computeSD.func: Function to estimate experimental variability of a sample
In aCGH: Classes and functions for Array Comparative Genomic Hybridization data

Description Usage Arguments Details Value Author(s) References See Also

This functions estimate experimental variability of a given sample. This value can be used to rank samples in terms of the quality as well as to derive thresholds for declaring gained and lost clones.

computeSD.Samples(aCGH.obj, maxChrom = 22, maxmadUse = .3,
                  maxmedUse = .5, maxState = 3, maxStateChange = 100,
				  minClone = 20)
computeSD.func(statesres, maxmadUse = 0.2, maxmedUse = 0.2,
               maxState = 3, maxStateChange = 100, minClone = 20,
               maxChrom = 22)

`aCGH.obj`	Object of class `aCGH`.
`statesres`	The states.hmm object, generally is the output of `mergeFunc`.
`maxmadUse`	Maximum median absolute deviation allowed to controbute to the overall variability calculation.
`maxmedUse`	Maximum median value for a state allowed to contribute to the calculation.
`maxState`	Maximum number of the states on a given chromosome for the states from that chromosome to be allowed to enter noise variability calculation.
`maxStateChange`	Maximum number of changes from state to state on a given chromosome for that chromosome to enter noise variability calculation.
`minClone`	Minimum number of clones in a state for clones in that sate to enter variability calculation.
`maxChrom`	Maxiumum chromosomal index (generally only autosomes are used for this calculation.

Median absolute deviation is estimated in all the states passing the criteria defined by the parameters of the function. Then median of all MADs on individual chromosomes as well as across all chromosomes is taken to estimate chromosomal experimental variability and sample experimental variability.

`madChrom`	Returns a matrix containing estimated variability for each chromosome for each sample.
`madGenome`	Returns a vector with estimate of experimental varibility for each sample.

Jane Fridlyand

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

aCGH

aCGH documentation built on Nov. 8, 2020, 6:58 p.m.