samrocN {SAGx}R Documentation

Calculate ROC curve based SAM statistic

Description

Calculation of the regularised t-statistic which minimises the false positive and false negative rates.

Usage

samrocN(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")

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

Details

The test statistic is based on the one in Tusher et al (2001):

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 for the columns such that the coresponding contrast coefficient is not zero. This means that factors not tested are kept fixed. NB This may be adequate for testing a factor with two levels, but it is not adequate for all linear models.

samrocN calls the function Xprep which has been improved in terms of speed.

Value

A list with components

d the statistic for each probe set
diff The effect estimate, e.g. the mean difference between two groups
se the standard error
d0 the bootstrapped values on the test statistic
p0 the proportion unchanged genes
s0 the regularising constant
pvalues the p-values
N The optimal toplist size
errors the estimated sum of false positive and false negative rates when selcted the gene and all higher ranking ones

Author(s)

Per Broberg

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


[Package SAGx version 1.6.0 Index]