[BioC] loess and duplicate correlation

Dennis Hazelett hazelett at uoneuro.uoregon.edu
Wed Mar 30 20:55:50 CEST 2005

Hello bioconductors,
I fit a linear model to my data with 3 coefficients. I used loess 
normalization on genepix data with no background correction. With my 
data set, loess normalization resulted in slight reductions in p values 
(relative to "median" normalization for example) and reordering of the 
lists of DE genes for all three coefficients, which I took to be a good 
sign. I also have a series of replicate spots, and running 
duplicateCorrelation and including the consensus correlation (~0.55) 
term in my linear fit further improved the p values and resulted in some 
changes in the lists of DE genes. All of this suggests to me that loess 
and duplicate correlation served to reduce the estimate of variance in 
gene expression and weed out artifacts.
However because I'm a little wary of normalisation, I took my raw data 
set, non-normalized and non-background corrected and ran 
duplicateCorrelation on it. For un-normalized data the consensus 
correlation is ~0.73, quite a bit higher than for the loess-normalized 
data. After running the same lmFit model with this data set I once again 
obtained different lists of DE genes, with many of the strongest 
conclusions carrying over, giving me confidence that I applied the 
correct methods and function calls. My question is, should I be 
suspicious of the normalized data set? Am I at significant risk of 
generating large numbers of artifactual DE genes?

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