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]