deds.stat integrates different statistics of differential
expression (DE) to rank and select a set of DE genes.
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A matrix, with m rows corresponding to variables
(hypotheses) and n columns corresponding to observations.
In the case of gene expression data, rows correspond to genes and
columns to mRNA samples. The data can be read using
A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1.
The number of permutations. For a complete enumeration,
A list of functions specifying the statistics to be
used to test the null hypothesis of no association between the
variables and the class labels. The default uses t, fold change and
SAM. The input can also be generated using the function
A character string specifying the type of rejection
A character string specifying the type of distance
measure used for the calculation of the distance to the extreme
A character string specifying the type of multiple testing
deds.stat summarizes multiple statistical measures 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 maxima of all statistics and the
distance from all points to the extreme is computed and 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 by permuting the data matrix.
Statistical measures currently in the DEDS package include t statistics
comp.t), fold changes(
comp.F), SAM ((
comp.modt), moderated F statistics
comp.modF), and B statistics (
user can also supply their own function for a statistic other than the
above, provided the function is written in a similar format as the
deds.stat could be slow if the size of the data
matrix and the number of permutations are big. We hence recommend the
user to use
deds.stat.linkC as the default
deds.stat.linkC interfaces to a C function,
which handles a 10,000 by 10 matrix and 1000 permutations in minutes.
DEDS can also summarize p values from different statistical
An object of class
Yang, Y. H., Xiao, Y. and Segal MR: Selecting differentially expressed genes from microarray experiment by sets of statistics. Bioinformatics, 2004, accepted. http://www.biostat.ucsf.edu/jean/Papers/DEDS.pdf.
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X <- matrix(rnorm(1000,0,0.5), nc=10) L <- rep(0:1,c(5,5)) # genes 1-10 are differentially expressed X[1:10,6:10]<-X[1:10,6:10]+1 # DEDS summarizing t, sam and fc deds.X <- deds.stat(X, L, B=200) # DEDS summarizing t, tmod and fc ## Not run: deds.X <- deds.stat(X, L, testfun=list(t=comp.t(L), tmod=comp.modt(L), sam=comp.SAM(L))) ## End(Not run) # one can also use: ## Not run: deds.X <- deds.stat(X, L, testfun=deds.chooseTest(L, tests=c("t","modt","fc"))) ## End(Not run)
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