Class of Families to be used in the EBarrays package
Objects used as family in the
The package contains three functions that create such objects for the three most commonly used families, Gamma-Gamma, Lognormal-Normal and Lognormal-Normal with modified variances. Users may create their own families as well.
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eb.createFamilyGG() eb.createFamilyLNN() eb.createFamilyLNNMV()
emfit function can potentially fit models
corresponding to several different Bayesian conjugate families. This
is specified as the
family argument, which ultimately has to be
an object of formal class “ebarraysFamily” with some specific slots
that determine the behavior of the ‘family’.
For users who are content to use the predefined GG, LNN and LNNMV models, no
further details than that given in the documentation for
emfit are necessary. If you wish to create your own
families, read on.
Objects of class “ebarraysFamily” for the three predefined families Gamma-Gamma , Lognormal-Normal and Lognormal-Normal with modified variances.
Objects from the Class
Objects of class “ebarraysFamily” can be created by calls of the
new("ebarraysFamily", ...). Predefined objects
corresponding to the GG, LNN and LNNMV models can be created by
eb.createFamilyLNNMV(). The same
effect is achieved by coercing from the strings
An object of class “ebarraysFamily” extends the class
"character" (representing a short hand name for the class) and
should have the following slots (for more details see the source
A not too long character string describing the family
function that maps user-visible parameters to the parametrization that would be used in the optimization step (e.g.
log(sigma^2)for LNN). This allows the user to think in terms of familiar parametrization that may not necessarily be the best when optimizing w.r.t. those parameters.
inverse of the link function
function of a single argument
data(matrix containing raw expression values), that calculates and returns as a numeric vector initial estimates of the parameters (in the parametrization used for optimization)
function taking arguments
thetaand a list called
f0calculates the negative log likelihood at the given parameter value
theta(again, in the parametrization used for optimization). This is called from
emfit. When called, only genes with positive intensities across all samples are used.
f0.ppis essentially the same as
f0except the terms common to the numerator and denominator when calculating posterior odds may be removed. It is called from
function that takes arguments
patterns(of class “ebarraysPatterns”) and
groupid(for LNNMV family only) and returns a list with two components,
common.argsis a list of arguments to
f0that don't change from one pattern to another, whereas
pattern.args[[i]][[j]]is a similar list of arguments, but specific to the columns in
pattern[[i]][[j]]. Eventually, the two components will be combined for each pattern and used as the
function of two arguments
x(data vector, containing log expressions) and
theta(parameters in user-visible parametrization). Returns log marginal density of the natural log of intensity for the corresponding theoretical model. Used in
vector of lower bounds for the argument
f0. Used in
vector of upper bounds for the argument
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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