| plotGrandLinear {ggbio} | R Documentation |
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)
obj |
|
... |
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. |
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")
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
gr.t <- transformToGenome(gr)
head(gr.t)
## GRanges with 6 ranges and 2 metadata columns:
## seqnames ranges strand | .start .end
## <Rle> <IRanges> <Rle> | <numeric> <numeric>
## [1] chr1 [ 1, 50] * | 1 50
## [2] chr1 [ 25, 74] * | 25 74
## [3] chr1 [ 50, 99] * | 50 99
## [4] chr1 [ 75, 124] * | 75 124
## [5] chr1 [100, 149] * | 100 149
## [6] chr2 [ 1, 50] * | 180 229
## ---
## seqlengths:
## chr1 chr2
## NA NA
## is
is_coord_genome(gr.t)
## [1] TRUE
metadata(gr.t)$coord
## [1] "genome"
## 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)
## chr1 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr2
## NA NA NA NA NA NA NA NA NA NA NA NA
## chr20 chr21 chr22 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chrX chrY
## NA NA NA NA NA NA NA NA NA NA NA NA
nms <- seqnames(seqinfo(gr.snp))
nms.new <- gsub("chr", "", nms)
names(nms.new) <- nms
gr.snp <- renameSeqlevels(gr.snp, nms.new)
seqlengths(gr.snp)
## 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 3 4 5 6 7 8 9 X Y
## NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## unorder
autoplot(gr.snp, coord = "genome", geom = "point", aes(y = pvalue), space.skip = 0.01)
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## 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)
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## 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)
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## line
autoplot(gr.snp, coord = "genome", geom = "line", aes(y = pvalue, group = seqnames,
color = seqnames))
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## plotGrandLinear
plotGrandLinear(gr.snp, aes(y = pvalue))
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## morecolor
plotGrandLinear(gr.snp, aes(y = pvalue, color = seqnames))
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
plotGrandLinear(gr.snp, aes(y = pvalue), color = c("green", "deepskyblue"))
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
plotGrandLinear(gr.snp, aes(y = pvalue), color = c("green", "deepskyblue", "red"))
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
plotGrandLinear(gr.snp, aes(y = pvalue), color = "red")
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## cutoff
plotGrandLinear(gr.snp, aes(y = pvalue), cutoff = 3, cutoff.color = "blue", cutoff.size = 4)
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## cutoff-low
plotGrandLinear(gr.snp, aes(y = pvalue)) + geom_hline(yintercept = 3, color = "blue", size = 4)
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## 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)
## chr000001 chr000002 chr000003 chr000004 chr000005 chr000006
## 249250621 243199373 198022430 191154276 180915260 171115067
## chr000007 chr000008 chr000009 chr0000010 chr0000011 chr0000012
## 159138663 146364022 141213431 135534747 135006516 133851895
## chr0000013 chr0000014 chr0000015 chr0000016 chr0000017 chr0000018
## 115169878 107349540 102531392 90354753 81195210 78077248
## chr0000019 chr0000020 chr0000021 chr0000022 chr00000X chr00000Y
## 59128983 63025520 48129895 51304566 155270560 59373566
## rotate
plotGrandLinear(gr.snp, aes(y = pvalue)) + theme(axis.text.x=element_text(angle=-90, hjust=0))
## using coord:genome to parse x scale
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## sessionInfo
sessionInfo()
## R Under development (unstable) (2012-07-17 r59871)
## Platform: x86_64-unknown-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=C LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid methods stats graphics grDevices utils datasets
## [8] base
##
## other attached packages:
## [1] biovizBase_1.5.9
## [2] BSgenome.Hsapiens.UCSC.hg19_1.3.19
## [3] BSgenome_1.25.9
## [4] VariantAnnotation_1.3.32
## [5] genefilter_1.39.0
## [6] vsn_3.25.0
## [7] rtracklayer_1.17.21
## [8] Rsamtools_1.9.31
## [9] Biostrings_2.25.12
## [10] TxDb.Hsapiens.UCSC.hg19.knownGene_2.8.0
## [11] GenomicFeatures_1.9.44
## [12] AnnotationDbi_1.19.46
## [13] Biobase_2.17.8
## [14] GenomicRanges_1.9.66
## [15] IRanges_1.15.48
## [16] BiocGenerics_0.3.2
## [17] ggbio_1.5.20
## [18] ggplot2_0.9.2.1
## [19] knitr_0.8
##
## loaded via a namespace (and not attached):
## [1] affy_1.35.1 affyio_1.25.0 annotate_1.35.5
## [4] BiocInstaller_1.5.12 biomaRt_2.13.2 bitops_1.0-4.1
## [7] cluster_1.14.2 colorspace_1.1-1 DBI_0.2-5
## [10] dichromat_1.2-4 digest_0.5.1 evaluate_0.4.2
## [13] formatR_0.6 gridExtra_0.9.1 gtable_0.1.1
## [16] Hmisc_3.9-3 labeling_0.1 lattice_0.20-6
## [19] limma_3.13.24 markdown_0.5.2 MASS_7.3-19
## [22] memoise_0.1 munsell_0.4 parallel_2.16.0
## [25] plyr_1.7.1 preprocessCore_1.19.0 proto_0.3-9.2
## [28] RColorBrewer_1.0-5 RCurl_1.95-0 reshape2_1.2.1
## [31] RSQLite_0.11.2 scales_0.2.2 splines_2.16.0
## [34] stats4_2.16.0 stringr_0.6.1 survival_2.36-14
## [37] tools_2.16.0 XML_3.95-0 xtable_1.7-0
## [40] zlibbioc_1.3.0