Accessors for the 'method' slot of an MLSeq object

Description

Used classification method for the trained model using classify function.

Usage

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

Arguments

Details

method slot stores the name of the classification method as "svm", support vector machines using radial-based kernel function; "bagsvm", support vector machines with bagging ensemble; "randomForest", random forest algorithm and "cart", classification and regression trees algorithm.

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")

method(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/method.Rd_%03d_medium.png", width=480, height=480)
> ### Name: method-methods
> ### Title: Accessors for the 'method' slot of an MLSeq object
> ### Aliases: method method,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 12 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
> 
> method(rf)
[1] "randomforest"
> 	
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>