sam.delta {siggenes} | R Documentation |
If the output of a previous analysis with sam
was assigned to a vector, this function
can be used to compute the number of significant genes and the FDR for values of the threshold Delta
that was not used in the previous analysis.
sam.delta(sam.out,delta)
sam.out |
the object to which the output of a previous analysis with sam was assigned. |
delta |
vector of values for the threshold Delta. |
a table of statistics (estimate for p0, number of significant genes, number of falsely called genes and FDR) for the specified set of Deltas.
This function can only be used for a SAM analysis – not for a SAM.Wilc analysis.
SAM was introduced by Tusher et al. (2001).
!!! There is a patent pending for the SAM technology at Stanford University. !!!
Holger Schwender, holger.schw@gmx.de
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 5, Diploma thesis, Department of Statistics, University of Dortmund, http://de.geocities.com/holgerschw/thesis.pdf.
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 Analysis of the Golub data. Setting rand=123, to make the results reproducible, # and setting med.fdr=FALSE, such that the mean number instead of the median number of falsely called # genes is computed. The output is assigned to an object for further analyses. if (interactive()) { sam.output<-sam(golub,golub.cl,med.fdr=FALSE,rand=123) # Now the number of significant genes and the FDR should be computed for other values of the threshold # Delta. Let's say Delta from 0.6 to 0.8 in steps of length 0.01. sam.delta(sam.output,seq(0.6,0.8,0.01)) }