46 lines
2.4 KiB
R
46 lines
2.4 KiB
R
#########################################################################
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# this script generates a plot of sample depth of coverage over the MHC.
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# It's rather specific to that use case, but is a good example of getting
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# Loess curve generation to work given a X/Y dataset.
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#
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# 12/9/2009
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# -Aaron
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#########################################################################
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# setup our output PNG
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png(filename="bySampleJPName.png",width=1500,height=700,bg="white")
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# input our data set
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tbl <- read.csv("docOutJP.csv",header=TRUE) # doc_JP_SN_totalled_clean.csv
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par(las=1) # make all labels horizontal
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par(xpd=T, mar=par()$mar+c(0,0,-2,4)) # adjust the margins to accommodate our legend
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# do the initial plot of one column of data
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plot(tbl[,1],tbl[,5],xlim=c(18517983,41461957),ylim=c(0,7),type="p",cex=0.2,axes=F,ylab="Average Read Depth Of Coverage",xlab="MHC Location",col=rgb(0,0,0,0.1))
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# add the custom x and y axis, so we can control their layout
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axis(1,pos=0,at=seq(18517983,42061957,by=500000),col.axis="black")
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axis(2,pos=18517983,at=seq(0,7,by=1),col="black")
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# setup two color schemes, both with the same colors. One has an alpha of 0.08 for the background points,
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# and the other is alpha=1 for the lines (which we want to be vibrant in the foreground)
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myColors <- rainbow(30,alpha=0.08)
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myColors2 <- rainbow(30)
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# add a legend. There is a better way to do this besides hard-coding it, but it wouldn't render correctly on my machine
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legend(x=41000000,y=5,c("NA18940","NA18942","NA18943","NA18944","NA18945","NA18947","NA18948","NA18949","NA18951","NA18952","NA18953","NA18956","NA18959","NA18960","NA18961","NA18964","NA18965","NA18967","NA18968","NA18969","NA18970","NA18971","NA18972","NA18973","NA18974","NA18975","NA18976","NA18980","NA18981","NA19005"),horiz=FALSE,lty=c(1),col=c(myColors2),cex=0.8)
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# loop over the remaining data sets, adding first the points to the graph, then calculating the loess points, and finally combining the points into a line
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# the loess smoothing parts were inspired by: http://research.stowers-institute.org/efg/R/Statistics/loess.htm
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# adjust the span value to adjust the sensitivity of curve to the local fit.
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for (i in 4:33) {
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points(tbl[,1],tbl[,i],col=myColors[i],cex=0.2)
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y.loess <- loess(y ~ x, span=0.05, data.frame(x=tbl[,1], y=tbl[,i]))
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y.predict <- predict(y.loess, data.frame(x=tbl[,1]))
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lines(tbl[,1],y.predict,col=myColors2[i])
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}
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# close our png
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dev.off()
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