Description Usage Arguments Details Value Author(s) References See Also Examples
deds.stat
integrates different statistics of differential
expression (DE) to rank and select a set of DE genes.
1 2 3 
X 
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

L 
A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k1. 
B 
The number of permutations. For a complete enumeration,

testfun 
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

tail 
A character string specifying the type of rejection
region. 
distance 
A character string specifying the type of distance
measure used for the calculation of the distance to the extreme
point (E). 
adj 
A character string specifying the type of multiple testing
adjustment. 
nsig 
If 
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.FC
), F
statistics (comp.F
), SAM ((comp.SAM
), moderated
t (comp.modt
), moderated F statistics
(comp.modF
), and B statistics (comp.B
). The
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
above ones.
The function 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
function. 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
models, see deds.pval
.
An object of class DEDS
. See DEDSclass
.
Yuanyuan Xiao, [email protected],
Jean Yee Hwa Yang, [email protected].
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  X < matrix(rnorm(1000,0,0.5), nc=10)
L < rep(0:1,c(5,5))
# genes 110 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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.