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

In pickgene: Adaptive Gene Picking for Microarray Expression Data Analysis

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)

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.

Value

Four parameter vector theta after convergence.

Author(s)

Michael Newton

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)

pickgene documentation built on Nov. 8, 2020, 6:50 p.m.