[BioC] Limma design and contrast matrix question.
James W. MacDonald
jmacdon at uw.edu
Fri Jan 24 19:28:55 CET 2014
Hi Matt,
Yeah, I don't know if there is much about treat in the limma User's
Guide or not. There is some info in the eBayes help page (?treat will
get you there), and there is a paper
(http://bioinformatics.oxfordjournals.org/content/25/6/765.short).
Usage is pretty straightforward. You just use treat() and topTreat() in
lieu of eBayes() and topTable(). The basic idea is that the alternate
hypothesis you are testing is that the absolute difference between the
mean of two groups is greater than some quantity (rather than the
conventional alternate hypothesis, which is that the absolute difference
is greater than zero).
Note that the addition of a threshold to the alternative hypothesis
tends to make it a pretty big hurdle, so I wouldn't go crazy with the
lfc argument. In other words, it seems pretty common for people to add a
post hoc 1.5 or 2-fold change requirement (e.g., topTable(fit2, lfc =
1)). That tends to be a much less conservative test than using treat
with an lfc = 1 argument, so you will likely be better served by using
the smallest log fold change that the biologists think is plausibly
linked to a phenotypic difference.
Best,
Jim
On 1/24/2014 12:55 PM, Thornton, Matthew wrote:
> Hi James,
>
> Thank you for your reply!
>
>> Not really. This is the point at which I start the experimental design
> grilling session. Why did you do the combination treatment? What did you
> expect to see (e.g., what is your hypothesis that you are testing)?
>
> Yes, we are expecting synergy between the treatments. I am simply processing the data, I didn't isolate the cells or process the samples. I have intentionally kept vague as to the exact details of the experiment, to prevent inadvertent bias. I know that there are two treatments, a combination of the two treatments and a control. I also know which group is which. I am looking for differences between each treatment versus control, the differences between combination and each individual treatment. There is a dramatic effect seen in vivo from the combination, but not so much from either treatment separately, so they do expect larger changes in gene expression for the combination than each individual treatment.
>
>> Do you expect synergistic effects? If so, by how much? You could use
> treat() to see if you reliably get a particular fold change increase in
> group 2 versus both group 3 and group 4 (or the mean expression of those
> two treatments). But how you analyze that combination treatment is
> dependent on the hypothesis you are testing.
>
> I looked in the Limma Users guide for more information on the treat() function and there isn't very much info, or more importantly for me, an example. I would have liked to use the treat() function with a lfc, based on the observed log fold change from the ERCC controls. Is there a better resource for the treat() function?
>
> Thank you again!
>
> Sincerely,
>
> Matt
>
>
>
> Matthew E. Thornton
>
> Research Lab Specialist
> Saban Research Institute
>
> USC/Children’s Hospital Los Angeles
> 513X, Mail Stop 35
> 4661 W. Sunset Blvd.
> Los Angeles, CA 90027-6020
>
> matthew.thornton at med.usc.edu
>
> ________________________________________
> From: James W. MacDonald [jmacdon at uw.edu]
> Sent: Friday, January 24, 2014 7:07 AM
> To: Thornton, Matthew
> Cc: bioconductor at r-project.org
> Subject: Re: [BioC] Limma design and contrast matrix question.
>
> Hi Matt,
>
> On 1/23/2014 7:13 PM, Thornton, Matthew wrote:
>> Hello,
>>
>> I am analysing microarray data collected with Affymetrix MouseGene 2.0 ST chips. I have a few questions about properly using limma. I have four groups with three replicates. The groups are Control, Treatment #1 & #2, Treatment #1, and Treatment #2. I may not have the proper design matrix. I am not familiar with their use in linear regression. Currently, my design matrix is set up like this:
>>
>> # Design matrix for Limma
>> design <- model.matrix(~ 0+factor(c(1,1,1,2,2,2,3,3,3,4,4,4)))
>> colnames(design) <- c("Control", "Group1", "Group2", "Group3")
>>
>>> design
>> Control Group1 Group2 Group3
>> 1 1 0 0 0
>> 2 1 0 0 0
>> 3 1 0 0 0
>> 4 0 1 0 0
>> 5 0 1 0 0
>> 6 0 1 0 0
>> 7 0 0 1 0
>> 8 0 0 1 0
>> 9 0 0 1 0
>> 10 0 0 0 1
>> 11 0 0 0 1
>> 12 0 0 0 1
>> attr(,"assign")
>> [1] 1 1 1 1
>> attr(,"contrasts")
>> attr(,"contrasts")$`factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4))`
>> [1] "contr.treatment"
>>
>> This was modified directly from the limma users guide page 36. Should the control group be all 1's? and should Group1 (treatment 1 & 2) be 1's from row 7:12? I would like to find genes different from control and I would like to find genes differentially expressed between the combination of treatments versus each treatment alone.
> You could parameterize your model that way, but I would personally keep
> it the way you have it. This parameterization (a cell means model)
> simply computes the mean expression for each group, and then you have to
> make all contrasts explicitly. The model with all 1's in the first
> column has an implicit contrast (everything is a comparison to control),
> so you have to make contrasts for some comparisons, but not for others.
>
> Either way you will get the same exact results - the only difference is
> the interpretation of the coefficients.
>
>> My Contrasts matrix is set up like this:
>>
>> # Limma contrast matrix more than 5, no Venn diagrams.
>> contrast.matrix <- makeContrasts(Group1-Control, Group2-Control, Group3-Control, Group3-Group2, Group3-Group1, Group2-Group1, levels=design)
>>
>>> contrast.matrix
>> Contrasts
>> Levels Group1 - Control Group2 - Control Group3 - Control Group3 - Group2
>> Control -1 -1 -1 0
>> Group1 1 0 0 0
>> Group2 0 1 0 -1
>> Group3 0 0 1 1
>> Contrasts
>> Levels Group3 - Group1 Group2 - Group1
>> Control 0 0
>> Group1 -1 -1
>> Group2 0 1
>> Group3 1 0
>>
>> Is there a better way to relate the fact that Group 2 is a combination of treatment 1 and treatment 2?
> Not really. This is the point at which I start the experimental design
> grilling session. Why did you do the combination treatment? What did you
> expect to see (e.g., what is your hypothesis that you are testing)?
>
> Do you expect synergistic effects? If so, by how much? You could use
> treat() to see if you reliably get a particular fold change increase in
> group 2 versus both group 3 and group 4 (or the mean expression of those
> two treatments). But how you analyze that combination treatment is
> dependent on the hypothesis you are testing.
>
> Best,
>
> Jim
>
>
>
>
>> Thanks!
>>
>> Matt
>> Matthew E. Thornton
>>
>> Research Lab Specialist
>> Saban Research Institute
>>
>> USC/Children’s Hospital Los Angeles
>> 513X, Mail Stop 35
>> 4661 W. Sunset Blvd.
>> Los Angeles, CA 90027-6020
>>
>> matthew.thornton at med.usc.edu
>>
>> _______________________________________________
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>> Bioconductor at r-project.org
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> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099
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