deds.stat.linkC {DEDS} | R Documentation |
deds.stat.linkC
integrates different statistics of differential
expression (DE) to rank and select a set of DE genes.
deds.stat.linkC(X, L, B = 1000, tests = c("t", "fc", "sam"), tail = c("abs", "lower", "higher"), extras = NULL, distance = c("weuclid", "euclid"), adj = c("fdr", "adjp"), nsig = nrow(X), quick = TRUE)
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 k-1. | ||||||||||||||
B |
The number of permutations. For a complete enumeration,
| ||||||||||||||
tests |
A character vector specifying the statistics to be
used to test the null hypothesis of no association between the
variables and the class labels,
| ||||||||||||||
tail |
A character string specifying the type of rejection
region. | ||||||||||||||
extras |
Extra parameter needed for the test specified; see
| ||||||||||||||
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 | ||||||||||||||
quick |
A logical variable specifying if a quick but memory
requiring procedure will be selected. If |
deds.stat.linkC
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
(tests="t"
), fold changes (tests="fc"
), F
statistics (tests="f"
), SAM (tests="sam"
), moderated
t (tests="modt"
), moderated F statistics
(tests="modf"
), and B statistics (tests="B"
). The
function deds.stat.linkC
interfaces to C functions for the
tests and the computation of DEDS. For more flexibility, the user can
also use deds.stat
which has the same functionality as
deds.stat.linkC
but is written completely in R (therefore
slower) and the user can supply their own function for a statistic
not covered in the DEDS package.
DEDS can also summarize p values from different statistical models, see
deds.pval
.
An object of class DEDS
. See DEDS-class
.
Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.
Yang, Y.H., Xiao, Y. and Segal M.R.: Selecting differentially expressed genes from microarray experiment by sets of statistics. Bioinformatics 2005 21:1084-1093.
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, fc and sam d <- deds.stat.linkC(X, L, B=200)