SAGx (version 1.46.0)

samrocNboot: Calculate ROC curve based SAM statistic

Description

A c-code version of samrocN. Calculation of the regularised t-statistic which minimises the false positive and false negative rates.

Usage

samrocNboot(data=M,formula=~as.factor(g), contrast=c(0,1), N = c(50, 100, 200, 300), B=100, perc = 0.6, 
smooth=FALSE, w = 1, measure = "euclid", probeset = NULL)

Arguments

data

The data matrix

formula

a linear model formula

contrast

the contrast to be estimnated

N

the size of top lists under consideration

B

the number of bootstrap iterations

perc

the largest eligible percentile of SE to be used as fudge factor

smooth

if TRUE, the std will be estimated as a smooth function of expression level

w

the relative weight of false positives

measure

the goodness criterion

probeset

probeset ids;if NULL then "probeset 1", "probeset 2", ... are used.

Value

• An object of class samroc.result.

Details

The test statistic is based on the one in Tusher et al (2001): $$\frac{d = diff}{s_0+s}$$ where $diff$ is a the estimate of a constrast, $s_0$ is the regularizing constant and $s$ the standard error. At the heart of the method lies an estimate of the false negative and false positive rates. The test is calibrated so that these are minimised. For calculation of $p$-values a bootstrap procedure is invoked. Further details are given in Broberg (2003). The p-values are calculated through permuting the rows of the design matrix. NB This is not adequate for all linear models. samrocNboot uses C-code to speed up the bootstrap loop.

References

Tusher, V.G., Tibshirani, R., and Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. PNAS Vol. 98, no.9, pp. 5116-5121 Broberg, P. (2002) Ranking genes with respect to differential expression , http://genomebiology.com/2002/3/9/preprint/0007 Broberg. P: Statistical methods for ranking differentially expressed genes. Genome Biology 2003, 4:R41 http://genomebiology.com/2003/4/6/R41

Examples

library(multtest)
#Loading required package: genefilter 
#Loading required package: survival 
#Loading required package: splines 
#Loading required package: reposTools 
data(golub)
 # This makes the expression data from Golub et al available
 # in the matrix golub, and the sample labels in the vector golub.cl
set.seed(849867)
samroc.res <- samrocNboot(data = golub, formula = ~as.factor(golub.cl))
# The proportion of unchanged genes is estimated at
samroc.res@p0
# The fudge factor equals
 samroc.res@s0
# A histogram of p-values
 hist(samroc.res@pvalues)
 # many genes appear changed