avs.rvse {RTN} | R Documentation

## An rQTL/VSE pipeline for variant set enrichment analysis.

### Description

The VSE method (`avs.vse`) provides a robust framework to cope with the heterogeneous structure of haplotype blocks, and has been designed to test enrichment in cistromes and epigenomes. In order to extend the variant set enrichment to genes this pipeline implements an additional step using regulon quantitative trait loci (rQTLs).

### Usage

    avs.rvse(object, annotation, regdata, snpdata, glist,
    maxgap=250, minSize=100, pValueCutoff=0.05,
    pAdjustMethod="bonferroni", boxcox=TRUE, verbose=TRUE)

### Arguments

`object` | an object of class `AVS-class`.
`annotation` | a data frame with genomic annotations listing chromosome coordinates to which a particular property or function has been attributed. It should include the following columns: <CHROM>, <START>, <END> and <ID>. The <ID> column can be any genomic identifier, while values in <CHROM> should be listed in ['chr1', 'chr2', 'chr3' ..., 'chrX']. Both <START> and <END> columns correspond to chromosome positions mapped to the human genome assembly used to build the AVS object.
`regdata` | object of class "matrix", a regulon activity matrix.
`snpdata` | either an object of class "matrix" or "ff", a single nucleotide polymorphism (SNP) matrix.
`glist` | a list with character vectors mapped to the 'annotation' data via <ID> column. This list is used to run a batch mode for gene sets and regulons.
`maxgap` | a single integer value specifying the max distant (kb) between the AVS and the annotation used to compute the eQTL analysis.
`minSize` | if 'glist' is provided, this argument is a single integer or numeric value specifying the minimum number of elements for each gene set in the 'glist'. Gene sets with fewer than this number are removed from the analysis.
`pValueCutoff` | a single numeric value specifying the cutoff for p-values considered significant.
`pAdjustMethod` | a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details).
`boxcox` | a single logical value specifying to use Box-Cox procedure to find a transformation of the null that approaches normality (when boxcox=TRUE) or not (when boxcox=FALSE). See `powerTransform` and `bcPower`.
`verbose` | a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).

### Value

a data frame in the slot "results", see 'what' options in `avs.get`.

### Author(s)

Mauro Castro

### See Also

`AVS-class`

### Examples

    ## Not run:
    # This example requires the RTNdata package! (currently available under request)
    library(RTNdata.LDHapMapRel27.hg18)
    library(Fletcher2013b)
    library(TxDb.Hsapiens.UCSC.hg18.knownGene)

    ##################################################
    ### Build AVS and random AVSs (mapped to hg18)
    ##################################################

    #--- step 1: load 'risk SNPs' data (e.g. BCa risk SNPs from the GWAS catalog)
    data(bcarisk, package = "RTNdata.LDHapMapRel27.hg18")

    #--- step 2: build an AVS and 1000 matched random AVSs for the input 'risk SNPs'
    bcavs <- avs.preprocess.LDHapMapRel27.hg18(bcarisk, nrand=1000)

    ##################################################
    ### Example of RVSE analysis
    ##################################################

    #--- step 1: load a precomputed AVS (same 'bcavs' object as above!)
    data(bcavs, package="RTNdata.LDHapMapRel27.hg18")

    #--- step 2: load genomic annotation for all genes
    genemap <- as.data.frame(genes(TxDb.Hsapiens.UCSC.hg18.knownGene))
    genemap <- genemap[,c("seqnames","start","end","gene_id")]
    colnames(genemap) <- c("CHROM","START","END","ID")

    #--- step 3: load a TNI object and get a list with regulons
    #--- (ids should be the same as in 'genemap')
    data("rtni1st")
    glist <- tni.get(rtni1st,what="refregulons",idkey="ENTREZ")
    glist <- glist[ c("FOXA1","GATA3","ESR1") ] #reduce the list for demonstration!

    #--- step 4: compute single-sample regulon activity
    rtni1st <- tni.gsea2(rtni1st, regulatoryElements = c("FOXA1","GATA3","ESR1"))
    regdata <- tni.get(rtni1st, what = "regulonActivity")$diff

    #--- step 5: load a variation dataset matched with the regulon activity dataset
    #--- here we use a "toy" dataset for demonstration purposes only.
    data(toy_snpdata, package="RTNdata.LDHapMapRel27")

    #--- step 6: check "snpdata"" and "regdata"" alignment (samples on cols)
    regdata <- t(regdata)
    all(colnames(toy_snpdata)==colnames(regdata))
    #TRUE

    #--- step 7: run the avs.rvse pipeline
    bcavs <- avs.rvse(bcavs,
                      annotation=genemap,
                      regdata=regdata,
                      snpdata=toy_snpdata,
                      glist=glist,
                      pValueCutoff=0.01)

    #--- step 8: generate the RVSE plot
    avs.plot2(bcavs, "rvse", height=2.5)

    ## End(Not run)

[Package RTN version 2.16.0 Index]