I have done a DESeq2 analysis using the package's vignette, anyway I have a some points I cant understand: I have 2 different conditions (wt vs mut) and 7 different time points (T0 to T6)
I obtained the genes with greatest differences over the time points, ordered by adj. p-value:
ddsTC = DESeqDataSetFromTximport(txi, design = ~ condition + timepoints + condition:timepoints, colData = dfSamples) ddsTC = DESeq(ddsTC, test="LRT", reduced = ~ condition + timepoints) resTC = results(ddsTC) resTC = resTC[order(resTC$padj, decreasing=F),]
Then, do I obtain the genes with greatest differences between T1 with respect to T0 using commands? And what exactly it means? Are these the genes which have a similar expression at T0 and an increased difference at T1 or are these the genes with an high difference both at T0 and T1?
resultsNames(ddsTC)  "Intercept" "condition_mut_vs_wt"  "timepoints_t1_vs_t0" "timepoints_t2_vs_t0"  "timepoints_t3_vs_t0" "timepoints_t4_vs_t0"  "timepoints_t5_vs_t0" "timepoints_t6_vs_t0"  "conditionmut.timepointst1" "conditionmut.timepointst2"  "conditionmut.timepointst3" "conditionmut.timepointst4"  "conditionmut.timepointst5" "conditionmut.timepointst6" resTC_t1vst0 = results(ddsTC, name="timepoints_t1_vs_t0")
In case I would compare samples at T0 as done in a classical analysis (3wt vs 3mut) is there a faster way instead of load with only one time point with tximport ?