em.ggb {pickgene} | R Documentation |
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).
em.ggb(x, y, theta, start = c(2,1.2,2.7), pprior = 2, printit = FALSE, tol = 1e-9, offset = 0 )
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) |
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.
Four parameter vector theta
after convergence.
Michael Newton
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.
## Not run: em.ggb( x, y ) ## End(Not run)