emfit {EBarrays} | R Documentation

## Implements EM algorithm for gene expression mixture model

### Description

Implements the EM algorithm for gene expression mixture model

### Usage

    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

    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)

[Package EBarrays version 2.56.0 Index]