samr.estimate.depth: estimate the sequencing depth
In bnaras/samr: SAM: Significance Analysis of Microarrays

Description Usage Arguments Details Value Author(s) References Examples

Estimate the sequencing depth of each experiment for sequencing data.

1	samr.estimate.depth(x)

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

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.

depth

sequencing depth of each experiment. a vector of length n.

Jun Li and Balasubrimanian Narasimhan and Robert Tibshirani

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

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=""))
depth <- samr.estimate.depth(data$x)