PlotPCA {grandR} | R Documentation |
Make a PCA plot
Description
Make a PCA plot
Usage
PlotPCA(
data,
mode.slot = DefaultSlot(data),
ntop = 500,
aest = NULL,
x = 1,
y = 2,
columns = NULL,
do.vst = TRUE
)
Arguments
data |
the grandR object that contains the data to plot |
mode.slot |
the mode and slot of data to plot; slot in the grandr object (eg "count") |
ntop |
how many genes to use |
aest |
parameter to set the visual attributes |
x |
number of principal component to show on the x axis (numeric) |
y |
number of principal component to show on the y axis (numeric) |
columns |
which columns (i.e. samples or cells) to perform PCA on (see details) |
do.vst |
perform a variance stabilizing transformation for count data? |
Details
Columns can be given as a logical, integer or character vector representing a selection of the columns (samples or cells).
The expression is evaluated in an environment having the Coldata
, i.e. you can use names of Coldata
as variables to
conveniently build a logical vector (e.g., columns=Condition=="x").
Value
a PCA plot