[BioC] get over it/WAKE uP and SMELL the COFFEE
Rafael A. Irizarry
ririzarr at jhsph.edu
Thu Dec 18 20:24:44 MET 2003
this is likely to be a consequence of the probe effect. consider that the
log scale correlation of two affy reps is higher than .99 (with rma).
however, if for each gene you define two probesets at random (half the
probes to one probeset and half to the other) and recompute rma, the
correlation between "half probe sets" for the same genes (within an array)
drops to around 0.5. this is consistent with your finds. the log ratio
cancels out the probe effect so tests based on these will have smaller
correlations than 0.99.
this goes to
show that correlations of log
expression values is not useful as a measure of agreement. a much better
measure is the spread (iqr, sd, ...) of the log ratios. similarly,
scatterplots arent as useful as MA plots.
On Thu, 18 Dec 2003, Naomi
Altman wrote:
> One thing that makes me very cautious about over-interpreting tests,
> however, is the following:
>
> We have tried several options for normalizing arrays, and found that the
> resulting expression values (for the methods we used) were correlated
> 98-99%. But if we then test for differential expression, we find the
> overlap in the list of "top genes" is only 50-60%.
>
> --Naomi
>
>
> At 09:09 AM 12/18/2003, Stephen Henderson wrote:
> >I agree with some of WHAT you say CHAD, the PROBLEM is THAT MOST
> >multiVARIATE methods are BUILt on top OF the marginal tests. FOR instance
> >machine learning methods are based on gene subsets for each of k CROSS
> >validations. USE of the appropriate TEST (fold/T/F/cyber-T/etc..)for subset
> >selection is IMHO the most IMPORTANT!! choice .
> >
> >
> >Stephen
> >
> >
> >**********************************************************************
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> Naomi S. Altman 814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics 814-863-7114 (fax)
> Penn State University 814-865-1348 (Statistics)
> University Park, PA 16802-2111
>
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