PolyFitPlot: Fit the mean-var relationship using polynomial regression

In wiscstatman/EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data

Fit the mean-var relationship using polynomial regression

Description

'PolyFitPlot' fits the mean-var relationship using polynomial regression.

Usage

PolyFitPlot(X, Y, nterms, xname = "Estimated Mean", 
	yname = "Estimated Var", pdfname = "", 
	xlim =  c(-1,5), ylim = c(-1,7), ChangeXY = F, 
	col = "red")

Arguments

X	The first group of values want to be fitted by the polynomial regression (e.g Mean of the data).
Y	The second group of values want to be fitted by the polynomial regression (e.g. variance of the data). The length of Y should be the same as the length of X.
nterms	How many polynomial terms want to be used.
xname	Name of the x axis.
yname	Name of the y axis.
pdfname	Name of the plot.
xlim	The x limits of the plot.
ylim	The y limits of the plot.
ChangeXY	If ChangeXY is setted to be TRUE, X will be treated as the dependent variable and Y will be treated as the independent one. Default is FALSE.
col	Color of the fitted line.

Value

The PolyFitPlot function provides a smooth scatter plot of two variables and their best fitting line of polynomial regression.

Author(s)

Ning Leng

References

Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013)

Examples

data(IsoList)
str(IsoList)
IsoMat = IsoList$IsoMat
IsoNames = IsoList$IsoNames
IsosGeneNames = IsoList$IsosGeneNames
IsoSizes = MedianNorm(IsoMat)
NgList = GetNg(IsoNames, IsosGeneNames)

IsoNgTrun = NgList$IsoformNgTrun
#IsoEBOut = EBTest(Data = IsoMat.small, 
#	NgVector = IsoNgTrun,
#	Conditions = as.factor(rep(c("C1","C2"), each=5)),
#	sizeFactors = IsoSizes, maxround = 5)

#par(mfrow=c(2,2))
#PolyFitValue = vector("list",3)

#for(i in 1:3)
#	PolyFitValue[[i]] = PolyFitPlot(IsoEBOut$C1Mean[[i]],
#		IsoEBOut$C1EstVar[[i]], 5)

#PolyAll = PolyFitPlot(unlist(IsoEBOut$C1Mean), 
#	unlist(IsoEBOut$C1EstVar), 5)

#lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]),
#	PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort], 
#	col="yellow", lwd=2)
#lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]),
#	PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort], 
#	col="pink", lwd=2)
#lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]),
#	PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort], 
#	col="green", lwd=2)

#legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"),
#	col = c("red","yellow","pink","green"), 
#	lty=1, lwd=3, box.lwd=2)

wiscstatman/EBSeq documentation built on June 3, 2023, 7:34 a.m.