Calculates posterior probabilities for expression patterns

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Description

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

Usage

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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

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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)