groupCNVs: Cluster segmentation scores into groups.

In CNVrd2: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data.

Description

Use the EM algorithm (Dempster et al., 1977) to cluster segmentation scores into various groups.

Usage

groupCNVs(Object, ...)

Arguments

Object	An object of class clusteringCNVs.
...	Further arguments.

Details

Users can set limits of segmentation scores: values being smaller than the left limit will be assigned to the smallest group and values being larger than righ limit will be assigned to the largest group.

Value

allGroups	Samples and their corresponding groups
means	Means of groups.
sigma	Variances of groups.
p	Proportions of groups in all data set.
loglk	Value of loglikehood function.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [e-mail]

References

Dempster, A. P., Laird, N. M., Rubin, D. B., 1977. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1-38.

See Also

emnormalCNV, searchGroupCNVs

Examples

data("fcgr3bMXL")
#resultSegment <- segmentSamples(Object = objectCNVrd2, stdCntMatrix = readCountMatrix)
objectCluster <- new("clusteringCNVs",
                     x = resultSegment$segmentationScores[, 1], k = 4, EV = TRUE)

#searchGroupCNVs(Object = objectCluster)
copynumberGroups <- groupCNVs(Object = objectCluster)

CNVrd2 documentation built on Nov. 8, 2020, 5:30 p.m.