######################################################################
 #
 # An illustration of the R functions in this directory.
 #
 # R 1.6.1 or later.
 #
 # License: GPL Version 2.0 or later.
 #
 # Author: M.A. Newton  newton@stat.wisc.edu
 #
 # Reference:  Newton, Noueiry, Sarkar, and Ahlquist (2003).
 #     Detecting differential gene expression with a semiparametric
 #     hierarchical mixture method.  Technical Report 1074-REVISED, Department
 #     of Statistics, UW Madison.  DECEMBER 2003.
 #
 # Web:  www.stat.wisc.edu/~newton/
 #
 ######################################################################


source("cvdiag.R")
source("spfit.R")

x <- # a matrix of expressions, raw scale, 1 column for each replicate
     # one row for each gene

y <- # like x, but in second condition

cvdiag(x)  # check the parametric component assumptions

fit  <- spfit(x,y) # fit the semiparametric model

        # Be patient, EM is slow and this prototype code is too!
        # Perhaps try it on a subset of genes to see it working.

# Take a look at the distribution of latent means
plot( fit$grid,  fit$pi)

# Take a look at some gene specific inference

fit$post[1:10,]