DEDS (version 1.46.0)

deds.stat.linkC: Differentail Expression via Distance Summary of Multiple Statistics

Description

deds.stat.linkC integrates different statistics of differential expression (DE) to rank and select a set of DE genes.

Usage

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)

Arguments

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:

"t":

one or two sample t-statistics;

"f":

F-statistics;

"fc":

fold changes among classes;

"sam":

SAM-statistics;

"modt":

moderated t-statistics;

"modt":

moderated F-statistics;

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.

Value

An object of class DEDS. See DEDS-class.

Details

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.

References

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.

See Also

deds.pval, deds.stat.

Examples

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)