Hello, this is probably a very simple point, but I want to make sure I understand this. In the DESeq2 ?results document it lists the following example (and comment):
## Example 2: two conditions, two genotypes, with an interaction term
dds <- makeExampleDESeqDataSet(n=100,m=12)
dds$genotype <- factor(rep(rep(c("I","II"),each=3),2))
design(dds) <- ~ genotype + condition + genotype:condition
dds <- DESeq(dds)
resultsNames(dds)
# Note: design with interactions terms by default have betaPrior=FALSE
# the condition effect for genotype I (the main effect)
results(dds, contrast=c("condition","B","A"))
QUESTION:
1. shouldn't what is called "the condition effect for genotype I (the main effect)" be called the simple effect for condition on genotype I?? The term "main effect" I am not sure is appropriate here as we are not averaging over all genotypes.
2. If (1) is correct, how would one specify on DESeq2 a real main effect of B vs A averaged over all genotypes?
Thank you,
Ramiro
## Example 2: two conditions, two genotypes, with an interaction term
dds <- makeExampleDESeqDataSet(n=100,m=12)
dds$genotype <- factor(rep(rep(c("I","II"),each=3),2))
design(dds) <- ~ genotype + condition + genotype:condition
dds <- DESeq(dds)
resultsNames(dds)
# Note: design with interactions terms by default have betaPrior=FALSE
# the condition effect for genotype I (the main effect)
results(dds, contrast=c("condition","B","A"))
QUESTION:
1. shouldn't what is called "the condition effect for genotype I (the main effect)" be called the simple effect for condition on genotype I?? The term "main effect" I am not sure is appropriate here as we are not averaging over all genotypes.
2. If (1) is correct, how would one specify on DESeq2 a real main effect of B vs A averaged over all genotypes?
Thank you,
Ramiro