preProcessing: Accessors for the 'preProcessing' slot of an 'MLSeq' object

Description Usage Arguments See Also Examples

Description

MLSeq package benefits from DESeqDataSet structure from bioconductor package DESeq2 for storing gene expression data in a comprehensive structure. This object is used as an input for classification task through classify. The input is stored in inputObject slot of MLSeq object.

Usage

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preProcessing(object)

preProcessing(object) <- value

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

## S4 replacement method for signature 'MLSeq,character'
preProcessing(object) <- value

Arguments

object

an MLSeq object.

value

a character string. Which preProcessing should be replaced with current one?

See Also

classify, DESeqDataSet

Examples

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## Not run: 
library(DESeq2)
data(cervical)

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

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

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

# number of samples for test set (30% test, 70% train).
nTest <- ceiling(n*0.3)
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(~ 1))

## Number of repeats (repeats) might change model accuracies ##
# Classification and Regression Tree (CART) Classification
cart <- classify(data = data.trainS4, method = "rpart",
          ref = "T", preProcessing = "deseq-vst",
          control = trainControl(method = "repeatedcv", number = 5,
                                 repeats = 3, classProbs = TRUE))

preProcessing(cart)

## End(Not run)

MLSeq documentation built on Nov. 8, 2020, 5:37 p.m.