findAmplif.func {aCGH}R Documentation

Function to determine high level amplifications

Description

This function identifies high level amplifications by considering the height, the width of an amplicon relative to the urrounding clones. Only narrow peaks much higher than its neigbors are considered as high level amplifications.

Usage

findAmplif.func(absValSingle = 1, absValRegion = 1.5, diffVal1 = 1,
diffVal2 = 0.5, maxSize = 10000, translen.matr = res3$translen.matrix,
trans.matr = res3$trans.matr, aber = res2$aber, outliers = res1$outlier,
pred = res1$pred.out, pred.obs = res1$pred.obs.out, statesres =
states.bic)

Arguments

absValSingle A clone is declared to be an amplification if it is a focal aberration or an outlier and its value exceeds absValSingle
absValRegion A clone is an amplification if if a clone belong to a region with width less than maxSize and observed value for a clones is greater than absValRegion
diffVal1 Clone is an amplification if it is an aberration and greater by diffVal1 than max of the two surrounding stretches
diffVal2 Clone is an amplification if it is an outlier, its observed values is greater by diffVal2 than max of the two surrounding stretches
maxSize The clones may not be declared as amplifications if they belong to the states with spanning more than maxSize
translen.matr State length matrix. The output of the findTrans.func
trans.matr Transition matrix. The output of the findTrans.func
aber Aberration matrix. The output of the findAber.func
outliers Outliers matrix. The output of the findOutliers.func
pred Predicted values matrix. The output of the findOutliers.func
pred.obs Predicted values matrix with observed values assigned to the outliers. The output of the findOutliers.func
statesres The states output of the hmm.run.func

Details

Note that all the distances are in Megabases and all the heights are on log2ratio scale.

Value

amplif.matrix Binary matrix with a row for each clone and column for each sample. "1" indicates amplification

...

Author(s)

Jane Fridlyand

References

Application of Hidden Markov Models to the analysis of the array CGH data, Fridlyand et.al., JMVA, 2004

See Also

aCGH


[Package aCGH version 1.1.4 Index]