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.
1 2 3
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 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.
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
theta and a list called
f0 calculates 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.pp is essentially the same as
f0 except the terms
common to the numerator and denominator when calculating posterior
odds may be removed. It is called from
function that takes arguments
class “ebarraysPatterns”) and
groupid (for LNNMV family
only) and returns a list with two components,
common.args is a list of arguments to
f0 that 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
the two components will be combined for each pattern and used as
args argument to
function of two arguments
x (data vector, containing log
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.
1 2 3
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.