Description Objects from the Class Slots Methods Author(s) References See Also

This is a class representation for the specification of the fudge factor in an EBAM analysis as proposed by Efron et al. (2001).

Objects can be created using the function `find.a0`

.

`mat.z`

:Object of class

`"matrix"`

containing the expression scores of the genes for each of the possible values for the fudge factor, where each row corresponds to a gene, and each column to one of the values for the fudge factor*a0*.`mat.posterior`

:Object of class

`"matrix"`

consisting of the posterior probabilities of the genes for each of the possible values for the fudge factor, where each row of`mat.posterior`

corresponds to a gene, and each column to one of the values for*a0*. The probabilities in`mat.posterior`

are computed using the monotonically transformed test scores (see the Details section of`find.a0`

).`mat.center`

:Object of class

`"matrix"`

representing the centers of the`nrow(mat.center)`

intervals used in the logistic regression with repeated observations for estimating*f/f0*for each of the`ncol(mat.center)`

possible values for the fudge factor.`mat.success`

:Object of class

`"matrix"`

consisting of the numbers of observed test scores in the`nrow(mat.success)`

intervals used in the logistic regression with repeated observations for each of the`ncol(mat.success)`

possible values for the fudge factor.`mat.failure`

:Object of class

`"matrix"`

containing the numbers of permuted test scores in the`nrow(mat.failure)`

intervals used in the logistic regression with repeated observations for each of the`ncol(mat.failure)`

possible values for the fudge factor.`z.norm`

:Object of class

`"numeric"`

comprising the values of the`nrow(mat.z)`

quantiles of the standard normal distribution (if any`mat.z<0`

) or an F-distribution (if all`mat.z >= 0`

).`p0`

:Object of class

`"numeric"`

specifying the prior probability that a gene is not differentially expressed.`mat.a0`

:Object of class

`"data.frame"`

comprising the number of differentially expressed genes and the estimated FDR for the possible choices of the fudge factor specified by`vec.a0`

.`mat.samp`

:Object of class

`"matrix"`

consisting of the`nrow{mat.samp}`

permutations of the class labels.`vec.a0`

:Object of class

`"numeric"`

representing the possible values of the fudge factor*a0*.`suggested`

:Object of class

`"numeric"`

revealing the suggested choice for the fudge factor, i.e. the value of`vec.a0`

that leads to the largest number of differentially expressed genes.`delta`

:Object of class

`"numeric"`

specifying the minimum posterior probability that a gene must have to be called differentially expressed.`df.ratio`

:Object of class

`"numeric"`

representing the degrees of freedom of the natural cubic spline used in the logistic regression with repeated observations.`msg`

:Object of class

`"character"`

containing information about, e.g., the type of analysis.`msg`

is printed when`print`

is called.`chip`

:Object of class

`"character"`

naming the microarray used in the analysis. If no information about the chip is available,`chip`

will be set to`""`

.

- plot
`signature(object = "FindA0")`

: Generates a plot of the (logit-transformed) posterior probabilities of the genes for a specified value of*Delta*and a set of possible values for the fudge factor. For details, see`help.finda0(plot)`

. For the arguments, see`args.finda0(plot)`

.`signature(object = "FindA0")`

: Prints the number of differentially expressed genes and the estimated FDR for each of the possible values of the fudge factor specified by`vec.a0`

. For details, see`help.finda0(print)`

. For arguments, see`args.finda0(print)`

.- show
`signature(object = "FindA0")`

: Shows the output of an analysis with`find.a0`

.

Holger Schwender, holger.schw@gmx.de

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis
of a Microarray Experiment, *JASA*, 96, 1151-1160.

Schwender, H., Krause, A. and Ickstadt, K. (2003). Comparison of
the Empirical Bayes and the Significance Analysis of Microarrays.
*Technical Report*, SFB 475, University of Dortmund, Germany.

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