I am doing some RNA-seq analysis using DESeq2 and following the vignette from here, where they use time points 0,15,30,60 ... 180 min. and two groups (strains)
In the time series chapter it says:
"Genes with small p values from this test are those which, at one or more time points after time 0 showed a strain-specific effect."
As an example the following is shown:
## log2 fold change (MLE): strainmut.minute180 ## LRT p-value: '~ strain + minute + strain:minute' vs '~ strain + minute' ## DataFrame with 4 rows and 7 columns ## baseMean log2FoldChange lfcSE stat pvalue padj symbol ## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <character> ## SPBC2F12.09c 174.6712 -2.65763737 0.7498270 99.23199 7.671942e-20 5.184698e-16 atf21
As I am understanding it- this gene has a strain specific expression profile over time. This effect occures over the other time points (from 0 to 180).
If I had picked the
strainmut.minute60 then we would have seen a list of DE genes from 0 to 60 minutes, right?
Just for clarification- here are the options of which one can choose:
resultsNames(ddsTC) ##  "Intercept" "strain_mut_vs_wt" "minute_15_vs_0" "minute_30_vs_0" ##  "minute_60_vs_0" "minute_120_vs_0" "minute_180_vs_0" "strainmut.minute15" ##  "strainmut.minute30" "strainmut.minute60" "strainmut.minute120" "strainmut.minute180"
Imagine a gene that behaves in the same way in all timepoints (between the two groups) but time point 0 to 15.
Will this be detected by deseq?