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\name{plotPCA-methods}
\docType{methods}
\alias{plotPCA}
\alias{plotPCA-methods}
\alias{plotPCA,matrix-method}
\alias{plotPCA,SeqExpressionSet-method}
\title{ Methods for Function \code{plotPCA} in Package \pkg{EDASeq} }
\description{
\code{plotPCA} produces a Principal Component Analysis (PCA) plot of the counts in \code{object}
}
\usage{
\S4method{plotPCA}{matrix}(object, k=2, labels=TRUE, isLog=FALSE, ...)
\S4method{plotPCA}{SeqExpressionSet}(object, k=2, labels=TRUE, ...)
}
\arguments{
\item{object}{Either a numeric matrix or a \code{\linkS4class{SeqExpressionSet}} object containing the gene expression.}
\item{k}{The number of principal components to be plotted.}
\item{labels}{Logical. If \code{TRUE}, and \code{k=2}, it plots the \code{colnames} of \code{object} as point labels.}
\item{isLog}{Logical. Set to \code{TRUE} if the data are already on the log scale.}
\item{...}{See \code{\link{par}}}
}
\details{
The Principal Component Analysis (PCA) plot is a useful diagnostic plot to highlight differences in the distribution of replicate samples, by projecting the samples into a lower dimensional space.
If there is strong differential expression between two classes, one expects the samples to cluster by class in the first few Principal Components (PCs) (usually 2 or 3 components are enough). This plot also highlights possible batch effects and/or outlying samples.
}
\section{Methods}{
\describe{
\item{\code{signature(x = "matrix")}}{
}
\item{\code{signature(x = "SeqExpressionSet")}}{
}
}}
\keyword{methods}
\examples{
library(yeastRNASeq)
data(geneLevelData)
mat <- as.matrix(geneLevelData)
data <- newSeqExpressionSet(mat,
phenoData=AnnotatedDataFrame(
data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
row.names=colnames(geneLevelData))))
plotPCA(data, col=rep(1:2, each=2))
}
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