plotGrandLinear {ggbio} | R Documentation

## Manhattan for GWAS

### Description

A Manhattan plot is special scatter plot used to visualize data with a large number of data points, with a distribute of some higher-magnitude values. For example, in the GWAS(genome-wide association studies). Here we mainly focus on GWAS Manhattan plots. X-axis is genomic coordinates and Y-axis is negative logarithm of the associated P-value for each single nucleotide polymorphism. So higher the value, more stronger the association they are.

### Usage

    plotGrandLinear(obj, ..., facets, space.skip = 0.01, geom = NULL,
                     cutoff = NULL, cutoff.color = "red", cutoff.size = 1,
                     legend = FALSE, xlim, ylim, xlab, ylab, main,
                     highlight.gr = NULL, highlight.name = NULL,
                     highlight.col = "red", highlight.label = TRUE,
                     highlight.label.size = 5, highlight.label.offset =
                     0.05, highlight.label.col = "black", spaceline =
                     FALSE)

### Arguments

`obj` | `GRanges` object which contains extra p value, before users pass this object, they need to make sure the pvalue has been changed to -log10(p).
`...` | extra arguments passed. such as color, size, alpha.
`facets` | facets formula, such as group ~ .
`space.skip` | numeric value for skip ratio, between chromosome spaces.default is 0.01.
`geom` | geometric object, defualt is "point".
`cutoff` | A numeric vector which used as cutoff for Manhattan plot.
`cutoff.color` | A character specifying the color used for cutoff. Default is "red".
`cutoff.size` | A numeric value which used as cutoff line size.
`legend` | A logical value indicate whether to show legend or not. Default is FALSE which disabled the legend.
`xlim` | limits for x scale.
`ylim` | limits for y scale.
`xlab` | Label for xscale.
`ylab` | Label for yscale.
`main` | title.
`highlight.gr` | a `GRanges` object, this wil highlight overlapped region with provided intervals.
`highlight.name` | if `NULL`, using rownames of `GRanges` object provided by argument `highlight.gr`, otherwise use character to indicate column used as labeled names.
`highlight.col` | highlight colors.
`highlight.label` | logical value, label the highlighted region of not.
`highlight.label.size` | highlight label size.
`highlight.label.offset` | highlight label offset.
`highlight.label.col` | highlight label color.
`spaceline` | show line between chromosomes.

### Details

Please use seqlengths of the object and space.skip arguments to control the layout of the coordiant genome transformation.

aes(y = ...) is requried.

aes(color = ) is used to mapping to data variables, if just pass "color" without aes(), then will recycle the color to represent each chromosomes.please see the example below.

### Value

Return a ggplot object.

### Author(s)

Tengfei Yin

### Examples

    ##  load
    library(ggbio)
    data(hg19IdeogramCyto, package = "biovizBase")
    data(hg19Ideogram, package = "biovizBase")
    library(GenomicRanges)

    ##  simul_gr
    library(biovizBase)
    gr <- GRanges(rep(c("chr1", "chr2"), each = 5),
                  IRanges(start = rep(seq(1, 100, length = 5), times = 2),
                          width = 50))
    autoplot(gr)

    ##  coord:genome
    autoplot(gr, coord = "genome")
    gr.t <- transformToGenome(gr)
    head(gr.t)

    ##  is
    is_coord_genome(gr.t)
    metadata(gr.t)$coord

    ##  simul_snp
    chrs <- as.character(levels(seqnames(hg19IdeogramCyto)))
    seqlths <- seqlengths(hg19Ideogram)[chrs]
    set.seed(1)
    nchr <- length(chrs)
    nsnps <- 100
    gr.snp <- GRanges(rep(chrs,each=nsnps),
                      IRanges(start =
                              do.call(c, lapply(chrs, function(chr){
                                N <- seqlths[chr]
                                runif(nsnps,1,N)
                              })), width = 1),
                      SNP=sapply(1:(nchr*nsnps), function(x) paste("rs",x,sep='')),
                      pvalue =  -log10(runif(nchr*nsnps)),
                      group = sample(c("Normal", "Tumor"), size = nchr*nsnps,
                        replace = TRUE)
                      )

    ##  shorter
    seqlengths(gr.snp)
    nms <- seqnames(seqinfo(gr.snp))
    nms.new <- gsub("chr", "", nms)
    names(nms.new) <- nms
    gr.snp <- renameSeqlevels(gr.snp, nms.new)
    seqlengths(gr.snp)

    ##  unorder
    autoplot(gr.snp, coord = "genome", geom = "point", aes(y = pvalue), space.skip = 0.01)

    ##  sort
    gr.snp <- keepSeqlevels(gr.snp, c(1:22, "X", "Y"))
    autoplot(gr.snp, coord = "genome", geom = "point", aes(y = pvalue), space.skip = 0.01)

    ##  with_seql
    names(seqlths) <- gsub("chr", "", names(seqlths))
    seqlengths(gr.snp) <- seqlths[names(seqlengths(gr.snp))]
    autoplot(gr.snp, coord = "genome", geom = "point", aes(y = pvalue), space.skip = 0.01)

    ##  line
    autoplot(gr.snp, coord = "genome", geom = "line", aes(y = pvalue, group = seqnames,
                                         color = seqnames))

    ##  plotGrandLinear
    plotGrandLinear(gr.snp, aes(y = pvalue))

    ##  morecolor
    plotGrandLinear(gr.snp, aes(y = pvalue, color = seqnames))
    plotGrandLinear(gr.snp, aes(y = pvalue), color = c("green", "deepskyblue"))
    plotGrandLinear(gr.snp, aes(y = pvalue), color = c("green", "deepskyblue", "red"))
    plotGrandLinear(gr.snp, aes(y = pvalue), color = "red")

    ##  cutoff
    plotGrandLinear(gr.snp, aes(y = pvalue), cutoff = 3, cutoff.color = "blue", cutoff.size = 4)

    ##  cutoff-low
    plotGrandLinear(gr.snp, aes(y = pvalue)) + geom_hline(yintercept = 3, color = "blue", size = 4)

    ##  longer
    ## let's make a long name
    nms <- seqnames(seqinfo(gr.snp))
    nms.new <- paste("chr00000", nms, sep = "")
    names(nms.new) <- nms
    gr.snp <- renameSeqlevels(gr.snp, nms.new)
    seqlengths(gr.snp)

    ##  rotate
    plotGrandLinear(gr.snp, aes(y = pvalue)) + theme(axis.text.x=element_text(angle=-90, hjust=0))

    ##  sessionInfo
    sessionInfo()

[Package ggbio version 1.38.0 Index]