deds.pval: Differential Expression via Distance Summary of p Values from...

Description Usage Arguments Details Value Author(s) References See Also

View source: R/DEDS.R

Description

deds.pval integrates different p values of differential expression (DE) to rank and select a set of DE genes.

Usage

1
deds.pval(X, E = rep(0, ncol(X)), adj = c("fdr", "adjp"), B = 200, nsig = nrow(X))

Arguments

X

A matrix, with m rows corresponding to variables (hypotheses) and n columns corresponding to p values from different statistical models.

E

A numeric vector indicating the location of the most extreme p values in the direction of differential expression.

adj

A character string specifying the type of multiple testing adjustment.
If adj="fdr", False Discovery Rate is controlled and q values are returned.
If adj="adjp", adjusted p values that controls family wise type I error rate is returned.

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.

nsig

A numeric variable specifying the number of top genes that will be returned.

Details

deds.pval summarizes p values from multiple statistical models 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 point that indicate DE, typically a vector of zero p values. The distance from all points to the extreme is computed and the 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 using an approach similar to the gap statistic (see Reference below). DEDS can also summarize different statistics, see deds.stat and deds.stat.linkC.

Value

An object of class DEDS. See DEDS-class.

Author(s)

Yuanyuan Xiao, [email protected],
Jean Yee Hwa Yang, [email protected].

References

Tibshirani, R., Walther G., and Hastie T. (2000). Estimating the number of clusters in a dataset via the gap statistic. Department of Statistics, Stanford University, http://www-stat.stanford.edu/~tibs/ftp/gap.ps

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.stat, deds.stat.linkC.


Bioconductor-mirror/DEDS documentation built on June 1, 2017, 5:11 p.m.