Small but important differences
Slowly the data are filling up and we start to see some differences emerge between the controls and the aPKC knock-outs:
We still need to get to about N=40, so there is still some way to go.
Quality control reduced number of animals
Going over the optomotor responses with a fine comb revealed a bunch of flies where the algorithm wasn’t able to provide a proper fit for the OMR asymptote. Therefore, I will need more time to finish the data set. Here the current torque-learning PIs:
Clearly, the genetic controls learn while the flies with knocked-out aPKC in FoxP neurons fail to show a significant learning score. However, the OMR asymmetry effect in the genetic controls appears weaker than the one we discovered in WTB flies, as can be seen in the OMR traces after the self-learning:
Then again, at the .05 level, the asymmetry index is significant. Not the alpha level we commonly use, but also a lower N than we strive for (above is before training, below is after):
The transgenic experimental flies, in contrast, don’t seem to show much of an effect at all:
Yaw torque avoidance reference
Just to have an example of yaw torque datasets and how they should avoid:
Passing the halfway mark
Finally have about half the number of flies needed. It looked like the flies that used the FoxP virgins didn’t fly as well as the other flies, so we dropped that branch and have stopped using them for the crosses. Pooling the FoxP>aPKC/CRISPR flies no increases the N in this group:
Adding flies and fixing figures
Added more flies to the aPKC knock-out in FoxP neurons. Now the knock-outs are close to zero, but one of the controls, too. Still too early to say much. The figure looks ok now, but the Bayes Factors get chopped off. need to fix this. As I’m, already working on some figures (histograms) in the code, I can fix this as well.
First data from aPKC knock-out in FoxP neurons
When Andi tested his aPKC CRISPR knock-outs, he used the Tang torque meter setup, where the OMRs aren’t recorded. So I’m attempting to replicate his results and compare OMRs between groups.
It looks like I need to tweak the graph in some way to make the density plots show up. Obviously, N is too low to say anything yet, but I need to work on the plot.
Optomotor graph coded
There had been some concerns about the optomotor display in the group evaluation sheets showing right-turning torque on the left side of the graph and vice versa. Also, the use of standard deviations seemed to blur differences between the experimental groups:
Because of these concerns, I have swapped the traces and used standard error of the means instead of standard deviations:
What do you think? Better or worse? Feedback very welcome!
Finally done: rut and rsh flies better at self-learning
At long last, I got all the flies together that we need for sufficient statistical power. As the preliminary data had indicated, WTB flies don’t learn with the short training, while rut and rsh flies do just fine.
However, this may be due to genetic background effects, so we need to check the CRISPR mutants.
Starting it back up
Last week, the torque mete ran for two days and I managed to record a few radish flies:
