[BioC] Limma design and contrast matrix question.
Thornton, Matthew
Matthew.Thornton at med.usc.edu
Fri Jan 24 18:55:56 CET 2014
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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--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
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