[BioC] Paired arrays and limma
James W. MacDonald
jmacdon at med.umich.edu
Wed Mar 12 21:09:45 CET 2008
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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