| MergeLevels {unknown} | R Documentation |
Merging of predicted levels for array CGH data and similar.
MergeLevels(vecObs, vecPred, pv.thres = 1e-04, ansari.sign = 0.05)
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 |
MergeLevels takes a vector of observed log2-ratios and predicted log2ratios and merges levels that are not significantly distinct.
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 |
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
Hanni Willenbrock (Hanni@cbs.dtu.dk) and Jane Fridlyand (jfridlyand@cc.ucsf.edu)
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]
# 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