gap (version 1.1-16)

mhtplot: Manhattan plot

Description

To generate Manhattan plot, e.g., of genomewide significance (p values) and a random variable that is uniformly distributed. By default, a log10-transformation is applied. Note that with real chromosomal positions, it is also appropriate to plot and some but not all chromosomes.

It is possible to specify options such as xlab and ylim when the plot is requested for data in other context.

Usage

mhtplot(data, control=mht.control(), hcontrol=hmht.control(), ...)

Arguments

data

a data frame with three columns representing chromosome, position and p values

control

A control function named mht.control() with the following arguments,

1. type. a flag with value "p" or "l" indicating if points or lines are to be drawn.
2. usepos. a flag to use real chromosomal positions as composed to ordinal positions with default value FALSE
3. logscale. a flag to indicate if p value is to be log-transformed with default value TRUE
4. base. the base of the logarithm with default value 10
5. cutoffs. the cut-offs where horizontal line(s) are drawn with default value NULL
6. colors. the color for different chromosome(s), and random if unspecified with default values NULL
7. labels. labels for the ticks on x-axis with default value NULL
8. srt. degree to which labels are rotated with default value of 45
9. gap. gap between chromosomes with default value NULL
10. cex. cex for the data points
11. yline. Margin line position
12. xline. Margin line position

hcontrol

A control function named hmht.control() with the following arguments,

1. data. chunk of data to be highlighted with default value NULL
2. colors. colors for annotated genes
3. yoffset. offset above the data point showing most significant p value with default value 0.5
4. cex. shrinkage factor for data points with default value 1.5
5. boxed. if the label for the highlited region with default value FALSE

...

other options in compatible with the R plot function

Value

The plot is shown on or saved to the appropriate device.

See Also

qqunif

Examples

## Not run: 
# # foo example
# test <- matrix(c(1,1,4,1,1,6,1,10,3,2,1,5,2,2,6,2,4,8),byrow=TRUE,6)
# mhtplot(test)
# mhtplot(test,mht.control(logscale=FALSE))
# 
# # fake example with Affy500k data
# affy <-c(40220, 41400, 33801, 32334, 32056, 31470, 25835, 27457, 22864, 28501, 26273, 
#          24954, 19188, 15721, 14356, 15309, 11281, 14881, 6399, 12400, 7125, 6207)
# CM <- cumsum(affy)
# n.markers <- sum(affy)
# n.chr <- length(affy)
# test <- data.frame(chr=rep(1:n.chr,affy),pos=1:n.markers,p=runif(n.markers))
# 
# # to reduce size of the plot
# # bitmap("mhtplot.bmp",res=72*5)
# oldpar <- par()
# par(cex=0.6)
# colors <- rep(c("blue","green"),11)
# # other colors, e.g.
# # colors <- c("red","blue","green","cyan","yellow","gray","magenta","red","blue","green",
# #             "cyan","yellow","gray","magenta","red","blue","green","cyan","yellow","gray",
# #             "magenta","red")
# mhtplot(test,control=mht.control(colors=colors),pch=19,srt=0)
# title("A simulated example according to EPIC-Norfolk QCed SNPs")
# axis(2)
# axis(1,pos=0,labels=FALSE,tick=FALSE)
# abline(0,0)
# # dev.off()
# par(oldpar)
# 
# mhtplot(test,control=mht.control(usepos=TRUE,colors=colors,gap=10000),pch=19,bg=colors)
# title("Real positions with a gap of 10000 bp between chromosomes")
# box()
# 
# png("manhattan.png",height=3600,width=6000,res=600)
# opar <- par()
# par(cex=0.4)
# ops <- mht.control(colors=rep(c("lightgray","lightblue"),11),srt=0,yline=2.5,xline=2)
# mhtplot(mhtdata[,c("chr","pos","p")],ops,xlab="",ylab="",srt=0)
# axis(2,at=1:16)
# title("An adaptable plot as .png")
# par(opar)
# dev.off()
# 
# data <- with(mhtdata,cbind(chr,pos,p))
# glist <- c("IRS1","SPRY2","FTO","GRIK3","SNED1","HTR1A","MARCH3","WISP3","PPP1R3B",
#          "RP1L1","FDFT1","SLC39A14","GFRA1","MC4R")
# hdata <- subset(mhtdata,gene%in%glist)[c("chr","pos","p","gene")]
# color <- rep(c("lightgray","gray"),11)
# glen <- length(glist)
# hcolor <- rep("red",glen)  
# par(las=2, xpd=TRUE, cex.axis=1.8, cex=0.4)
# ops <- mht.control(colors=color,yline=1.5,xline=3,labels=paste("chr",1:22,sep=""),
#                    srt=270)
# hops <- hmht.control(data=hdata,colors=hcolor)
# mhtplot(data,ops,hops,pch=19)
# axis(2,pos=2,at=1:16)
# title("Manhattan plot with genes highlighted",cex.main=1.8)
# 
# mhtplot(data,mht.control(cutoffs=c(4,6,8,16)),pch=19)
# title("Another plain Manhattan plot")
# 
# ## End(Not run)