emfit {EBarrays} | R Documentation |
Implements the EM algorithm for gene expression mixture model
emfit(data, family, hypotheses, cluster, type=2, criterion="BIC", cluster.init = NULL, num.iter = 20, verbose = getOption("verbose"), optim.control = list(),...)
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
|
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''. |
an object of class ``ebarraysEMfit'', that can be summarized by
show()
and used to generate posterior probabilities using
postprob
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.
ebPatterns
, ebarraysFamily-class
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)