samr (version 3.0)

samr.norm.data: output normalized sequencing data

Description

Output a normalized sequencing data matrix from the original count matrix.

Usage

samr.norm.data(x, depth=NULL)

Arguments

x

the original count matrix. p by n matrix of features, one observation per column.

depth

sequencing depth of each experiment. a vector of length n. This function will estimate the sequencing depth if it is not specified.

Value

x

the normalized data matrix.

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.

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

Examples

# NOT RUN {
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)
# }

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