# (1) import, check, normalization and transformation of RNAseq data
# An example on a subsample of a data set published by Zhou et al. 2017
# Effect on mouse kidney transcriptomes of tetrachloroethylene
# (see ? Zhou for details)
#
datafilename <- system.file("extdata", "RNAseq_sample.txt", package="DRomics")
(o <- RNAseqdata(datafilename, check = TRUE, transfo.method = "vst"))
plot(o)
# If you want to use your own data set just replace datafilename,
# the first argument of RNAseqdata(),
# by the name of your data file (e.g. "mydata.txt")
#
# You should take care that the field separator of this data file is one
# of the default field separators recognised by the read.table() function
# when it is used with its default field separator (sep argument)
# Tabs are recommended.
# Use of an R object of class data.frame
# below the same example taking a subsample of the data set
# Zhou_kidney_pce (see ?Zhou for details)
data(Zhou_kidney_pce)
subsample <- Zhou_kidney_pce[1:1000, ]
(o <- RNAseqdata(subsample, check = TRUE, transfo.method = "vst"))
plot(o)
PCAdataplot(o)
# (2) transformation with two methods on the whole data set
# \donttest{
data(Zhou_kidney_pce)
# variance stabilizing tranformation
(o1 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "vst"))
plot(o1)
# regularized logarithm
(o2 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "rlog"))
plot(o2)
# variance stabilizing tranformation (blind to the experimental design)
(o3 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "vst",
transfo.blind = TRUE))
plot(o3)
# regularized logarithm
(o4 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "rlog",
transfo.blind = TRUE))
plot(o4)
# }
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