[BioC] limma: print-tip loess and empty spots

Gordon Smyth smyth at wehi.EDU.AU
Sat Jun 2 06:26:28 CEST 2007

Dear Adrian,

At 08:36 AM 2/06/2007, Adrian Steward wrote:
>Thank you for your reply, Dr. Smyth.
>I do not yet completely understand exactly HOW normalizing works 
>(I've seen the data, transformations, and so I know what it does, 
>just not how, yet) but it appears to me that I can simply change the 
>sign of the normalized output to make the proper tests

In general, you cannot simplify the constructions of tests by 
swapping the sign of the normalized log-ratios. The only experiment 
in which people might be tempted to do this is a simple replicated 
comparison using two-colour arrays with dye-swaps (and you have given 
no indication that this is your experiment.) For anything more 
complicated, swapping the signs of the log-ratios would only 
complicate matters. Even for the replicated comparison, swapping the 
log-ratios is unhelpful because it prevents the inclusion of 
probe-specific dye-effects in the model.

>  (or as someone else stated, reverse the contrast / estimate statements).
>You picked up on my motivations here - I am chiefly concerned that 
>the exported normalized data has proper signs

The normalized data already has what we consider to be the "proper" signs.

>  because at present I am required to do all of my linear modeling 
> in SAS, and large datasets need to be 'read in.'   I personally 
> would rather do it all in R which is why I am running things in 
> parallel to make the case for limma-only analysis.

You can certainly fit linear models in SAS, but you can't do a limma 
empirical Bayes analysis.

>You people are both programmers and teachers, and thanks for your 
>patience with the noobs.

You can easily change the signs of columns of data in either R or 
SAS. You could get advice on how to do this from the R help list. But 
don't expect this from me or Keith because I believe it is undesirable.

There is absolutely no reason why linear modelling in SAS or R 
requires any prior fudging of the data. You can easily handle the 
data as it actually is. Spend a little more time understanding how 
linear modelling works for microarray data, then you'll see why this 
is so. That would be time much better spent than trying to persuade 
limmaGUI to do what it doesn't want to do.

Best wishes

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