Description Usage Arguments Author(s) Examples
This slot stores the information about reference category. Confusion matrix and related statistics are calculated using the user-defined reference category.
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object |
an |
Gokmen Zararsiz
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | 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")
# Classification and Regression Trees (CART)
cart <- classify(data = data.trainS4, method = "cart",
transformation = "vst", ref = "T", normalize = "deseq",
control = trainControl(method = "repeatedcv", number = 5,
repeats = 3, classProbs = TRUE))
ref(cart)
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