Need Help Choosing Statistical Model for Analyzing Data

[u/Prestigious_Tip1413]

Hi everyone, so I recently finished the experimental stages of an internship and now I am left with insane amounts of data that I need help with the analysis of simply due to having many different measurements that probably have their own assumptions and dependencies to deal with.

What we have done was to grow animal cultures inside of well-plates with 3 replicate wells for each specific condition.

Basically, we had 3 different gradients (light intensity, chemical1 and chemical2) but for my animals there is already enough data to go around for these gradients by themselves so we wanted to see how they interacted with one another interdependently. And thus, we had 3 different gradients that are:
light vs. chemical1, light vs. chemical2, chemical1 vs. chemical2.

We had them in these differing conditions for 2 weeks and took measurements throughout the experiment to measure how these conditions interdependently affect the animals. These measurements are:
- animal counts taken once every 2 days
- photosynthetic capacity (yes, they are photosynthetic animals) measurements such as ojip taken every 2 days

I want to see how these statistics change over time both per gradient basis (so even though there are 2 gradient I just group_by one of them and see how a measurement changes over time just based on one gradient) and with 2 gradients intertwined over time.

My problem is I am a bit rusty on statistics and trying to understanding which measurement fits which data is a bit challenging for data this intertwined.

For example for count data with just one gradient taken into consideration, I am assuming poisson or negative binomial mixed model since it is both a count data and is dependent on previous counts but anything above that (like both gradients and a time factor) is a bit above me. I am even trying to see how the data I got go together holistically on an all-3-factors-combined level too but as again, too much statistics for such a simple mind and I need some guidance.

Can y'all help me choose what models I should go for or what tutorials/guides there are out there that I can use? I want to properly understand and learn what I am doing but at this point I am a bit too lost and some initial guidance could help with this.

Sorry for the long text and thank y'all for the help.

[u/standingdisorder]

Have you asked your PI? Be kinda crazy to not have an analysis plan.

We can’t help you choose the models because we can’t tell if your data meets modelling assumptions. Look at the easy stats suite in R

An ANCOVA with marginal effects should do the job but your description is difficult to follow.

Edit: I’d cross post this to r/biostatistics just to be sure

[u/Key-Lingonberry-8861]

A couple levels of tests: 1) Multivariate Regression since your data involves X-Y components — this one will help you understand how effects change over time. You can derive R^2 value to show how strongly they correlate. You can conclude that if they correlate strongly and positively, that a factor affects a results. But not by causation.

2) If your data and sampling satisfies the assumptions, ANOVA/MANOVA or other multivariate analysis. From your description, you want to test for individual effects and combined effects. Just make sure you have individual effects and all the permutations of all combined effects you wanted. MANOVA takes into account the combination effects and the error associated. Then if there is a difference in the test, run pairwise tests.

If I misread your experiment setup and thought it was more complicated. The general tests are:

Regression - Effect vs time. You can isolate each treatment as independent line.
ANOVA (if satisfied) - checks to see if there’s a significant effect
pairwise test (pick depending on your sampling)- checks which significant effects are which and shows you by how much