samr.norm.data: output normalized sequencing data

In samr: SAM: Significance Analysis of Microarrays

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.

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

x	the normalized data matrix.

Author(s)

Jun Li and Balasubrimanian Narasimhan and Robert Tibshirani

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

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)

Example output

Loading required package: impute
Loading required package: matrixStats

samr documentation built on May 1, 2019, 7:49 p.m.