Description Usage Arguments Details Value Author(s) References See Also
deds.pval
integrates different p values of differential
expression (DE) to rank and select a set of DE genes.
1 
X 
A matrix, with m rows corresponding to variables (hypotheses) and n columns corresponding to p values from different statistical models. 
E 
A numeric vector indicating the location of the most extreme p values in the direction of differential expression. 
adj 
A character string specifying the type of multiple testing
adjustment. 
B 
The number of permutations. For a complete enumeration,

nsig 
A numeric variable specifying the number of top genes that will be returned. 
deds.pval
summarizes p values from multiple statistical models
for the evidence of DE. The DEDS methodology treats each gene as
a point corresponding to a gene's vector of DE measures. An "extreme
origin" is defined as the point that indicate DE, typically a vector
of zero p values. The distance from all points to the extreme is
computed and the ranking of a gene for DE is determined by the
closeness of the gene to the extreme. To determine a cutoff for
declaration of DE, null referent distributions are generated using an
approach similar to the gap statistic (see Reference below). DEDS can also summarize
different statistics, see deds.stat
and
deds.stat.linkC
.
An object of class DEDS
. See DEDSclass
.
Yuanyuan Xiao, [email protected],
Jean Yee Hwa Yang, [email protected].
Tibshirani, R., Walther G., and Hastie T. (2000). Estimating the number of clusters in a dataset via the gap statistic. Department of Statistics, Stanford University, http://wwwstat.stanford.edu/~tibs/ftp/gap.ps
Yang, Y.H., Xiao, Y. and Segal M.R.: Selecting differentially expressed genes from microarray experiment by sets of statistics. Bioinformatics 2005 21:10841093.
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