postprob {EBarrays} | R Documentation

## Calculates posterior probabilities for expression patterns

### Description

Takes the output from emfit and calculates the posterior probability of each of the hypotheses, for each gene.

### Usage

    postprob(fit, data, ...)

### Arguments

`fit` | output from `emfit`
`data` | a numeric matrix or an object of class “ExpressionSet” containing the data, typically the same one used in the `emfit` fit supplied below.
`...` | other arguments, ignored

### Value

An object of class “ebarraysPostProb”. Slot `joint` is an three dimensional array of probabilities. Each element gives the posterior probability that a gene belongs to certain cluster and have certain pattern. `cluster` is a matrix of probabilities with number of rows given by the number of genes in `data` and as many columns as the number of clusters for the fit. `pattern` is a matrix of probabilities with number of rows given by the number of genes in `data` and as many columns as the number of patterns for the fit. It additionally contains a slot ‘hypotheses’ containing these hypotheses.

### Author(s)

Ming Yuan, Ping Wang, Deepayan Sarkar, Michael Newton, and Christina Kendziorski

### References

Newton, M.A., Kendziorski, C.M., Richmond, C.S., Blattner, F.R. (2001). On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 8:37-52.

Kendziorski, C.M., Newton, M.A., Lan, H., Gould, M.N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22:3899-3914.

Newton, M.A. and Kendziorski, C.M. Parametric Empirical Bayes Methods for Microarrays in The analysis of gene expression data: methods and software. Eds. G. Parmigiani, E.S. Garrett, R. Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.

Newton, M.A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture model. Biostatistics 5: 155-176.

Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.

### See Also

`emfit`

### Examples

    data(sample.ExpressionSet) ## from Biobase
    eset <- exprs(sample.ExpressionSet)
    patterns <- ebPatterns(c("1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
                             "1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2"))
    gg.fit <- emfit(data = eset, family = "GG", hypotheses = patterns, verbose = TRUE)
    prob <- postprob(gg.fit,eset)

[Package EBarrays version 2.56.0 Index]