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

1 |

`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 |

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.

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

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.

`emfit`

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)
``` |

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