plotPCA(object, groups = NULL, groupnames = NULL, addtext = NULL, x.coord = NULL, y.coord = NULL, screeplot = FALSE, squarepca = FALSE, pch = NULL, col = NULL, pcs = c(1, 2), legend = TRUE, main = "Principal Components Plot", plot3d = FALSE, outside = FALSE, ...)ExpressionSet, matrix or prcomp
object.vector delineating group membership for
samples. Default is NULL, in which case default plotting symbols and
colors will be used.vector describing the different groups.
Default is NULL, in which case the sample names will be used.vector of additional text to be placed
just above the plotting symbol for each sample. This is helpful if there are
a lot of samples for identifying e.g., outliers.screeplot instead of a
PCA plot? Defaults to FALSE.FALSE.vector indicating what plotting symbols to use.
Default is NULL, in which case default plotting symbols will be used.
Note that this argument will override the 'groups' argument.vector indicating what color(s) to
use for the plotting symbols. Default is NULL in which case default
colors will be used. Note that this argument will override the 'groups'
argument.vector of length two (or three if plot3d is
TRUE), indicating which principal components to plot. Defaults to the
first two principal components.TRUE.vector for the plot title.TRUE, then the PCA plot will be rendered in
3D using the rgl package. Defaults to FALSE. Note that the pcs
argument should have a length of three in this case.TRUE the legend will be placed outside the
plotting region, at the top right of the plot.plot. See the help
page for plot for further information.
library("affy")
data(sample.ExpressionSet)
plotPCA(sample.ExpressionSet, groups =
as.numeric(pData(sample.ExpressionSet)[,2]), groupnames =
levels(pData(sample.ExpressionSet)[,2]))
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