xPierSNPsConsensus | R Documentation

## Function to resolve relative importance of distance weight and eQTL weight priorising consensus gene ranks given a list of seed SNPs together with the significance level (e.g. GWAS reported p-values)

### Description

`xPierSNPsConsensus` is supposed to priorise genes given a list of seed SNPs together with the significance level. It is a parameter-free version of `xPierSNPs` identifying the consensus rank (less sensitive to the relative importance of the distance weight and eQTL weight). It returns an object of class "pNode" but appended with components on optimal distance weight and consensus info

### Usage

    xPierSNPsConsensus(data, include.LD = NA, LD.customised = NULL,
    LD.r2 = 0.8, significance.threshold = 5e-05, distance.max = 2e+05,
    decay.kernel = c("rapid", "slow", "linear"), decay.exponent = 2,
    GR.SNP = c("dbSNP_GWAS", "dbSNP_Common"), GR.Gene = c("UCSC_knownGene",
    "UCSC_knownCanonical"), include.eQTL = c(NA, "JKscience_TS2A",
    "JKscience_TS2B", "JKscience_TS3A", "JKng_bcell", "JKng_mono",
    "JKnc_neutro",
    "JK_nk", "GTEx_V4_Adipose_Subcutaneous", "GTEx_V4_Artery_Aorta",
    "GTEx_V4_Artery_Tibial", "GTEx_V4_Esophagus_Mucosa",
    "GTEx_V4_Esophagus_Muscularis", "GTEx_V4_Heart_Left_Ventricle",
    "GTEx_V4_Lung", "GTEx_V4_Muscle_Skeletal", "GTEx_V4_Nerve_Tibial",
    "GTEx_V4_Skin_Sun_Exposed_Lower_leg", "GTEx_V4_Stomach",
    "GTEx_V4_Thyroid",
    "GTEx_V4_Whole_Blood", "eQTLdb_NK", "eQTLdb_CD14", "eQTLdb_LPS2",
    "eQTLdb_LPS24", "eQTLdb_IFN"), eQTL.customised = NULL,
    cdf.function = c("empirical", "exponential"), 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"), weighted = FALSE,
    network.customised = NULL, normalise = c("laplacian", "row", "column",
    "none"), restart = 0.75, normalise.affinity.matrix = c("none",
    "quantile"), parallel = TRUE, multicores = NULL, verbose = TRUE,
    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 pre-calculated 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
`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
`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"
`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"
`include.eQTL` | genes modulated by eQTL (also Lead SNPs or in LD with Lead SNPs) are also included. By default, it is 'NA' to disable this option. Otherwise, those genes modulated by eQTL will be included: immune stimulation in monocytes ('JKscience_TS1A' and 'JKscience_TS2B' for cis-eQTLs or 'JKscience_TS3A' for trans-eQTLs) from Science 2014, 343(6175):1246949; cis- and trans-eQTLs in B cells ('JKng_bcell') and in monocytes ('JKng_mono') from Nature Genetics 2012, 44(5):502-510; cis- and trans-eQTLs in neutrophils ('JKnc_neutro') from Nature Communications 2015, 7(6):7545; cis-eQTLs in NK cells ('JK_nk') which is unpublished. Also supported are GTEx cis-eQTLs from Science 2015, 348(6235):648-60, including 13 tissues: 'GTEx_Adipose_Subcutaneous','GTEx_Artery_Aorta','GTEx_Artery_Tibial','GTEx_Esophagus_Mucosa','GTEx_Esophagus_Muscularis','GTEx_Heart_Left_Ventricle','GTEx_Lung','GTEx_Muscle_Skeletal','GTEx_Nerve_Tibial','GTEx_Skin_Sun_Exposed_Lower_leg','GTEx_Stomach','GTEx_Thyroid','GTEx_Whole_Blood'.
`eQTL.customised` | a user-input matrix or data frame with 3 columns: 1st column for SNPs/eQTLs, 2nd column for Genes, and 3rd for eQTL mapping significance level (p-values or FDR). It is designed to allow the user analysing their eQTL data. This customisation (if provided) has the high priority over built-in eQTL data.
`cdf.function` | a character specifying a Cumulative Distribution Function (cdf). It can be one of 'exponential' based on exponential cdf, 'empirical' for empirical cdf
`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 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), "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 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
`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
`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. If the user provides the "igraph" object with the "weight" edge attribute, RWR will assume to walk on the weighted network
`normalise` | the way to normalise the adjacency matrix of the input graph. It can be 'laplacian' for laplacian normalisation, 'row' for row-wise normalisation, 'column' for column-wise normalisation, or 'none'
`restart` | the restart probability used for Random Walk with Restart (RWR). The restart probability takes the value from 0 to 1, controlling the range from the starting nodes/seeds that the walker will explore. The higher the value, the more likely the walker is to visit the nodes centered on the starting nodes. At the extreme when the restart probability is zero, the walker moves freely to the neighbors at each step without restarting from seeds, i.e., following a random walk (RW)
`normalise.affinity.matrix` | the way to normalise the output affinity matrix. It can be 'none' for no normalisation, 'quantile' for quantile normalisation to ensure that columns (if multiple) of the output affinity matrix have the same quantiles
`parallel` | logical to indicate whether parallel computation with multicores is used. By default, it sets to true, but not necessarily does so. Partly because parallel backends available will be system-specific (now only Linux or Mac OS). Also, it will depend on whether these two packages "foreach" and "doMC" have been installed. It can be installed via: `source("http://bioconductor.org/biocLite.R"); biocLite(c("foreach","doMC"))`. If not yet installed, this option will be disabled
`multicores` | an integer to specify how many cores will be registered as the multicore parallel backend to the 'foreach' package. If NULL, it will use a half of cores available in a user's computer. This option only works when parallel computation is enabled
`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

