CMplot is available on CRAN, so it can be installed with the following R code:
> install.packages("CMplot")
> library("CMplot")
# if you want to use the latest version:
> source("https://raw.githubusercontent.com/YinLiLin/R-CMplot/master/R/CMplot.r")There are two example datasets attached in CMplot, users can export and view the details by following R code:
> data(pig60K) #calculated p-values by MLM
> data(cattle50K) #calculated SNP effects by rrblup
> head(pig60K)
SNP Chromosome Position trait1 trait2 trait3
1 ALGA0000009 1 52297 0.7738187 0.51194318 0.51194318
2 ALGA0000014 1 79763 0.7738187 0.51194318 0.51194318
3 ALGA0000021 1 209568 0.7583016 0.98405289 0.98405289
4 ALGA0000022 1 292758 0.7200305 0.48887140 0.48887140
5 ALGA0000046 1 747831 0.9736840 0.22096836 0.22096836
6 ALGA0000047 1 761957 0.9174565 0.05753712 0.05753712
> head(cattle50K)
SNP chr pos Somatic cell score Milk yield Fat percentage
1 SNP1 1 59082 0.000244361 0.000484255 0.001379210
2 SNP2 1 118164 0.000532272 0.000039800 0.000598951
3 SNP3 1 177246 0.001633058 0.000311645 0.000279427
4 SNP4 1 236328 0.001412865 0.000909370 0.001040161
5 SNP5 1 295410 0.000090700 0.002202973 0.000351394
6 SNP6 1 354493 0.000110681 0.000342628 0.000105792
As the example datasets, the first three columns are names, chromosome, position of SNPs respectively, the res of columns are the pvalues of GWAS or effects of GS/GP for traits, the number of traits is unlimited. Note: if plotting SNP_Density, only the first three columns are needed.
Now CMplot could handle not only Genome-wide association study results, but also SNP effects, Fst, tajima's D and so on.
Total 50~ parameters are available in CMplot, typing ?CMplot can get the detail function of all parameters.
Please cite the website https://github.com/YinLiLin/R-CMplot
> CMplot(pig60K,plot.type="d",bin.size=1e6,chr.den.col=c("darkgreen", "yellow", "red"),file="jpg",memo="",dpi=300,
file.output=TRUE,verbose=TRUE,width=9,height=6)
# users can personally set the windowsize and the min/max of legend by:
# bin.size=1e6
# bin.range=c(min, max)
# memo: add a character to the output file name
# chr.labels: change the chromosome names
> CMplot(pig60K,plot.type="c",chr.labels=paste("Chr",c(1:18,"X"),sep=""),r=0.4,cir.legend=TRUE,
outward=FALSE,cir.legend.col="black",cir.chr.h=1.3,chr.den.col="black",file="jpg",
memo="",dpi=300,file.output=TRUE,verbose=TRUE,width=10,height=10)
> CMplot(pig60K,plot.type="c",r=0.4,col=c("grey30","grey60"),chr.labels=paste("Chr",c(1:18,"X"),sep=""),
threshold=c(1e-6,1e-4),cir.chr.h=1.5,amplify=TRUE,threshold.lty=c(1,2),threshold.col=c("red",
"blue"),signal.line=1,signal.col=c("red","green"),chr.den.col=c("darkgreen","yellow","red"),
bin.size=1e6,outward=FALSE,file="jpg",memo="",dpi=300,file.output=TRUE,verbose=TRUE,width=10,height=10)
#Note:
1. if signal.line=NULL, the lines that crosse circles won't be added.
2. if the length of parameter 'chr.den.col' is not equal to 1, SNP density that counts
the number of SNP within given size('bin.size') will be plotted around the circle.
> CMplot(cattle50K,plot.type="c",LOG10=FALSE,outward=TRUE,col=matrix(c("#4DAF4A",NA,NA,"dodgerblue4",
"deepskyblue",NA,"dodgerblue1", "olivedrab3", "darkgoldenrod1"), nrow=3, byrow=TRUE),
chr.labels=paste("Chr",c(1:29),sep=""),threshold=NULL,r=1.2,cir.chr.h=1.5,cir.legend.cex=0.5,
cir.band=1,file="jpg", memo="",dpi=300,chr.den.col="black",file.output=TRUE,verbose=TRUE,
width=10,height=10)
#Note: parameter 'col' can be either vector or matrix, if a matrix, each trait can be plotted in different colors.
> CMplot(pig60K,plot.type="m",LOG10=TRUE,threshold=NULL,file="jpg",memo="",dpi=300,
file.output=TRUE,verbose=TRUE,width=14,height=6)
> CMplot(pig60K, plot.type="m", col=c("grey30","grey60"), LOG10=TRUE, ylim=c(2,12), threshold=c(1e-6,1e-4),
threshold.lty=c(1,2), threshold.lwd=c(1,1), threshold.col=c("black","grey"), amplify=TRUE,
chr.den.col=NULL, signal.col=c("red","green"), signal.cex=c(1,1),signal.pch=c(19,19),
file="jpg",memo="",dpi=300,file.output=TRUE,verbose=TRUE,width=14,height=6)
#Note: if the ylim is setted, then CMplot will only plot the ponits which among this interval.
