[BioC] Paired arrays and limma
john seers (IFR)
john.seers at bbsrc.ac.uk
Thu Mar 13 10:18:59 CET 2008
Hi James
Thanks for the reply.
>Theoretically yes.
That sounds a bit doubting? Are you saying not really?
>By my count, each paired sample got the same diet and had the same
disease. In which case, what's up >with the paired samples?
Yes, exactly correct. The arrays are a Before and After for a volunteer.
A Before array was made. They are then fed either diet1 or diet2 for 6
months and the After array made. There are volunteers who are controls
and volunteers who have the condition. So the pairings are because they
are the same volunteer before and after. Does that make it clearer?
Diet1 is a control diet.
>I *was* going to say that you could just compute the differences
between the pairs (diet1 - diet2). Then >it would be easy to get the
diet-specific genes for each type (control and disease), as well as the
>interaction (genes that react differently to diet depending on the
disease status). But given how you
>describe the samples below I don't think that is necessary,
What you describe sounds something like what I want. How would that be
done? Can you explain why in your view it is not necessary? You have
left me behind at this point.
>nor do I really understand what you are doing.
I am sorry I cannot explain it better. Perhaps if you ask me a specific
question I can make it clearer.
Thanks again for taking the time and trouble to help.
Regards
John
---
-----Original Message-----
From: James W. MacDonald [mailto:jmacdon at med.umich.edu]
Sent: 12 March 2008 20:10
To: john seers (IFR)
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] Paired arrays and limma
Hi John,
john seers (IFR) wrote:
>
>
> Hi James
>
> Thanks and thanks for your effort. I understand that mostly but I am
> still not sure what I can do setting up a design matrix and a
> contrasts matrix. I do not like the idea of losing flexibility so I
> would like to be able to do it all through limma.
>
> I want to be able to see the effects between the two diets and on the
> disease condition. Diet1 is a control diet and I would like to see the
> effects of Diet2. Also the difference between the Control and Disease
> conditions by diet.
>
> So if I do something like:
>
>
> Pairing<-factor(descr[,"Pairing"])
> Condition<-factor(descr[,"Condition"])
> Diet<-factor(descr[,"Diet"])
>
> design<-model.matrix(~ -1 + Diet + Condition + Pairing)
>
> Is that valid?
Theoretically yes.
>
> If I then run this line I get an error message. Can I ignore the
error?
> Or has the lmFit failed?
>
> fit<-lmFit(eset, design)
>
> #(I get this error message: Coefficients not estimable: Pairing6
> Pairing9 )
It's not an error. It is a warning indicating that the design matrix you
specified isn't of full column rank. Simply put, you have more
parameters in your model than you have data to estimate with. What limma
is doing is removing the extra columns of the design matrix and then
fitting the model (along with the warning that you had an over-specified
model).
I'm sort of surprised that model.matrix will create such a design
matrix, but I have to assume there is a good reason.
I don't really understand the pairings you have below. By my count, each
paired sample got the same diet and had the same disease. In which case,
what's up with the paired samples? I assumed you had something like
control and diseased animals that you fed different diets at different
times (or maybe siblings or some other dependence structure), but this
doesn't seem to be the case.
I *was* going to say that you could just compute the differences between
the pairs (diet1 - diet2). Then it would be easy to get the
diet-specific genes for each type (control and disease), as well as the
interaction (genes that react differently to diet depending on the
disease status). But given how you describe the samples below I don't
think that is necessary, nor do I really understand what you are doing.
Best,
Jim
>
> Then I am not sure what contrasts I can make to get what I want. Can
> you suggest to me what would be some sensible choices?
>
> For instance, is this a valid contrast to show the difference between
> the two diets?
> contrast.matrix<-makeContrasts(DietDiet2 - DietDiet1, levels=design)
>
>
> Help appreciated.
