on Monday, July 30th, 2018 2:08 | by Amanda Torres
on Monday, July 23rd, 2018 6:40 | by Christian Rohrsen
Mean trace of the positive control in the Joystick to get to see what are the overall dynamics and maybe to get an idea what might be the best score to pick.Here the standard deviation of the flies along the time axis. This is just to see if all the flies have more similar phenotypes with each other or not at each time.
This is to see if the flies have less degrees of freedom at any segment by measuring the standard deviation at each segment. There does not seem to be any effect. Although this might be mixed with the wiggle scores. I think measuring entropy is a better measure.
All the same plots as above but for TH-D’, the interesting line from the screen.
on | by Christian Rohrsen
This are the performance indices for the different models performed to estimate the valence of the dopaminergic clusters. AIC: Akaike Information Criteria; BIC: Bayesian information Criteria; LogLikelihood: log Likelihood estimation
lm: linear model
+ int: taking double interaccions into consideration
b lm: bayesian linear model with bayesglm function
b lm MCMC: bayesian linear model with MCMCglm function
nlm: nonlinear model with lm function with splines fitted
b nlm: splines fitted to each cluster and MCMCglm function
GAM: general additive model with gam function
Adding double interactions seems to produce better models, nonlinearities also make models better and frequentist also. To me it seems like this data might be noise and therefore adding interactions, nonlinearities and frequentist methods is just fitting the noise better (overfitting) and that is why I get better scores with them. In addition, care needs to be taken since I use different functions that calculate the model performance scores differently (although the formulas are theoretically the same for all!)
on Friday, July 20th, 2018 3:46 | by Christian Rohrsen
This is the correlation from the T-maze experiments from Gaia and Naman. Neither ranked nor regular correlation show any significant effect. This means that these effects seem to be random, at least for most of them, is this an overfitting result?
I would say blue 1 is a line that was negative for all the tests I have so far seen. So this might be an interesting line. What to do next?
I would unblind the blue1, which is TH-D’. It was shown to be required for classical conditionning in shock and temperature learning (Galili et al. 2014). Another interesting observation is that th-g4+th-g80 seems to have like zero PI scores in all of the experiments (Naman and Gaia in the Tmaze, Joystick and Y-mazes). So could it be that all of these neurons have indeed a meaning, but is depending every time in the context?? Maybe Vanessa Ruta´s work might be interesting for that.
on Monday, July 2nd, 2018 2:00 | by Christian Rohrsen
This is the linear model with its statistics
This is the linear model adding interactions. It is perfectly possible to have interactions between neurons, kind of what occurs with olfactory processing where ORNs activated alone or in different combinations have completely different meanings
I uploaded in slack the bayesian linear model with interactions. For any reason, it does not let me upload it now to the website
I am trying one of the ways of nonlinear models: GAM (Generalized additive models). Here one fit splines to the effects to certain degrees of freedom.
This is the kind of bar graphs I thought I could use for all the plots.
on Monday, June 25th, 2018 1:52 | by Christian Rohrsen
Dopaminergic clusters are differently targeted by the different Gal4s. Some of the express faintly, others stronger. Here I try to see if the dose-response curve (or expression-PI curve) seems to be linear or not. Here I put two examples from the 17 clusters, where the first two seem to have nonlinear curves, with and optimal expression level, and the last two seem to have a linear response curve.
This will be important for the modelling in order to decide to make a linear/nonlinear model. Down below I show the results from a linear model and it´s statistics. From Aso et al. 2012, one could see that activating the lines with TrpA1 shows a linear response curve. But in this case it does not necessarily seem to be the case. Therefore, light intensities might have an effect, as well as the expression level, and conclusion needs to be taken carefully.
In addition it is difficult to calculate this for all the clusters with just one single light intensity test, because not all clusters are expressed in several Gal4s to different level, so that we can estimate from there. So for the interesting lines we might need to make several experiments at different intensities, and see the dose response curve.
on Monday, June 18th, 2018 1:55 | by Christian Rohrsen
This is just to show that I am trying to find a new ratio so that all graphs have from -1 to +1 ranges. That is why now the difference in occupancy time is divided by the total time. The same with the speed. Because speed differences are so subtle, the Y axis scale has to be lower
on Monday, November 20th, 2017 2:59 | by Christian Rohrsen
After getting a new spectrometer, we confirmed that the first one was faulty. Comparison of old (1st and 3rd measures) and new (2nd and 4th) spectrometer. Now much more sensitive and the right spectrum measured for the green LED present in the spectrometer itself and for the light comming out of the light guide coupled with the red LED (whose spectrum does not seem to change after travelling through the light guide)
on Monday, July 17th, 2017 2:40 | by Christian Rohrsen
This is the first line I completed from the screen. What it is more important is that I found out that NorpA;UAS-Chrimson has orange eyes, when it should be red. So I think I will start to do the line again and meanwhile test the contaminated line to see what the phenotype looks like