update: Update 'MLSeq' objects returnd from 'classify()'

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

Description

This function updates the MLSeq object. If one of the options is changed inside MLSeq object, it should be updated to pass its effecs into classification results.

Usage

## S3 method for class 'MLSeq'
update(object, ..., env = .GlobalEnv)

## S4 method for signature 'MLSeq'
update(object, ..., env = .GlobalEnv)

Arguments

object	a model of MLSeq class returned by classify
...	optional arguements passed to classify function.
env	an environment. Define the environment where the trained model is stored.

Value

same object as an MLSeq object returned from classify.

Note

When an MLSeq object is updated, new results are updated on the given object. The results before update process are lost when update is done. To keep the results before update, one should copy the MLSeq object to a new object in global environment.

See Also

classify

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))

# test set
data.test <- data[ ,ind]
data.test <- as.matrix(data.test + 1)
classts <- data.frame(condition=class[ind, ])

data.testS4 <- DESeqDataSetFromMatrix(countData = data.test,
                                      colData = classts, 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))
cart

# Change classification model into "Random Forests" (rf)
method(cart) <- "rf"
rf <- update(cart)

rf

## End(Not run)

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