I was wondering how one can do a deseq2 analysis of multiple time points when trying to test for a WT vs TREAT difference at any of the given times. Let's say I have three time points (1h,2h,3h) and two conditions (WT, TREAT)
In edgeR one can create a contrast matrix and pass the complete matrix to the glm() function.
con<- makeContrasts( TP1.WTvs.T = T1.T - T1.WT, TP2.WTvs.T = T2.T - T2.WT, TP3.WTvs.T = T3.T - T3.WT, levels=design) glmQLFTest(fit, contrast = TP1.WTvs.T) # single TP comparison glmQLFTest(fit, contrast = con) # compare all time points in one go.
With this contrast matrix i can call each contrast separately to detect genes differentially expressed in each of the single time-points or to compare changes in all TP.
I'm interested to understand how this can be done using the
I know i can use the interaction terms after running the
results() command. something like:
resultsNames(ddsTC) ##  "Intercept" "strain_T_vs_WT" "time_TP1_vs_TP0" "time_TP2_vs_TP0" ##  "..." "..." "..." "strainT.timeTP1" ##  "strainT.timeTP2" ...
results(dds, name="time_TP2_vs_TP0") I can detect all genes in WT whcih ae changed between
The call for
results(dds, name="strainT.timeTP1") will test if the Treat vs WT fold change is different at
TP1 than at
Where i'm not sure is by following this logic, how can I do the same I did with
How do I identify genes which are changed across all three time points?