Hi, I'm working with a data set with three time points (1d, 5d, 10d and four treatments + ctrl treat1, treat2, treat3, treat4). For each of the 15 combinations I have triplicates (in total 45)
If I understood it correctly both edger and deseq2 works with this interactions terms to combine multiple factors (They use different commands, but the interactions are similar).
In this case the full model would be
(~Treat + day + Treat:day) and the reduced model
(~Treat + Time).
To take the example from the edger manual's contrast matrix - what would be the difference between this two contrasts?
DrugvsPlacebo.0h = Drug.0h-Placebo.0h, DrugvsPlacebo.1h = (Drug.1h-Drug.0h)-(Placebo.1h-Placebo.0h),
If I want to test for changes between treat and ctrl for each TP should I use the first contrast and do this (after combining the columns
day from the sample information table:
treat1vsWT.1d = treat1.1d-WT.1d ... treat2vsWT.1d = treat2.1d-WT.1d ... treat3vsWT.10d = treat3.10d-WT.10d ...
which would give me 12 different pair-wise comparisons.
But what is different in the second contrast in the example above?
Another question is what would happen, if I use the given full and reduced model to get this
design <- model.matrix(~Treat + Treat:day, data=sampleInfo)
Now i will have many coefficients. I f I'm looking for genes changing over all time points, I would combine the coefficients into one vector. Let's say I would like to find all genes that significantly changed between the control and treat1 on all days. Would this be the correct syntax?
qlf <- glmQLFTest(fit, coef=c(7,12,17))
Would This give me the genes changed over all time points? Does this mean these genes are significantly changed in all time points independently?