segN {aCGH.Spline} | R Documentation |
Segmentation method for noise calculation.
segN(r, t)
r |
- vector of ratio values. |
t |
- value between 0 and 1 (a percentile). |
This method assesses the difference between consecutive points and median normalises segments above the threshold (t).
A ratio which represents the noise of the original ratio(r) as if it were more normal.
This method is useful when looking at highly aneuploid data (e.g. cancer samples). It can be used in an iterative manner if required.
It is important to make sure the data is sorted by genomic position before running this.
Tomas William Fitzgerald
v = seq(1,100000,0.5) d = sin(2*pi/500 * v) red = d + rnorm(length(d),0,100) + 1000 dd = sin(2*pi/1000 * v) green = dd + rnorm(length(dd),0,120) + 1000 rat = log2(red / green) - median(log2(red / green), na.rm=TRUE) rat[20000:30000] = abs(rat[20000:30000] * 2) rat[60000:70000] = -abs(rat[60000:70000] * 2) seg = segN(rat,0.50) par(mfrow=c(2,1)) plot(rat, pch=46, ylim=c(-2,2), main="Before_segN") plot(seg, pch=46, ylim=c(-2,2), main="After_segN")