selectGene-methods: Gene selection using SPLS model

Description Usage Arguments Examples

Description

Sparse Partial Least Square (SPLS) step for gene selection and dimension reduction.By applying SPLS to each pathway, we achieve the goal of gene selection and dimension reduction at the same time.

Usage

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## S4 method for signature 'Prefiltered'
selectGene(object, K = 5, etas = seq(0.1, 0.9,
  0.1), fold = 5, se1 = TRUE, method = "plik", par = FALSE,
  foldid = NULL, seed = 123)

Arguments

object

output list of prefilter step

K

the maximum number of hidden features in spls.

etas

Thresholding parameter. eta should be between 0 and 1.

fold

The number of folds to use to perform the cross-validation process.

se1

one standard error criteria, default = TRUE

method

if auc is used, this is the maximum auc criteria, if partial likelihood(plik) is used, this is the min deviance criteria. Default = TRUE

par

parallel option for running the code, default = FALSE

foldid

the number of folds to use to perform the cross-validation process.

seed

random seed that was set, default = 123.

Examples

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data(TCGA)
geneRegroup.result=geneRegroup(plist=TCGA$pathList )
prefilter.results=prefilter( data=TCGA$geneexpr, time=TCGA$t, status=TCGA$d,
                             plist=geneRegroup.result@gset )
gene.results=selectGene( object=prefilter.results, fold=5, K=5, etas=c(0.1,0.9),seed=123)

sunny-zq/INGRID documentation built on Oct. 15, 2019, 6:45 p.m.