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

`deds.stat.linkC`

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

1 2 3 |

`X` |
A matrix, with | ||||||||||||||

`L` |
A vector of integers corresponding to observation (column)
class labels. For | ||||||||||||||

`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, [email protected],

Jean Yee Hwa Yang, [email protected].

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.

1 2 3 4 5 6 7 | ```
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)
``` |

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