Change in gene expression over time for different environments when each individual can only be sampled once?

Question

A colleague has collected data on plant root gene expression. Gene expression is expected to change over time, and be dependent upon the environment that each plant is grown in (i.e fertilizer mix). Measuring gene expression kills the plant, so each plant can only be sampled once. My colleague has two groups (high vs low fertilizer) and plans to sample different plants at 3 time points (day 5, day 10 and day 15).

In the past when we've looked at plant growth we've measured height at intervals for each plant and then used a multilevel longitudinal model. But that seems to need multiple measurements for each individual which isn't possible here (we can only make one measurement per individual). So how would you go about analysing the results from this experiment? Or would you design it differently in the first place?

Answer 1

One very straightforward way to do it would be: $Gene=\alpha+\beta_1Groups+\beta_2Time+\epsilon$

Since they are different plants, we can assume them to be independent unlike your earlier experiment which requires multilevel analysis. You can also add other variables like protein levels, etc in the model. Time may be treated as a dummy variable; but I think that really depends on your specific knowledge of the field which I have none.