plotGrandLinear: Manhattan for GWAS
In ggbio: Visualization tools for genomic data

Description Usage Arguments Details Value Author(s) Examples

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.

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)

`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.

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.

Return a ggplot object.

Tengfei Yin

##  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()