deds.stat integrates different statistics of differential
expression (DE) to rank and select a set of DE genes.
deds.stat(X, L, B = 1000, testfun = list(t = comp.t(L), fc = comp.FC(L),
sam = comp.SAM(L)), tail = c("abs", "lower", "higher"), distance =
c("weuclid", "euclid"), adj = c("fdr", "adjp"), nsig = nrow(X))read.table.B should be 0 (zero) or any number not less than the total
number of permutations.deds.chooseTest.side="abs", two-tailed tests, the null hypothesis is
rejected for large absolute values of the test statistic.
If side="higher", one-tailed tests, the null hypothesis
is rejected for large values of the test statistic.
If side="lower", one-tailed tests, the null hypothesis is
rejected for small values of the test statistic.
distance="weuclid", weighted euclidean distance, the
weight for statistic $t$ is $1/MAD(t)$;
If distance="euclid", euclidean distance.
adj="fdr", False Discovery Rate is controled and $q$ values
are returned.
If adj="adjp", ajusted $p$ values that controls family wise
type I error rate is returned.adj = "fdr", nsig specifies the number of top
differentially expressed genes whose $q$ values will be calculated; we recommend
setting nsig < m, as the computation of $q$ values will be extensive. $q$ values
for the rest of genes will be approximated to 1. If adj = "adjp", the
calculation of the adjusted $p$ values will be for the whole dataset.DEDS. See DEDS-class.
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.
deds.pval, deds.stat.linkCX <- 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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