tsByRank {gQTLstats} | R Documentation

## harvest contents of a TransStore by rank in associations of features to SNP

### Description

Harvest contents of a TransStore by rank in associations of features to SNP.

### Usage

    tsByRankAccum(tsin, maxrank = 3, mcol2keep=c("REF", "ALT", "snp", "MAF", "z.HWE"), filt=force)

### Arguments

`tsin` | An instance of `TransStore-class`
`maxrank` | The maximum rank of association scores to retrieve, cumulatively. Each variant has been tested for association with each genomic feature (e.g., gene in a typical expression QTL study), but only the top ranking associations are recorded for each variant. If maxrank=k, for each variant, this function retrieves the features exhibiting the kth largest association recorded over all features, along with all k-1 larger association scores.
`mcol2keep` | a character vector of metadata columns to retain
`filt` | a function accepting a GRanges and returning a GRanges. The mcols of the GRanges to be processed will have elements `c(mcol2keep, "scorebuf", "elnames", "dist")`, where the latter two are matrices with number of columns equal to the `bufsize` of the `transAssoc` call that generated `ts`. Only SNP-specific elements can be used to define the filter.

### Details

tsByRankAccum_sing and other functions with suffix _sing were developed for the case of a single permutation

getTransRegistries() accesses objects packaged for demonstration purposes

### Value

A `GRanges` instance.

### Examples

    ## Not run:  # dec 2017 -- BatchJobs registry must be updated
     if (require(doParallel)) {
      registerDoSEQ()
     lit = TransStore(getTransRegistries()) # very limited slice
     tbga = tsByRankAccum(lit, maxrank=5)
     plot(ecdf(as.numeric(data.matrix(tbga$permscoresByRank1))), ylim=c(.99,1),
        main="eCDF of permutation dist. of association, by variant rank")
     exr = paste0("permscoresByRank", 2:5)
     for (i in 1:4)
       lines(ecdf(as.numeric(data.matrix(mcols(tbga)[[exr[i]]]))), col=i+1)
     legend(200, .994, lty=1, col=1:5, legend=paste("rank", 1:5))
     plot(ecdf(as.numeric(data.matrix(tbga$permscoresByRank1[,1]))), ylim=c(.99,1),
        main="between-permutation variation")
     lines(ecdf(as.numeric(data.matrix(tbga$permscoresByRank1[,2]))),col=2)
     lines(ecdf(as.numeric(data.matrix(tbga$permscoresByRank1[,3]))),col=3)
     lines(ecdf(as.numeric(data.matrix(tbga$permscoresByRank5[,1]))),col=4)
     lines(ecdf(as.numeric(data.matrix(tbga$permscoresByRank5[,2]))),col=5)
     lines(ecdf(as.numeric(data.matrix(tbga$permscoresByRank5[,3]))),col=6)
     legend(200, .994, col=1:6, lty=1, legend=c("rank 1 (perm 1)", "(2)", "(3)",
      "rank 5 (perm 1)", "(2)", "(3)"))
    # head(tbga,2)
     # consider the following filtering utility
    # tbfilt = function(tbg, seqnames.="17", minMAF=.1, minabsodist = 1e7,
    #    nrec=1000) {
    #   tbg = tbg[ which(as.character(seqnames(tbg)) %in% seqnames.) ]
    #   tbg = tbg[ which(tbg$MAF > minMAF) ]
    #   tbg[ order(tbg$scores, decreasing=TRUE) ][1:nrec]
    # }
     }

    ## End(Not run)

[Package gQTLstats version 1.12.0 Index]