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?

Value

a PCA plot

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").