Accessors for the 'normalization' slot of an MLSeq object

Description

Used normalization method for the trained model using classify function.

Usage

  ## S4 method for signature 'MLSeq'
normalization(object)

Arguments

Details

normalization slot stores the name of the normalization method "deseq", "none" or "tmm"

Author(s)

Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver, Ahmet Ozturk

Examples

data(cervical)

data = cervical[c(1:150),]  # a subset of cervical data with first 150 features.

class = data.frame(condition=factor(rep(c("N","T"),c(29,29))))# defining sample classes.

n = ncol(data)  # number of samples
p = nrow(data)  # number of features

nTest = ceiling(n*0.2)  # number of samples for test set (20% test, 80% train).
ind = sample(n,nTest,FALSE)

# train set
data.train = data[,-ind]
data.train = as.matrix(data.train + 1)
classtr = data.frame(condition=class[-ind,])

# train set in S4 class
data.trainS4 = DESeqDataSetFromMatrix(countData = data.train,
colData = classtr, formula(~ condition))
data.trainS4 = DESeq(data.trainS4, fitType="local")

# Random Forest (RF) Classification
rf = classify(data = data.trainS4, method = "randomforest", normalize = "deseq", deseqTransform = "vst", cv = 5, rpt = 3, ref="T")

normalization(rf)	
		

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(MLSeq)
Loading required package: caret
Loading required package: lattice
Loading required package: ggplot2
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit


Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:DESeq2':

    plotMA

The following object is masked from 'package:BiocGenerics':

    plotMA

Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.

Attaching package: 'randomForest'

The following object is masked from 'package:Biobase':

    combine

The following object is masked from 'package:BiocGenerics':

    combine

The following object is masked from 'package:ggplot2':

    margin

Loading required package: edgeR
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MLSeq/normalization.Rd_%03d_medium.png", width=480, height=480)
> ### Name: normalization-methods
> ### Title: Accessors for the 'normalization' slot of an MLSeq object
> ### Aliases: normalization normalization,MLSeq-method
> 
> ### ** Examples
> 
> data(cervical)
> 
> data = cervical[c(1:150),]  # a subset of cervical data with first 150 features.
> 
> class = data.frame(condition=factor(rep(c("N","T"),c(29,29))))# defining sample classes.
> 
> n = ncol(data)  # number of samples
> p = nrow(data)  # number of features
> 
> nTest = ceiling(n*0.2)  # number of samples for test set (20% test, 80% train).
> ind = sample(n,nTest,FALSE)
> 
> # train set
> data.train = data[,-ind]
> data.train = as.matrix(data.train + 1)
> classtr = data.frame(condition=class[-ind,])
> 
> # train set in S4 class
> data.trainS4 = DESeqDataSetFromMatrix(countData = data.train,
+ colData = classtr, formula(~ condition))
converting counts to integer mode
> data.trainS4 = DESeq(data.trainS4, fitType="local")
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 9 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
> 
> # Random Forest (RF) Classification
> rf = classify(data = data.trainS4, method = "randomforest", normalize = "deseq", deseqTransform = "vst", cv = 5, rpt = 3, ref="T")
found already estimated dispersions, replacing these
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
> 
> normalization(rf)	
[1] "deseq"
> 		
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>