be either "orderStatistic" for the method based on the order statistics of p-values, or "fishers" for Fisher's method, "Ztransform" for Z-transform method, "logistic" for the logistic method. Without loss of generality, the Z-transform method does well in problems where evidence against the combined null is spread widely (equal footings) or when the total evidence is weak; Fisher's method does best in problems where the evidence is concentrated in a relatively small fraction of the individual tests or when the evidence is at least moderately strong; the logistic method provides a compromise between these two. Notably, the aggregate methods 'Ztransform' and 'logistic' are preferred here
`verbose` | logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
`silent` | logical to indicate whether the messages will be silent completely. By default, it sets to false. If true, verbose will be forced to be false
`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. If permutation test is enabled, it also has a graph attribute (combinedP) and an edge attribute (edgeConfidence)

### Note

The algorithm identifying a gene subnetwork that is likely modulated by input genomic regions (GR) includes two major steps. The first step is to use `xGR2GeneScores` for defining and scoring nearby genes that are located within distance window of input GR. 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

`xGR2GeneScores`, `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
    ## 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
    df <- as.data.frame(gr, row.names=NULL)
    chr <- df$seqnames
    start <- df$start
    end <- df$end
    sig <- df$Pvalue
    GR <- paste(chr,':',start,'-',end, sep='')
    data <- cbind(GR=GR, Sig=sig)

    # b) perform network analysis
    # b1) find maximum-scoring subnet based on the given significance threshold
    subnet <- xSubneterGR(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 <- xSubneterGR(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)