PCAdataplot {DRomics} | R Documentation |
Performs and plots the results of a PCA on omic data
Description
Provides a two dimensional plot (two first components) of a principal component analysis (PCA) performed on omic data after normalization and/or transformation, to check the promiximity of samples exposed to the same dose and optionally the presence/absence of a potential batch effect.
Usage
PCAdataplot(omicdata, batch, label)
Arguments
omicdata |
An object of class |
batch |
Optionnally a factor coding for a potential batch effect (factor of length the number of samples in the dataset). |
label |
Could be FALSE (default choice), TRUE or a character vector defining the sample names. In the two last cases, the points are replaced by labels of samples (so the batch cannot be identified by the shape of points, but may appear in the sample names. |
Value
a ggplot object.
Author(s)
Marie-Laure Delignette-Muller
Examples
# (1) on a microarray dataset
#
datafilename <- system.file("extdata", "transcripto_very_small_sample.txt",
package="DRomics")
o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess")
print(o)
plot(o)
PCAdataplot(o)
PCAdataplot(o, label = TRUE)
samplenames <- paste("sample", 1:ncol(o$data), sep = "")
PCAdataplot(o, label = samplenames)
# (2) an example on an RNAseq dataset with a potential batch effect
#
data(zebraf)
str(zebraf)
data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts,
dose = zebraf$dose)
o <- RNAseqdata(data4DRomics, transfo.method = "vst")
PCAdataplot(o, batch = zebraf$batch)
PCAdataplot(o, label = TRUE)