rankVariables: CNV ranking

In gada: Genome Alteration Detection Algorithm (GADA)

Description

Ranks each CNV block according to their highest correlation across population centroids. Hence, it assigns the population that the variable discriminate the best, according to the discriminant analysis performed by discriminCNV.

Usage

rankVariables(x, pop, cnv.blocks = NULL, proj = TRUE, ...)

Arguments

x	object of class cnv.discrimin or reduced matrix as obtained by reduceMatrix or getReducedData.
pop	factors vector with population labeling of each subject.
cnv.blocks	as obtained by the attribute "cnv.blocks" from the result of reduceMatrix or getReducedData.
proj	whether projection on centroid direction or principal axes should be used for the ranking.
...	additional parameters passed to discriminCNV

Value

rankVariables produces a data.frame in which each CNV block is listed with the number of CNV is composed of, the initial and final genomic possition, the chromosome it belongs to, and the correlation to the axes defined by its most prominent population.

Author(s)

Alejandro Cacereres

References

Caceres A, Basaga~na X, Gonzalez JR: Multiple Correspondence Discriminant Analysis: An Application to Detect Stratication in Copy Number Variation 2009. [ISCB Prague, Abstract: S05.03, submitted to the special issue of Statistics in Medicine].

See Also

discriminCNV

Examples

## Not run: 

# 'HapMap270reducedData.RData' file can be downloaded from ...

load("HapMap270reducedData.RData")

cp<-discrimin.cnv(mat.f,pop.cla)

var.proj<-rank.variables(cp,cnv.blocks=cnv.blocks)
var.proj[1:10,]

## End(Not run)

gada documentation built on May 2, 2019, 6:10 p.m.