[BioC] loess and duplicate correlation

Gordon Smyth smyth at wehi.edu.au
Thu Mar 31 12:39:58 CEST 2005

>Date: Wed, 30 Mar 2005 10:55:50 -0800
>From: Dennis Hazelett <hazelett at uoneuro.uoregon.edu>
>Subject: [BioC] loess and duplicate correlation
>To: bioconductor at stat.math.ethz.ch
>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.

Actually the two processes have different purposes. Loess normalization 
reduces the residual variability. Duplicate correlation does not do this, 
rather it assesses the residual variability more realistically -- p-values 
may go up or down as a consequence.

>However because I'm a little wary of normalisation,

Given the enormous weight of evidence showing that microarray data needs to 
be normalised, I'm wary of unnormalized data.

>  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

Effective normalisation improves the consistency of results between arrays, 
and hence the duplicate correlation, which measures the similarity between 
arrays to that between arrays, will tend to decrease. This is to be expected.

>  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?

You haven't stated any reason for suspicion -- you seem to have had only 
good experience -- so it is hard to know what further to say.


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