Hi Jose,
  Thank you very much for your time and effort put into answering my questions.
   
  The main thing for me now is to clarify whether it is wrong to normalize after filtering "undetectable" probes. I specifically use the term "undetectable" because this is what they are. One might speculate on whether they are low expressed or unexpressed, but actually their measurements represent technical noise I suppose. For example, AB1700 software replaces all measurements with signal-to-noise <1 with their upper limit standard deviations, thus avoiding negative values after the default background correction. Would you treat them as signals? I'd say that these are just background-level noise. Moreover, if I am not mistaken, background level measures are probe specific, meaning that there is no guarantee that low signal-to-noise ratios would always correspond to low signals, although generaly they correlate. I would not be arguing about this, but my plots show that (i) after filtering I still have a lot of low expressed genes and (ii) filtering removes quite a lot of
 genes with high signals. It does not look like by filtering I made the distribution of signals truncated on the left side, not at all. So I would say that probes on the left-hand side of the distribution after filtering represent those that are "unexpressed" and probes being filtered out are just noise.
  Jenny kindly answered my previous e-mails, sayng that filtering before normalization is not a good idea. However, is there any strong justification for this? Or it really does not matter when you do filtering as long as the number of remaining for normalization probes is still significant, say more than 10000? My idea of filtering before normalization step was to "help" a normalization algorithm by reducing the noise level and making initial distributions more close to each other. I do not entirely understand why using ALL available data for normalization step helps to normalize better.
   
  Thank you very much,
  Lev.
   
   
   
   
   
  J.delasHeras@ed.ac.uk wrote:
  
Hi Lev,

thanks for clarifying. It turns out that you're doing what I thought 
you were doing :-) But I still don't understand why you would want to 
remove that probe that only has a strong signal in one treatment. I 
mean, that very probe will probably be one of the hits you're looking 
for in the first place.
You seem concerned that having no signal in teh other treatments is 
somehow worsening your stats for comparisons between the other 3 
treatments, but I wouldn't be concerned about that. By removing probes 
that are only present in one treatment you're removing one class of 
potentially very interesting probes... I wouldn't do that.
You'd be removing a probe that has strong signal in 4, and negligible 
in 1, yet you'd leave something that has the same signal in 4, and 
just above background in 1... but both probes belong to the same class 
and you removed the one showing a more striking difference of the two.
If the biology of your treatments is such that you expect treatment 4 
to be quite different to the others, such that a very large proportion 
of probes will be expressed in 4 but absent in 1&2&3, then the removal 
of such probes may be useful in some way... I don't know, because in 
that case I'd be more concerned about how I normalise all 4 treatments 
together, considering that one of them should have a completely 
different distribution.

Please, don't take my word for it, I don't know everything and I am 
still learning, everyday, as I go along. I find that it's hard to make 
categorical statements about what is best in microarray analysis, 
because what is best depends on the actual experiment, and you need to 
understand the biology behind the data: what the experiments test for, 
what questions you're aiming to answer, and what the actual biological 
system is.
In principle I feel that if I am analysing together X number of 
experiments, making contrasts etc, is because they're somehow related 
and can be compared. If I do that, then as a principle, I don't want 
to remove a probe that only shows expression in one of the treatments 
because it seems to me that it is one class of probes that I really 
want to know about.

I think I can't say much more on this matter, Lev. You have to make 
your own decisions based on what it is that you are after.

best,

Jose




Quoting Lev Soinov :

> Hi Jose,
>
> Let's say we have 4 treatments, 3 replicates each. I am interested 
> in comparing 1vs2, 1vs3 and 1vs4. I assume that instead of making 
> pairwise comparisons it would be better to use something like the 
> following script.
>
> temp<-normalizeBetweenArrays(log2(signals), method='quantile')
> design <- model.matrix(~0 +factor(c(1,1,1,2,2,2,3,3,3,4,4,4)))
> colnames(design) <- c("T1","T2","T3","T4")
> contrast.matrix <-
> makeContrasts(T2-T1, T3-T1, T4-T1, levels=design)
> fit <- lmFit(temp, design)
> fit2 <- contrasts.fit(fit, contrast.matrix)
> fit2 <- eBayes(fit2)
>
> So, all treatments are included in lmFit to get more power. Let's 
> suppose a probe has "negligible" signal in 1, 2 and 3, but very 
> strong signal in 4. It would mean that for this probe T2-T1 and 
> T3-T1 could produce erroneous results and also would negatively 
> influence T4-T1 as lmFit would estimate parameters gathering 
> information across all 4 treatments.
>
> Is it reasonable?
> Thank you,
> Lev.
>
>
>
>
> J.delasHeras@ed.ac.uk wrote:
> Quoting Lev Soinov :
>
>> Hi Jose,
>>
>> Yes, I totally understand the point about losing some probes that
>> are "not expressed" in one treatment but "expressed" in some other
>> treatments. However, let's say we have 4 treatments, comparing 1vs2,
>> 1vs3 and 1vs4 in LIMMA. If I am not mistaken, it is recommended to
>> process all treatments together to get more power. Suppose a probe
>> has "negligible" signal in 1, 2 and 3, but very strong signal in 4.
>> You would obviously keep it, according to you procedure, and you
>> would be absolutely right if you were interested in 1vs4 only.
>> However, in this particular situation 1vs2 and 1vs3 doesn't make
>> much sense and could produce false positive results. Also, if 1, 2
>> and 3 do not contain "true" signals but only some near-background
>> noise, how would it help to estimate the variance for this probe? I
>> may be wrong here, but it seems to me that information from 1, 2 and
>> 3 would just add more error in lmFit, thus obscuring inferences for
>> 1vs4 as well.
>>
>> Thank you,
>> Lev.
>
> Hi Lev,
>
> I must admit I don't quite follow the way you pick comparisons and probes.
> If I want to analyse comparisons between all 4 treatments, and a probe
> is only present in one of them, say number 4, I'd probably keep it. If
> you want to remove it because it's only expressed in one of them,
> that's your call, but I'd keep it: I am looking for differences
> between treatments, and right there there's a clear one between 4 and
> all teh others... why lose it? Just to improve the FDR a little? I
> don't understand that rationale.
> It's not how I would go about things, but it probably depends on the
> actual experiment and what your goal is. If I don't care about things
> that are different between treatment 4 and the other three... then I'd
> just leave treatment 4 out altogether.
>
> I'm sorry if I am not fully understanding you.
>
> Jose
>
> --
> Dr. Jose I. de las Heras Email: J.delasHeras@ed.ac.uk
> The Wellcome Trust Centre for Cell Biology Phone: +44 (0)131 6513374
> Institute for Cell & Molecular Biology Fax: +44 (0)131 6507360
> Swann Building, Mayfield Road
> University of Edinburgh
> Edinburgh EH9 3JR
> UK
>
>
>
>
> ---------------------------------
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> Championship.



-- 
Dr. Jose I. de las Heras Email: J.delasHeras@ed.ac.uk
The Wellcome Trust Centre for Cell Biology Phone: +44 (0)131 6513374
Institute for Cell & Molecular Biology Fax: +44 (0)131 6507360
Swann Building, Mayfield Road
University of Edinburgh
Edinburgh EH9 3JR
UK



       
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