Hi all, I am struggling to make a contrast with EdgeR.
design <- model.matrix(~Gender + Age + Group, data = patient.table)
The (disease) Group is a factor with [Control, Mild, Severe]. I want to compare C-M. M-S and C-S. Thus s i
DGEL <- edgeR::DGEList(expressionData)
keep <- edgeR::filterByExpr(DGEL)
DGEL <- DGEL[keep, , keep.lib.sizes=FALSE]
DGEL <- edgeR::calcNormFactors(DGEL, method = "TMM")
DGEL <- edgeR::estimateDisp(DGEL, design)
fit <- edgeR::glmQLFit(DGEL, design)
contrasts <- makeContrasts(c.vs.m = GroupC - GroupM,
m.vs.s = GroupM - GroupS,
c.vs.s = GroupC - GroupS,
levels = design)
However, there is no GroupC in the model matrix. There is only an Intercept, GroupM and GroupS. How can I compare anything against the control group on a full-rank design matrix.
I already searched in the user manual and here on the website, but could not find it. Also, I tried
design <- model.matrix(~Gender + Age + Group,
data = patient.table,
contrasts.arg = list(
Group = contrasts(patient.table$Group, contrasts = FALSE)
)
)
But edgeR did not like that and informed me that the table was not of full rank. I am pretty sure this is something basic, but I just don't get it as a wet-lab worker.
Check edgeR Users Guide, chapter 3.3, on how to define the contrasts for such a complex design. For me, the approach that works better (as in, it is easier to understand) is the one described in section 3.3.1. Start by creating a composite group variable:
group <- factor( paste0( patient.table$Gender, paste0( patient.table$Age, patient.table$Group ) )
Then specify the design without an intercept, and create the contrasts of interest.
design <- model.matrix( ~0 + group )
colnames( design ) <- levels( group )
contrasts <- makeContrasts( GenderM_Age0_GroupC_vs_M = GenderMAge0GroupC - GenderMAge0GroupM,
# and so on for the other contrasts
levels=design)
To perform the tests:
fit <- glmQLFit( DGEL, design )
qlf <- glmQLFTest( fit, contrast = contrastss[ , "GenderM_Age0_GroupC_vs_M" ] )
This tests for GroupC vs GroupM differences within GenderM and Age0.
See Which is the best approach to get DEG across one condition, in a three-factor design experiment? for a recent similar post, you may find the answer there useful.
If you want more complex tests, such as any genes changing over all ages, read sections 3.3.3 and 3.3.4 from the guide.
Hey @h.mon, thanks for the reply. Is it correct that if I only have GroupM and GroupS, that just filling in GroupM or GroupS would be a comparison of the group agains baseline (groupC) ?
Ok, so... Making a design and then comparing COEFs is probably the hardest thing in EdgeR or DE in general. Up to now. I changed my code slightly;
design <- model.matrix(~0 + Group + Gender + Age, data = master.table)
This makes it so that the design has all the groups that I want to compare. Then I use the makeContrasts and glmQLFTest functions to get the result I want (was also incorrect):
contrasts <- makeContrasts(c.vs.s = GroupS - GroupC, m.vs.s = GroupS - GroupM, c.vs.m = GroupM- GroupC, levels = design)
fit.hc.vs.seo <- glmQLFTest(fit, contrast = contrasts[,"c.vs.s"])
Log in to answer this question.
