[BioC] question about limma ebayes Fit lod score

[magates at u.washington.edu]

Hello all, 
 
With limma I am confused about the meaning of lods values (ebfit$lods) for an 
eBayes Fit Object.  
Many of the genes listed as significant by classifyTestsF have negative 
ebfit$lods scores, which is what is confusing me. 
 
Associated ebfit$p.values of the genes listed by classifyTestsF are all indeed 
smaller than the threshold p.value, as I would expect.    
(A plot of log(p.values) vs lods is pretty darn linear, again as I would 
expect, just the intercept is shifted by about -8 units.) 
 
My dim understanding of Lods would be that negative numbers are not 
significant. Do I misunderstand the meaning of the lods being reported, or has 
something gone awry? 
 
As always, thanks for your help. 
 
Michael Gates 
 
some details: 
fit <- lmFit(MAb1,design) 
ebfit <- eBayes(fit) 
results <- classifyTestsF(ebfit,p.value=0.05) 
o <- order(ebfit$lods[,3],decreasing=TRUE) 
 
# now filter thru a boolean function that -among other things -  
# selects a subset of the significant genes for which the  
# interaction effect is significant, but a particular main effect  
# is not significant (details below) 
s <- myResultsFilter() 
 
# and look at the fits, p.values, results, lods, etc  
# of the desired subset of signficant genes 
data.frame( 
Name=MAb1$genes$Name[o[s[o]]], 
Expression=ebfit$coefficients[o[s[o]],3], 
P=ebfit$p.value[o[s[o]],3], 
Ftest=results05[o[s[o]],3], 
LOD=ebfit$lods[o[s[o]],3] 
) 
 
myResultsFilter <- function(fitObject=ebfit, resultObject=results,foldDiff=0) 
{  
# make sure LB is a number  
# and that it's larger than the desired fold difference 
  (!is.na(fitObject$coefficients[,3]) & 
  (abs(fitObject$coefficients[,3]) > foldDiff)) & 
  ( 
# if L is not significant, select significant LB  
# that are not significant for B  
   ((resultObject[,1] %in% c(0)) & (resultObject[,3] %in% c(-1,1)) & 
    (resultObject[,2] %in% c(0))) | 
# if L is significant, make sure it and LB go same way,  
# and that B is not significant 
   ((resultObject[,1] %in% c(1)) &  
    (resultObject[,3] %in% c(1)) &  
    (resultObject[,2] %in% c(0))) | 
   ((resultObject[,1] %in% c(-1)) &  
    (resultObject[,3] %in% c(-1)) &  
    (resultObject[,2] %in% c(0)) 
   ) 
  ) 
}