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

In dncR/MLSeq: Machine Learning Interface for RNA-Seq Data

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

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

## 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)

dncR/MLSeq documentation built on May 17, 2020, 6:45 p.m.