MAPlot2 {aCGH.Spline}R Documentation

Produce a MAplot showing points fitted vs. points interpolated.

Description

Produce a MA-plot showing points fitted and point interpolated.

Usage

MAPlot2(x, raw, ntyp="percentile", p=0.68, fact=4.5, segN=FALSE, sn=0.75, pch=46, Fitcol="green", Intcol="red", ylim=c(-5,5), xlim=c(0,20), xlab="log2(mean(cy5, cy3))", ylab="log2(cy5/cy3)", main="MA-plot")

Arguments

x - the ".temp" formatted data structure after normalisation.
raw - the".temp" formatted data structure before normalisation.
ntyp - the type of noise calculation to use, c("percentile", "derivative", "combined"), defaults to "percentile".
p - numeric value between 0 and 1, the quantile to use.
fact - numeric value, the factor by which the noise estimation will be rised.
segN - use segmentation prior to noise estimation TRUE or FALSE (see segN).
sn - the segmentation threshold.
pch - the point character to use defaults to 46.
Fitcol - the point color to use for the points used during spline fitting, defaults to "green".
Intcol - the point color to use for the points excluded from spline fitting, defaults to "red".
xlim - the x-axis limits defaults to c(0,20).
ylim - the y-axis limits defaults to c(-5,5).
xlab - the label for the x-axis.
ylab - the label for the y-axis.
main - the main title for the plot.

Value

MAplot showing relative number of points considered to be outside the normal distribution.

Note

Points outside the interpolation threshold are not used during the spline fitting. We believe that these points are highly unrealiable for assessing the dye bias.

Author(s)

Tomas William Fitzgerald

Examples

# Create some noisy data with a bias
v = seq(1,100000,0.5)
d = sin(2*pi/500 * v) 
nd = d  +  rnorm(length(d),0,100) + 1000
dd = sin(2*pi/1000 * v) 
ndd = dd  +  rnorm(length(dd),0,120) + 1000

## Create ".temp" data stucture
mat = matrix(ncol=8,nrow=length(dd))
mat[,1] = log2(nd/ndd)
mat[,2] = nd
mat[,3] = ndd
mat[,4] = 0
mat[,5] = 0
mat[,6] = 0
mat[,7] = 0
mat[,8] = 0

# Add a few outliers
mat[100:150,1] = mat[100:150,1] * 1.5
mat[100:150,2] = mat[100:150,2] / 1.5

# Run Jspline and plot
aa = Jspline(mat)

MAPlot2(aa, mat, ylim=c(-1.5,1.5),xlim=c(9.4,10.4))


[Package aCGH.Spline version 2.2 Index]