Adding demographic information to respirometry analysis

Just popping in here to say I updated my respirometry analysis to properly account for the impact of various demographic variables! Modifications done in this script.

Notes:

• Statistical output found here
• No demographic variables were significant
• I……forgot to do a multiple test correction when I was originally looking at the impact of experimental variables on oxygen consumption. Turns out when you do the necessary corrections, only temperature is a significant predictor of oxygen consumption! Not day, and not the interaction between temperature and day. This is different than the 2022 experiment where temperature and day were both significant. I think the main difference here is that the 25ºC condition wasn’t eliciting as stressful of a response as the 30ºC condition did. There’s a slight increase in oxygen consumption between days 7 and 14, and a slight difference in variation in consumption at day 14, but other than that things are pretty consistent.
• No impact of genotype variables on oxygen consumption. This makes me sad because you’d think with HIF-1a mutations we’d see something going on! But perhaps there’s more to be studied at the molecular level.

Guess it’s time to open up the new molecular datasets!

Going forward

1. Revise respirometry analysis
2. Preliminary metabolomics analysis
3. Preliminary RNA-Seq analysis
4. Examine HOBO data from 2023 experiment
5. Demographic data analysis for 2023 paper
6. Start methods and results of 2023 paper