predictClassify: Extract predictions from 'classify()' object
In gokmenzararsiz/MLSeq: Machine Learning Interface for RNA-Seq Data

Description Usage Arguments Details Value Author(s) References See Also Examples

This function predicts the class labels of test data for a given model.

1	predictClassify(model, test.data, ...)

`model`	a model of `MLSeq` class
`test.data`	a `DESeqDataSet` instance of new observations.
`...`	further arguments to be passed to or from methods. These arguements are used in `predict.train` from caret package.

predictClassify function returns the predicted class information along with trained model. Predicted values are given either as class labels or estimated probabilities of each class for each sample. If type = "raw", as can be seen in the example below, the predictions are extracted as raw class labels.In order to extract estimated class probabilities, one should follow the steps below:

set classProbs = TRUE within control arguement in classify
set type = "prob" within predictClassify

MLSeqObject an MLSeq object returned from classify. See details.

Predictions a data frame or vector including either the predicted class probabilities or class labels of given test data.

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

Kuhn M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, (http://www.jstatsoft.org/v28/i05/)

Anders S. Huber W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11:R106

Witten DM. (2011). Classification and clustering of sequencing data using a poisson model. The Annals of Applied Statistics, 5(4), 2493:2518

Charity WL. et al. (2014) Voom: precision weights unlock linear model analysis tools for RNA-Seq read counts, Genome Biology, 15:R29, doi:10.1186/gb-2014-15-2-r29

Witten D. et al. (2010) Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls. BMC Biology, 8:58

Robinson MD, Oshlack A (2010). A scaling normalization method for differential expression analysis of RNA-Seq data. Genome Biology, 11:R25, doi:10.1186/gb-2010-11-3-r25

classify, train, trainControl

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 (20% test, 80% train).
nTest <- ceiling(n*0.2)
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")

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

# test set in S4
data.testS4 <- DESeqDataSetFromMatrix(countData = data.test,
                 colData = classts, formula(~ condition))
data.testS4 <- DESeq(data.testS4, fitType = "local")

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

# Random Forest (RF) Classification
rf <- classify(data = data.trainS4, method = "randomforest",
        transformation = "vst", ref = "T", normalize = "deseq",
        control = trainControl(method = "repeatedcv", number = 5,
                               repeats = 3, classProbs = TRUE))
rf

# predicted classes of test samples for CART method (class probabilities)
pred.cart = predictClassify(cart, data.testS4, type = "prob")
pred.cart

# predicted classes of test samples for RF method (class labels)
pred.rf = predictClassify(rf, data.testS4, type = "raw")
pred.rf