Help with Two-way ANOVA in Limma
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tkhodges ▴ 20
@tkhodges-5775
Last seen 7.7 years ago

I was asked to run a 2-way ANOVA using LIMMA with strain (AJ, BALBC, C57BL6, DBA, FVB) and treatment (drug, naive, vehicle) as my factors. They want to know which genes change in expression due to strain, treatment, and the interaction. I ran the two-way ANOVA using treatment-contrast parametrization with C57BL6 naive as my baseline but they have asked me to run it without a reference. However, if I do not specify the reference then it will pick AJ drug as my reference.  The contrasts are different but the F statistics are the same. From reading the limma guide, my understanding is that the factorial 2-way ANOVA needs to have a baseline.  Is it possible to run a 2-way ANOVA without a baseline?  

Thanks in advance for your help, 

Theresa


snippet of script from my original 2-way ANOVA

#Model design
strain <-factor(info$strain)
strain <-relevel(strain, ref="C57BL6")
treatment <-factor(info$treatment)
treatment <-relevel(treatment, ref="N")
design = model.matrix(~strain*treatment)
rownames(design)<-colnames(oExpr)
design

#Model fitting 
fit<-lmFit(oExpr, design)
fit2<-eBayes(fit)

#Differential gene expression

out<-topTable(fit2,number=nrow(fit2), adjust.method="BH", p.value=1, sort.by="none")

limma two factor anova • 1.5k views
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@ryan-c-thompson-5618
Last seen 9 months ago
Scripps Research, La Jolla, CA

What do they mean by running the analysis without a reference? You could re-parametrize the design matrix with no intercept, e.g. using a design of ~0+strain:treatment and then forming the appropriate contrasts, but you would still be doing the same analysis and would get exactly the same p-values and logFC values.

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tkhodges ▴ 20
@tkhodges-5775
Last seen 7.7 years ago

Hi Ryan,

Thanks. Treatment naive (not treatment) is fine but there is no particular genetic background that should serve as the baseline. 

Thanks,Theresa

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(Please reply using the "add comment" button, not the "add answer" box.)

What I'm saying is that the choice of baseline is purely a matter of convenience and has no bearing on the statistics. You can choose any baseline, or use my suggested design to get a separate coefficient for each combination of factor levels with no baseline, but regardless, you will still get the same p-values and same logFC for the same comparisons. So if you already have a design that works, it's a waste of time to change the baseline around in this way. You are still fitting exactly the same model.

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Entering edit mode
tkhodges ▴ 20
@tkhodges-5775
Last seen 7.7 years ago

 Thank you for the additional explanation. I have a much better understanding now. I will give your suggested design a try.  

 

 

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