EMA (version 1.4.3)

runSAM: SAM analysis with siggenes package

Description

Performs a Significance Analysis of Microarrays (SAM). It is possible to perform one and two class analyses using either a modified t-statistic or a (standardized) Wilcoxon rank statistic, and a multiclass analysis using a modified F-statistic. Moreover, this function provides a SAM procedure for categorical data such as SNP data and the possibility to employ an user-written score function.

Usage

runSAM(data, labels, nbpermut = 500, q = 0.05, plot = TRUE, method
="d.stat",var.equal = TRUE, include.zero = FALSE, paired = FALSE,
seed=123)

Arguments

data

A matrix, a data frame, or an ExpressionSet object. Each row of 'data' (or 'exprs(data)', respectively) must correspond to a gene, and each column to a sample.

labels

A vector of length 'ncol(data)' containing the class labels of the samples. In the two class unpaired case, 'labels' should be a vector containing 0's (specifying the samples of, e.g., the control group) and 1's (specifying, e.g., the case

nbpermut

A numeric value specifying the number of permutation.

q

A numeric value specifying the FDR threshold. see details.

plot

A logical value specifying if drawing plots or not.

method

A character string or a name specifying the method/function that should be used in the computation of the expression scores d.

If 'method = d.stat', a modified t-statistic or F-statistic, respectively, will be com

var.equal

A logical value. If 'method=d.stat', TRUE for student test , FALSE for Welch test.

include.zero

A numeric value specifying if sO=0 is possible.

paired

A logical value specifying if paired test or not.

seed

Seed initialization for results reproducibility.

Value

• A matrix with the probes ID, the statistics, the raw p-values, and the significance (according to SAM FDR procedure).

Details

SAM has it own FDR procedure which allows to find significant genes for a fixed threshold 'q'. The genes' signicance found by SAM is not based on the adjusted pvalues (qvalues). That's why we do not report them.

References

Schwender, H., Krause, A. and Ickstadt, K. (2003). Comparison of the Empirical Bayes and the Significance Analysis of Microarrays. _Technical Report_, SFB 475, University of Dortmund, Germany. Schwender, H. (2004). Modifying Microarray Analysis Methods for Categorical Data - SAM and PAM for SNPs. To appear in: _Proceedings of the the 28th Annual Conference of the GfKl_. Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. _PNAS_, 98, 5116-5121.

See Also

sam

Examples

## load data
data(marty)

## filtering data
marty <- expFilter(marty, threshold=3.5, graph=FALSE)

##Class label 0/1
marty.type.num <- ifelse(marty.type.cl=="Her2+",0,1)

## run sam analysis on example set
example.subset <- marty[1:100,]
samOUT <- runSAM(example.subset, marty.type.num, nbpermut=50, q=0.05, plot=TRUE)
samSIGN <- samOUT[which(samOUT[,"Significant"]),]