## Not run:
# library(biovizBase)
# data(hg19IdeogramCyto, package = "biovizBase")
# library(GenomicRanges)
#
# ## you can also get ideogram by biovizBase::getIdeogram
#
# ## make shorter and clean labels
# old.chrs <- seqnames(seqinfo(hg19IdeogramCyto))
# new.chrs <- gsub("chr", "", old.chrs)
# ## lst <- as.list(new.chrs)
# names(new.chrs) <- old.chrs
# new.ideo <- renameSeqlevels(hg19IdeogramCyto, new.chrs)
# new.ideo <- keepSeqlevels(new.ideo, c(as.character(1:22) , "X", "Y"))
# new.ideo
#
#
# ## sample data
# data(darned_hg19_subset500, package = "biovizBase")
# idx <- is.na(values(darned_hg19_subset500)$exReg)
# values(darned_hg19_subset500)$exReg[idx] <- "unknown"
#
# ## you need to add seqlengths for accruate mapping
# chrnames <- unique(as.character(seqnames(darned_hg19_subset500)))
# data(hg19Ideogram, package = "biovizBase")
# seqlengths(darned_hg19_subset500) <- seqlengths(hg19Ideogram)[sort(chrnames)]
#
#
# dn <- darned_hg19_subset500
# values(dn)$score <- rnorm(length(dn))
#
# ## plotStackedOverview is a simple wrapper around this functions to
# create a stacked layout
# plotStackedOverview(new.ideo, cytoband = TRUE)
#
# plotStackedOverview(dn)
# plotStackedOverview(dn, aes(color = exReg, fill = exReg))
# ## this will did the trick for you to rescale the space
# plotStackedOverview(dn, aes(x = midpoint, y = score), geom = "line")
# plotStackedOverview(dn, aes(x = midpoint, y = score), geom = "line", rescale.range = c(4, 6))
# ## no rescale
# plotStackedOverview(dn, aes(x = midpoint, y = score), geom = "line", rescale = FALSE,
# xlab = "xlab", ylab = "ylab", main = "main") + ylab("ylab")
#
# ## no object? will ask you for species and query the data on the fly
# plotStackedOverview()
# plotStackedOverview(cytoband = TRUE)
# ## End(Not run)
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