samr.estimate.depth: estimate the sequencing depth

In samr: SAM: Significance Analysis of Microarrays

Description

Estimate the sequencing depth of each experiment for sequencing data.

Usage

samr.estimate.depth(x)

Arguments

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

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

depth

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

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

Example output

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