I have a question regarding doing time course analysis with (bulk) RNASeq data.

Currently, I have wildtype vs. Condition at Day Zero (undifferentiated), Day 7, Day 14, Day 21, and Day 28.

Using this Vignette, I used the following code to find a list of genes that had a condition-specific effect at any given time:

ddsTxi <- DESeqDataSetFromTximport(txi, 
                                   colData = sample_colData,
                                   design = ~ condition + age + condition:age)
ddsTxiTC <- DESeq(ddsTxi, test="LRT", reduced = ~ condition + age) #test for interaction term between condition/age

#list of genes that are significantly different at different time points
resTC <- results(ddsTxiTC)

Following the vignette, am I correct in interpreting that by looking at the result name "conditionProband.ageDIV7", I am comparing Wildtype Day7 VS Condition Day 7, or is there a different comparison being made?

If I can't look at those specific result names, and I wanted to compare wildtype vs condition at every time point, would I need to subset the data to compare those 2 specifically, or is there a clever way to set contrasts?

resultsNames(ddsTxiTC)
 [1] "Intercept"                    "condition_Proband_vs_Control" "age_DIV7_vs_Undiff"           "age_DIV14_vs_Undiff"         
 [5] "age_DIV21_vs_Undiff"          "age_DIV28_vs_Undiff"          "conditionProband.ageDIV7"     "conditionProband.ageDIV14"   
 [9] "conditionProband.ageDIV21"    "conditionProband.ageDIV28"   

resDIV7 <- results(ddsTxiTC, name="conditionProband.ageDIV7", test="Wald")

Also, would the superset of the list of DE genes at each time point for the comparison above be equivalent to the list of genes that had a condition-specific effect (from the LRT)?

Lastly, I thought an interesting follow up might be to categorize the genes with species/condition specific differences by their behavior (e.g. those that are always higher in condition at every time point, those that are higher in the first few time points, before becoming similar, etc.)

I was considering clustering the genes' differences in mean counts (generated from plotCounts). Would there be another suggested way within DESeq2?

Thank you so much.

I am comparing Wildtype Day7 VS Condition Day 7, or is there a different comparison being made?

yes.

Remember, from the workflow, we say: "Keep in mind that the interaction terms are the difference between the two groups at a given time after accounting for the difference at time 0."

Also, would the superset of the list of DE genes at each time point for the comparison above be equivalent to the list of genes that had a condition-specific effect (from the LRT)?

Roughly, yes, although the power for these tests is slightly different so it's not exactly identical. The LRT is preferred.

Lastly, I thought an interesting follow up might be to categorize the genes with species/condition specific differences by their behavior (e.g. those that are always higher in condition at every time point, those that are higher in the first few time points, before becoming similar, etc.) I was considering clustering the genes' differences in mean counts (generated from plotCounts). Would there be another suggested way within DESeq2?

In the workflow we clustering by the coefficients. We say,

"We can furthermore cluster significant genes by their profiles. We extract a matrix of the log2 fold changes using the coef function. Note that these are the maximum likelihood estimates (MLE). For shrunken LFC, one must obtain them one coefficient at a time using lfcShrink."

Both are fine, but we have code for the MLE in the workflow.