deds.pval {DEDS} | R Documentation |

## Differential Expression via Distance Summary of p Values from
Multiple Models

### Description

`deds.pval`

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

### Usage

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 controled and q values
are returned.
If `adj="adjp"` , ajusted *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, yxiao@itsa.ucsf.edu,

Jean Yee Hwa Yang, jean@biostat.ucsf.edu.

### 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 MR: Selecting differentially expressed
genes from microarray experiment by sets of
statistics. *Bioinformatics*,
2004, accepted. http://www.biostat.ucsf.edu/jean/Papers/DEDS.pdf.

### See Also

`deds.stat`

, `deds.stat.linkC`

.

### Examples

[Package

*DEDS* version 1.0.3

Index]