an object of class "pNode", a list with following components:

- `priority`: a matrix of nNode X 4 containing node priority information, where nNode is the number of nodes in the input graph, and the 4 columns are "name" (node names), "seed" (1 for seeds, 0 for non-seeds), "weight" (weight/score values for seed genes), "priority" (the priority scores that are rescaled to the range [0,1]), "rank" (ranks of the priority scores), and two additional columns: 'driver' telling who drives the prioritisation ('nGenes','eGenes' or'both'), and 'consensus_rank'

- `g`: an input "igraph" object

- `SNP`: a data frame of nSNP X 3 containing input SNPs and/or LD SNPs info, where nSNP is the number of input SNPs and/or LD SNPs, and the 3 columns are "SNP" (dbSNP), "Score" (the SNP score), "Pval" (the SNP p-value)

- `Gene2SNP`: a matrix of Genes X SNPs, each non-zero cell telling an SNP's genetic influential score on a seed gene

- `nGenes`: the relative weight for nearby genes

- `consensus`: a matrix containing details on rank results by decreasing the relative importance of nGenes. In addition to rank matrix, it has columns 'rank_median' for median rank excluding two extremes ‘n_1' (nGenes only) and 'n_0' (eGenes only), ’rank_MAD' for median absolute deviation, 'driver' telling who drives the prioritisation ('nGenes','eGenes' or'both'), 'consensus_rank' for the rank of the median rank list

- `call`: the call that produced this result

### Note

none

### See Also

`xPierSNPs`

### Examples

    ## Not run:
    # Load the library
    library(Pi)

    ## End(Not run)

    # a) provide the SNPs with the significance info
    ## get lead SNPs reported in AS GWAS and their significance info (p-values)
    #data.file <- "http://galahad.well.ox.ac.uk/bigdata/AS.txt"
    #AS <- read.delim(data.file, header=TRUE, stringsAsFactors=FALSE)
    ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase')
    gr <- ImmunoBase$AS$variants
    AS <- as.data.frame(GenomicRanges::mcols(gr)[, c('Variant','Pvalue')])

    ## Not run:
    # b) perform priority analysis
    pNode <- xPierSNPsConsensus(data=AS, include.LD="EUR",
    include.eQTL=c("JKscience_TS2A","JKscience_TS3A"),
    network="PCommonsUN_medium", restart=0.7)

    # c) save to the file called 'SNPs_priority.consensus.txt'
    write.table(pNode$priority, file="SNPs_priority.consensus.txt",
    sep="\t", row.names=FALSE)

    # d) manhattan plot
    mp <- xPierManhattan(pNode, highlight.top=10)
    #pdf(file="Gene_manhattan.pdf", height=6, width=12, compress=TRUE)
    print(mp)
    #dev.off()

    ## End(Not run)