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

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?

• Just to be clear, I'm mainly interested in the overall question of how to treat the data, rather than the intricacies of comparing gene -expression. He has other data on protein levels, length of roots etc that he'd also like to look at. – Andrew Apr 5 '12 at 7:48

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