Confused about the design matrix in edgeR: How to tell R to consider one level as a reference and the other is a treatment to be compared to that ref.
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Mohamed • 0
@aa1ae679
Last seen 9 hours ago
United Kingdom

This question relates to my previous post (Advice on designing an EdgeR object in complex experimental setup ?)

I have an experimental design like in this image it aims to compare treated Vs control, adjusting for animals (sex + individual) l

I first created a data frame for the two groups (Animals) and (Status):

Animal <- factor(paste(Sample_info$Individual, Sample_info$Breed, sep = '.'))
Status<-factor(Sample_info$Status, levels = c('Treated', 'Control'))

Then I made the 'design matrix'

design <- model.matrix(~Animal+Status)

which produced a design matrix that looks like this design matrix.

My question is why the design matrix named its last column ' StatusControl' and not 'statustreated' ? this caused it to label the control samples as 1 and treated as 0, which I assume that this told the program to consider the treated groups as a 'reference' and control group as 'comparison' group so that all my genes (which in the count was higher in treated samples than in control) appeared as downregulated ? It should be the reverse..... So I want to tell R to consider the control as a 'reference group' and I think label it as 0 (not 1). How can I do that ?

This was my DE code :

fit<- glmQLFit(y, design, robust = TRUE)
tr <- glmQLFTest(fit, coef = "StatusUnstimulated")    

I would be appreciating any answer !

R edgeR • 180 views
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@steve-lianoglou-2771
Last seen 1 day ago
United States

My question is why the design matrix named its last column ' StatusControl' and not 'statustreated' ?

It's because the Intercept is the first level of the factor. If you define your factor like so:

Status <- factor(Sample_info$Status, levels = c('Control', 'Treated'))

You'll get what you're after.

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Thanks a lot, will try this and see.

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When I answered your original question, I assumed you would use the simplest possible code

Status <- factor(Sample_info$Status)

which would have given the correct result. By default, the factor levels are ordered in alphabetical order, and "Control" comes before "Treated" alphabetically.

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