mergeLevels {aCGH}R Documentation

mergeLevels

Description

Merging of predicted levels for array CGH data and similar.

Usage

mergeLevels(vecObs,vecPred,pv.thres=0.0001,ansari.sign=0.05,thresMin=0.05,thresMax=0.5,verbose=1,scale=TRUE)

Arguments

vecObs Vector of observed values, i.e. observed log2-ratios
vecPred Vector of predicted values, i.e. mean or median of levels predicted by segmentation algorithm
pv.thres Significance threshold for Wilcoxon test for level merging
ansari.sign Significance threshold for Ansari-Bradley test
thresMin merge if segment medians are closer than thresMin , defaiult is 0.05
thresMax don't merge if segment medians are further than thresMax (unless needs to be merged for a different reason: wilcoxon test), default is .5
verbose if 1, progress is printed
scale whether thresholds are on the log2ratio scale and thus need to be converted to the copy number. default is TRUE

Details

mergeLevels takes a vector of observed log2-ratios and predicted log2ratios and merges levels that are not significantly distinct.

Value

vecMerged Vector with merged values. One merged value returned for each predicted/observed value
mnNow Merged level medians
sq Vector of thresholds, the function has searched through to find optimum. Note, these thresholds are based on copy number transformed values
ansari The p-values for the ansari-bradley tests for each threshold in sq

Note

vecObs and vecPred must have same length and observed and predicted value for a given probe should have same position in vecObs and vedPred. The function assumes that log2-ratios are supplied

Author(s)

Hanni Willenbrock (Hanni@cbs.dtu.dk) and Jane Fridlyand (jfridlyand@cc.ucsf.edu)

References

Willenbrock H, Fridlyand J. (2005). A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics. 2005 Sep 14; [Epub ahead of print]

Examples

# Example data of observed and predicted log2-ratios
vecObs <- c(rep(0,40),rep(0.6,15),rep(0,10),rep(-0.4,20),rep(0,15))+rnorm(100,sd=0.2)
vecPred <- c(rep(median(vecObs[1:40]),40),rep(median(vecObs[41:55]),15),
  rep(median(vecObs[56:65]),10),rep(median(vecObs[66:85]),20),rep(median(vecObs[86:100]),15))

# Plot observed values (black) and predicted values (red)
plot(vecObs,pch=20)
points(vecPred,col="red",pch=20)

# Run merge function
merge.obj <- mergeLevels(vecObs,vecPred)

# Add merged values to plot
points(merge.obj$vecMerged,col="blue",pch=20)

# Examine optimum threshold
merge.obj$sq

[Package aCGH version 1.16.0 Index]