sam.wilc {siggenes} | R Documentation |
Performs a Significance Analysis of Microarrays for a set of integer thresholds Delta. Instead of the modified t statistics, it uses Wilcoxon Rank Sums.
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)
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. |
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.
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. !!!
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 6, 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-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) }