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emfit: Implements EM algorithm for gene expression mixture model

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Implements the EM algorithm for gene expression mixture model

Usage

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emfit(data,
      family,
      hypotheses,
      ...)

Arguments

data

a matrix

family

an object of class “ebarraysFamily” or a character string which can be coerced to one. Currently, only the characters "GG" and "LNN", and "LNNMV" are valid. For LNNMV, a groupid is required. See below. Other families can be supplied by constructing them explicitly.

hypotheses

an object of class “ebarraysPatterns” representing the hypotheses of interest. Such patterns can be generated by the function ebPatterns

...

other arguments. These include:

cluster

if type=1, cluster is a vector specifying the fixed cluster membership for each gene; if type=2, cluster specifies the number of clusters to be fitted

type

if type=1, the cluster membership is fixed as input cluster; if type=2, fit the data with a fixed number of clusters

criterion

only used when type=2 and cluster contains more than one integers. All numbers of clusters provided in cluster will be fitted and the one that minimizes criterion will be returned. Possible values now are "BIC", "AIC" and "HQ"

cluster.init

only used when type=2. Specify the initial clustering membership.

num.iter

number of EM iterations

verbose

logical or numeric (0,1,2) indicating desired level of information printed for the user

optim.control

list passed unchanged to optim for finer control

groupid

an integer vector indicating which group each sample belongs to, required in the “LNNMV” model. It does not depend on “hypotheses”.

Details

There are many optional arguments. So a call might look more like this:

emfit(data, family, hypotheses, cluster, type=2, criterion="BIC", cluster.init = NULL, num.iter = 20, verbose = getOption("verbose"), optim.control = list(), ...)

Value

an object of class “ebarraysEMfit”, that can be summarized by show() and used to generate posterior probabilities using postprob

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

ebPatterns, ebarraysFamily-class

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)
show(gg.fit)


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