Description Usage Arguments Value Author(s) References See Also Examples
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
.
1 | rankVariables(x, pop, cnv.blocks = NULL, proj = TRUE, ...)
|
x |
object of class |
pop |
|
cnv.blocks |
as obtained by the attribute "cnv.blocks" from the result of
|
proj |
whether projection on centroid direction or principal axes should be used for the ranking. |
... |
additional parameters passed to |
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.
Alejandro Cacereres
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].
1 2 3 4 5 6 7 8 9 10 11 12 | ## 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.