>
> Regards
>
> John Seers
>
>
>> colnames(design)
> [1] "DietDiet1" "DietDiet2" "ConditionDisease"
> "Pairing10" "Pairing11" "Pairing12" "Pairing13"
>
> [8] "Pairing14" "Pairing15" "Pairing16"
> "Pairing17" "Pairing18" "Pairing19" "Pairing2"
>
> [15] "Pairing20" "Pairing21" "Pairing22"
> "Pairing23" "Pairing24" "Pairing25" "Pairing26"
>
> [22] "Pairing27" "Pairing28" "Pairing3"
"Pairing4"
> "Pairing5" "Pairing6" "Pairing7"
> [29] "Pairing8" "Pairing9"
>
>
>> Pairing
> [1] 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11
> 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23
> [46] 23 24 24 25 25 26 26 27 27 28 28
> Levels: 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27 28 3
> 4
> 5 6 7 8 9
>
>> Diet
> [1] Diet1 Diet1 Diet1 Diet1 Diet1 Diet1 Diet2 Diet2 Diet2 Diet2 Diet2
> Diet2 Diet1 Diet1 Diet1 Diet1 Diet1 Diet1 Diet1 Diet1 Diet1 Diet1 [23]
> Diet1 Diet1 Diet1 Diet1 Diet2 Diet2 Diet2 Diet2 Diet2 Diet2 Diet2
> Diet2 Diet2 Diet2 Diet2 Diet2 Diet2 Diet2 Diet1 Diet1 Diet2 Diet2 [45]
> Diet1 Diet1 Diet1 Diet1 Diet1 Diet1 Diet2 Diet2 Diet2 Diet2 Diet2
> Diet2
> Levels: Diet1 Diet2
>
>> Condition
> [1] Control Control Control Control Control Control Control Control
> Control Control Control Control Disease Disease Disease Disease
> Disease [18] Disease Disease Disease Disease Disease Disease Disease
> Disease Disease Disease Disease Disease Disease Disease Disease
> Disease Disease [35] Disease Disease Disease Disease Control Control
> Disease Disease Disease Disease Disease Disease Control Control
> Disease Disease Disease [52] Disease Disease Disease Control Control
> Levels: Control Disease
>
>
> ---
> Web sites:
>
> www.ifr.ac.uk
> www.foodandhealthnetwork.com
>
> -----Original Message-----
> From: James W. MacDonald [mailto:jmacdon at med.umich.edu]
> Sent: 12 March 2008 14:59
> To: john seers (IFR)
> Cc: bioconductor at stat.math.ethz.ch
> Subject: Re: [BioC] Paired arrays and limma
>
> Hi John,
>
> john seers (IFR) wrote:
>>
>>
>> Hi James
>>
>> A fortuitous coincidence you mention my name in a posting when I was
>> reading one of your threads the other day and this prompts me to ask
>> you directly.
>>
>> The thread in question is:
>>
>>
>> https://stat.ethz.ch/pipermail/bioconductor/2007-November/020291.html
>>
>>
>> I followed the thread through and found it did not offer a solution
>> and you finish with:
>>
>> "I'm not sure why you would want to do things pair-wise, but if you
>> really want paired t-tests, then you will have to analyze the data in
>> pairs rather than all at once."
>>
>> I am using limma on a similar setup and I am not sure how to pair the
>> data. The setup is before and after two diets and a condition control
>> and disease. (There is a section in the limma manual on paired data
>> and a section on factorial designs but I am not sure how to marry
> them).
>> Can you explain what you mean by "analyzing the data in pairs rather
>> than all at once"?
>
> Well, the poster wanted his data to agree with the results from a
> paired t-test, which won't happen if you fit a model to all the data
> and then compute contrasts. This is because the denominator of the
> statistic will be different in the two cases.
>
> In the former, the denominator will be the standard error of the mean,
> which is computed using only the two samples under consideration.
>
> In the latter, it will be the sums of squares of error (or some
> variant thereof, depending on the model), which measures the
> within-group variance of all the groups in the model, not just the two
> under consideration for a given contrast.
>
> I didn't know why he would want to do things pair-wise, as the
> variance estimates get better as n goes up, so the linear model
> approach is often preferable. You can see that in his example - the
> t-statistic was larger when he used all his data than when he just
used the paired data.
>
>> My solution so far is to preprocess the data and take the ratio of
>> the expression values of the paired arrays (so halving the number of
>> columns) and analyzing them in limma. That removes the pairing from
> the
>> limma analysis. Does that make sense to you?
>
> Well, if by 'take the ratio' you mean 'compute the difference'(we
> _are_ on the log scale?), then this is essentially what you would be
> doing by fitting a batch effect anyway. However, it will limit the
> types of comparisons you can make since you are combining the paired
> data into differences.
>
> Best,
>
> Jim
>
>
>> Thank you in advance for any time you take.
>>
>> Regards
>>
>>
>> John Seers
>>
>>
>>
>> ---
>>
>> Web sites:
>>
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>
--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
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