samrocN: 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", p0 = NULL, probeset = NULL)

Arguments

data

The data matrix, or ExpressionSet

formula

a linear model formula

contrast

the contrast to be estimnated

the size of top lists under consideration

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

the relative weight of false positives

measure

the goodness criterion

the proportion unchanged probesets; if NULL p0 will be estimated

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). Note that the definition of p-values follows that in Davison and Hinkley (1997), in order to avoid p-values that equal zero. The p-values are calculated through permuting the residuals obtained from the null model, assuming that this corresponds to the full model except for the parameter being tested, coresponding to the contrast coefficient not equal to zero. This means that factors not tested are kept fixed. NB This may be adequate for testing a factor with two levels or a regression coefficient (correlation), but it is not adequate for all linear models.

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 Davison A.C. and Hinkley D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press