sam.plot {siggenes} | R Documentation |
This functions performs a SAM or a SAM-Wilc analysis, respectively, for a specific threshold Delta, and (optionally) stores information on the differentially expressed genes in a file.
sam.plot(sam.out,delta,q.values=TRUE,R.fold=TRUE,R.unlog=TRUE, na.rm=FALSE,file.out=NA,gene.names=NULL)
sam.out |
the object to which the output of a previous analysis with
sam or sam.wilc was stored. |
delta |
the value of the threshold Delta for which the analysis should be performed. |
q.values |
if TRUE (default), the q-value for each gene will be computed. |
R.fold |
if TRUE (default), the fold change for each differentially
expressed gene will be computed. If the previous SAM analysis was an one class analysis
R.fold is automatically set to FALSE . |
R.unlog |
if TRUE , 2^data will be used in the computation of the
R.fold. This is recommended if data consists of log2 transformed gene expression
data. |
na.rm |
if FALSE (default), the fold change of genes with at least one
missing value will be set to NA . If TRUE , missing values will be
replaced by the genewise mean. |
file.out |
if specified (i.e. not NA ), general information like the number
of significant genes and the estimated FDR and gene-specific information is
stored in the corresponding file. |
gene.names |
a vector containing the names of the genes. |
a SAM Plot for the specified Delta and (optionally) an output file containing general information like the number of significant genes and the FDR and information about the differentially expressed genes like their names, q-values and fold changes.
vec.fdr |
a list containing the number of p0, significant genes, the FDR etc. for the specified Delta. |
sam.output |
a table containing gene-specific information about the differentially expressed genes like their IDs (i.e. the rows of the data set that contain the expression data of these genes), their expression score, q-values, the fold changes etc. |
row.sig.genes |
vector that consists of the rows of the data set that contain the expression data of the differentially expressed genes. |
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.
Storey, J.D., and Tibshirani, R. (2003). Statistical significance for genome-wide experiments, Technical Report, Department of Statistics, Stanford University.
Schwender, H. (2003). Assessing the false discovery rate in a statistical analysis of gene expression data, Chapters 5 and 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 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 sam.plot can be used to obtain the SAM plot and information about the differentially expressed # genes for a specified value of delta. sam.14<-sam.plot(sam.output,1.4,gene.names=golub.gnames[,3]) # sam.plot can also be used in a SAM-Wilc analysis for a specific Delta. # For further analyses, the row numbers of the differentially expressed genes can be obtained by sam.14$row.sig.genes }