sam.wilc {siggenes}R Documentation

SAM Analysis using Wilcoxon Rank Sums

Description

Performs a Significance Analysis of Microarrays for a set of integer thresholds Delta. Instead of the modified t statistics, it uses Wilcoxon Rank Sums.

Usage

    sam.wilc(data,cl,delta=1:max(abs(W.diff)),na.rm=FALSE,zero.rand=TRUE,
        rand=NA,graphic=TRUE,thres=round(quantile(2:max(abs(W.diff)),(0:3)/3)),
        use.numbers=TRUE)

Arguments

data the data set that should be analyzed. Every row of this data set must correspond to a gene.
cl a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). In the two class paired case, the labels are the integers between 1 and n/2 (e.g., before treatment group) and between -1 and -n/2 (e.g., after treatment group), where n is the length of cl and k is paired with -k.
delta a vector of integer for which the SAM-Wilc analysis should be performed.
na.rm if FALSE (default), the expression scores W of genes with one or more missing values will be set to NA. If TRUE, the missing values will be replaced by the genewise mean of the non-missing values.
zero.rand if TRUE (default), the sign of each Zero in the calculation of the Wilcoxon signed rank score will be randomly assigned. If FALSE, the sign of the Zeroes will be set to '–'.
rand if specified (i.e. not NA), the random number generator will be put in a reproducible state.
graphic if TRUE (default), both the SAM plot and the plots of Delta vs. FDR and Delta vs. number of significant genes are generated. To avoid this plotting, set graphic=FALSE.
thres a vector of integer values for Delta for which two lines parallel to the 45-degree line are generated.
use.numbers if TRUE (default), the symbol for each point in the SAM Plot of a SAM-Wilc analysis will be the number of observations that correspond to this point.

Value

a table of statistics (estimate for p0, number of significant genes, number of falsely called genes and FDR) for the specified set of Deltas, a SAM Plot, a Delta vs. FDR plot, and a plot of Delta vs. the number of significant genes.

Note

For further analyses with sam.plot, the results of sam.wilc must be assigned to an object.

SAM was developed by Tusher et al. (2001).

!!! There is a patent pending for the SAM technology at Stanford University. !!!

Author(s)

Holger Schwender, holger.schw@gmx.de

References

Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, PNAS, 98, 5116-5121.

Schwender, H. (2003). Assessing the False Discovery Rate in a Statistical Analysis of Gene Expression Data, Chapter 6, Diploma thesis, Department of Statistics, University of Dortmund, http://de.geocities.com/holgerschw/thesis.pdf.

See Also

sam.plot sam

Examples

    library(multtest)
    # Load the data of Golub et al. (1999). data(golub) contains a 3051x38 gene expression
    # matrix called golub, a vector of length called golub.cl that consists of the 38 class labels,
    # and a matrix called golub.gnames whose third column contains the gene names.
    data(golub)
    
    # Performing a SAM-Wilc Analysis of the Golub data. Setting rand=123 makes the results reproducible.
    # The output is assigned to an object for further analyses.
    if (interactive()) {
    sam.output<-sam.wilc(golub,golub.cl,rand=123)
}

[Package siggenes version 1.0.6 Index]