subPCA: PCA plot with class labels.

In peterawe/CMScaller: CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models

Description

Principal component analysis (PCA) plot with class labels.

Usage

subPCA(
  emat,
  class = NULL,
  keepN = TRUE,
  nGenes = 1000,
  normMethod = NULL,
  dim = c(1, 2),
  labelSamp = FALSE,
  legend = "topright",
  classConfusion = NULL,
  classCol = getOption("subClassCol"),
  ...
)

Arguments

emat	numeric matrix with row features and sample columns.
class	a factor vector specifying sample classes length(class)==ncol(emat).
keepN	a logical or numeric vector specifying which samples to keep.
nGenes	an integer specifying number of features to used for PCA.
normMethod	a character, passed to calcNormFactors if element in c("TMM","RLE", "upperquartile","none") or voom ("scale", "quantile", "cyclicloess").#'
dim	a numeric vector of length 2, specifying which principal components to plot.
labelSamp	a logical indicating whether points or colnames(emat) are plotted.
legend	a character specifying legend placement (e.g. "topleft", "bottomright").
classConfusion	a factor vector with same length and levels as class specifying alternative classifications.
classCol	a character vector of hexcolors with length(classCol)>=levels(class).
...	arguments to be passed to plot.

Details

plotPCA provides a PCA with points colored according to class labels. If classConfusion is specified, samples where class != classConfusion are highlighted.

Value

a labeled PCA plot and list of class prcomp passed from prcomp

See Also

prcomp, subMDS

peterawe/CMScaller documentation built on June 13, 2020, 4:49 a.m.