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\name{samr.norm.data}
\alias{samr.norm.data}
\title{output normalized sequencing data}
\description{
Output a normalized sequencing data matrix from the original count matrix.
}
\usage{
samr.norm.data(x, depth=NULL)}
\arguments{
\item{x}{the original count matrix. p by n matrix of features, one observation per column.}
\item{depth}{sequencing depth of each experiment. a vector of length n.
This function will estimate the sequencing depth if it is not specified.}
}
\details{normalize the data matrix so that each number looks roughly like
Gaussian distributed and each experiment has the same sequencing depth.
To do this, we first use Anscombe transformation to stablize the variance
and makes each number look like Gaussian,
and then divide each experiment by the square root of the sequencing depth.}
\value{
\item{x}{the normalized data matrix.}
}
\references{Tusher, V., Tibshirani, R. and Chu, G. (2001):
Significance analysis of microarrays applied to the ionizing radiation response PNAS 2001 98: 5116-5121, (Apr 24).
http://www-stat.stanford.edu/~tibs/SAM}
\author{Jun Li and Balasubrimanian Narasimhan and Robert Tibshirani}
\examples{
set.seed(100)
mu <- matrix(100, 1000, 20)
mu[1:100, 11:20] <- 200
mu <- scale(mu, center=FALSE, scale=runif(20, 0.5, 1.5))
x <- matrix(rpois(length(mu), mu), 1000, 20)
y <- c(rep(1, 10), rep(2, 10))
data=list(x=x,y=y, geneid=as.character(1:nrow(x)),
genenames=paste("g",as.character(1:nrow(x)),sep=""))
x.norm <- samr.norm.data(data$x)
}
\keyword{univar}% at least one, from doc/KEYWORDS
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