# 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

1 |

### Arguments

`fit` |
output from |

`data` |
a numeric matrix or an object of class “ExpressionSet”
containing the data, typically the same one used in the |

`...` |
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

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