Utilitiy functions for the EBarrays package
x can be a character vector (of length > 2) (see example), or an
arbitrary connection which should provide patterns, one line for
each pattern. If
logical variable specifying whether the pattern is ordered or not
ebPatterns creates objects that represent a collection of
hypotheses to be used by
ebPatterns creates an Object of class “ebarraysPatterns”, to
be used in other functions such as
emfit. This is
nothing more than a list (and can be treated as such as far as
indexing goes) and is used only for method dispatch.
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.
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