I have a situation where I have a factoral independant variable 'suicide' with three levels 'non_suicide', suicide, and 'unkown'. I have been setting up my analysis thus:

suicide.non.undet <- as.factor(df$suicide) CauseofDeath.recode <- as.factor(df$CauseofDeath) Sex <- as.factor(df$Sex) Smoking <- as.factor(df$Smoking) Ethanol <- as.factor(df$Ethanol) svseq1 <- as.numeric(df$svseq1)

design1 <- model.matrix(~ suicide + CauseofDeath +Sex +Smoking + Ethanol +svseq1)

Because control or 'non_suicide' is the lowest level it does not appear as a column in my design matrix. Instead, it should be the intercept. 'Suicide' cases appear as the second column in my design matrix. So I have been performing my regression using coef=2:

dge.counts <- DGEList(counts=counts )
y1 <- calcNormFactors(dge.counts, method="TMM")
y1 <- estimateDisp(y1, design1)


fit.1 <- glmQLFit(y1,  design1, robust=TRUE)
result.1 <- glmQLFTest(fit.1, coef=2)

The results I get from this are reasonable, looking at the qqplot of pvalues.

However, because i have three levels in my independant variable, and I like to cut, paste, and re-work code from one project to the next as needed, I decided it would be safer to specify the exact contrast I was performing using the makeContrasts method, to prevent future me from accidentally running the wrong contrast if the order of the levels of a future project didn't set the control as the intercept. So i did the following:

contrast2 <- makeContrasts(suicide - intercept , levels=design1)
fit.2 <- glmQLFit(y1,  design.1, robust=TRUE)
result2 <- glmQLFTest(fit.2, contrasts=contrast2)

Doing things this way I got a drastically different result with a very different pvalue qqplot (pvalue histogram shows a massive shift to 'significant' pvalues).

My question is this, I know I am doing something wrong here, but what? I got the same result when I tried setting up my design matrix:

design2 <- model.matrix(~ 0 + suicide + CauseofDeath+Sex +Smoking + Ethanol +svseq1)

and specifying the contrasts as

contrast3 <- makeContrasts(non_suicide - suicide , levels=design2)

Shouldnt the coef=2 have been essentially the same as contrasting suicide to the intercept?