> CMplot(pig60K, plot.type="m", LOG10=TRUE, ylim=NULL, threshold=c(1e-6,1e-4),threshold.lty=c(1,2),
threshold.lwd=c(1,1), threshold.col=c("black","grey"), amplify=TRUE,bin.size=1e6,
chr.den.col=c("darkgreen", "yellow", "red"),signal.col=c("red","green"),signal.cex=c(1,1),
signal.pch=c(19,19),file="jpg",memo="",dpi=300,file.output=TRUE,verbose=TRUE,
width=14,height=6)
#Note: if the length of parameter 'chr.den.col' is bigger than 1, SNP density that counts
the number of SNP within given size('bin.size') will be plotted.
> signal <- pig60K$Position[which.min(pig60K$trait2)]
> SNPs <- pig60K$SNP[pig60K$Chromosome==13 &
pig60K$Position<(signal+1000000)&pig60K$Position>(signal-1000000)]
> CMplot(pig60K, plot.type="m",LOG10=TRUE,col=c("grey30","grey60"),highlight=SNPs,
highlight.col="green",highlight.cex=1,highlight.pch=19,file="jpg",memo="",
dpi=300,file.output=TRUE,verbose=TRUE,width=14,height=6)
#Note:
highlight.col, highlight.cex, highlight.pch can be value or vecter, if its length equals to the length of highlighted SNPs,
each SNPs have its special colour, size and shape.
> CMplot(pig60K[pig60K$Chromosome==13, ], plot.type="m",LOG10=TRUE,col=c("grey60"),highlight=SNPs,
highlight.col="green",highlight.cex=1,highlight.pch=19,file="jpg",memo="",
threshold=c(1e-6,1e-4),threshold.lty=c(1,2),threshold.lwd=c(1,2), width=9,height=6,
threshold.col=c("red","blue"),amplify=FALSE,dpi=300,file.output=TRUE,verbose=TRUE)
> SNPs <- pig60K[pig60K[,5] < (0.05 / nrow(pig60K)), 1]
> genes <- paste("GENE", 1:length(SNPs), sep="_")
> set.seed(666666)
> CMplot(pig60K[,c(1:3,5)], plot.type="m",LOG10=TRUE,col=c("grey30","grey60"),highlight=SNPs,
highlight.col=c("red","blue","green"),highlight.cex=1,highlight.pch=c(15:17), highlight.text=genes,
highlight.text.col=c("red","blue","green"),threshold=0.05/nrow(pig60K),threshold.lty=2,
amplify=FALSE,file="jpg",memo="",dpi=300,file.output=TRUE,verbose=TRUE,width=14,height=6)
#Note:
the order of 'highlight.text' must be consistent with 'highlight'
highlight.text.cex: value or vecter, control the size of added text
highlight.text.font: value or vecter, control the font of added text
highlight.text.xadj: value or vecter, -1, 0, 1 limited, control the position of text around the highlighted SNPs,
-1(left), 0(center), 1(right)
highlight.text.yadj: value or vecter, same as above, -1(down), 0(center), 1(up)
> CMplot(cattle50K, plot.type="m", band=0.5, LOG10=FALSE, ylab="SNP effect",threshold=0.015,
threshold.lty=2, threshold.lwd=1, threshold.col="red", amplify=TRUE, width=14,height=6,
signal.col=NULL, chr.den.col=NULL, file="jpg",memo="",dpi=300,file.output=TRUE,
verbose=TRUE,cex=0.8)
#Note: if signal.col=NULL, the significant SNPs will be plotted with original colors.
> cattle50K[,4:ncol(cattle50K)] <- apply(cattle50K[,4:ncol(cattle50K)], 2,
function(x) x*sample(c(1,-1), length(x), rep=TRUE))
> CMplot(cattle50K, plot.type="m", band=0, LOG10=FALSE, ylab="SNP effect",ylim=c(-0.02,0.02),
threshold.lty=2, threshold.lwd=1, threshold.col="red", amplify=FALSE,cex=0.6,
chr.den.col=NULL, file="jpg",memo="",dpi=300,file.output=TRUE,verbose=TRUE)
#Note: Positive and negative values are acceptable.
> CMplot(pig60K, plot.type="m",pch=1:3,multracks=TRUE,threshold=c(1e-6,1e-4),threshold.lty=c(1,2),
threshold.lwd=c(1,1), threshold.col=c("black","grey"), amplify=TRUE,bin.size=1e6,
chr.den.col=c("darkgreen", "yellow", "red"), signal.col=c("red","green"),signal.cex=c(1,1),
file="jpg",memo="",dpi=300,file.output=TRUE,verbose=TRUE)
> CMplot(pig60K,plot.type="q",conf.int=TRUE,box=FALSE,file="jpg",memo="",dpi=300,
,file.output=TRUE,verbose=TRUE,width=5,height=5)
> pig60K$trait1[sample(1:nrow(pig60K), round(nrow(pig60K)*0.80))] <- NA
> pig60K$trait2[sample(1:nrow(pig60K), round(nrow(pig60K)*0.25))] <- NA
> CMplot(pig60K,plot.type="q",col=c("dodgerblue1", "olivedrab3", "darkgoldenrod1"),threshold=1e-6,
signal.pch=19,signal.cex=1.5,signal.col="red",conf.int=TRUE,box=FALSE,multracks=
TRUE,file="jpg",memo="",dpi=300,file.output=TRUE,verbose=TRUE,ylim=c(0,8),width=5,height=5)
Questions, suggestions, and bug reports are welcome and appreciated.
- Author: Lilin Yin
- Contact: [email protected]
- QQ group: 166305848
- Institution: Huazhong agricultural university