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 read.table . | ||||||||||||||
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,
B should be 0 (zero) or any number not less than the total
number of permutations. | ||||||||||||||
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, test could be any of the
following:
| ||||||||||||||
tail |
A character string specifying the type of rejection
region. If 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.
| ||||||||||||||
extras |
Extra parameter needed for the test specified; see
deds.genExtra . | ||||||||||||||
distance |
A character string specifying the type of distance
measure used for the calculation of the distance to the extreme
point (E). If distance="weuclid" , weighted euclidean distance, the
weight for statistic t is 1/MAD(t); If distance="euclid" , euclidean distance.
| ||||||||||||||
adj |
A character string specifying the type of multiple testing
adjustment. If 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 are returned. | ||||||||||||||
nsig |
If 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. | ||||||||||||||
quick |
A logical variable specifying if a quick but memory
requiring procedure will be selected. If quick=TRUE ,
permutation will be carried out once and stored in memory; If
quick=FALSE a fixed seeded sampling procedure will be
employed, which requires more computation time as the permutation
will be carried out twice, but will not use extra memory for storage. |
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)