pickgene (version 1.44.0)

em.ggb: EM calculation for Gamma-Gamma-Bernoulli Model

Description

The function plots contours for the odds that points on microarray show differential expression between two conditions (e.g. Cy3 and Cy5 dye channels on the same microarray).

Usage

em.ggb(x, y, theta, start = c(2,1.2,2.7), pprior = 2, printit = FALSE, tol = 1e-9, offset = 0 )

Arguments

x

first condition expression levels

y

second condition expression levels

theta

four parameters a,a0,nu,p

start

starting estimates for theta

pprior

Beta hyperparameter for prob p of differential expression

printit

print iterations if TRUE

tol

parameter tolerance for convergence

offset

offset added to xx and yy before taking log (can help with negative adjusted values)

Value

Four parameter vector theta after convergence.

Details

Fit Gamma/Gamma/Bernoulli model (equal marginal distributions) The model has spot intensities x ~ Gamma(a,b); y ~ Gamma(a,c). The shape parameters b and c are ~ Gamma(a0,nu). With probability p, b = c; otherwise b != c. All spots are assumed to be independent.

References

MA Newton, CM Kendziorski, CS Richmond, FR Blattner and KW Tsui (2000) ``On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data,'' J Computational Biology 00: 000-000.

See Also

oddsplot

Examples

## Not run: 
# em.ggb( x, y )
# ## End(Not run)