ec-code

Code for the analysis of eurydendroid cells data.

Code is being moved here from this private repo.

Code that has been moved:

• E0050 with responses to motor activity w/ and w/o visual feedback for ECs, PCs, IONs, GCs
• E0022 responses of ECs to different visual stimuli
• Analysis of ephys data from EC recordings

Specify dataset location

To handle compatibility with local vs. FUNES vs. otherwise stored datasets, the master folder that contains all data should be specified in a dataset_location.txt file that contains a single string with the folder, placed in the repo (ec-code/dataset_location.txt). This file is git-ignored (with the .gitignore file) as it has to remain local.

Code conventions

• manually defined parameters for the analysis (time intervals, hardcoded thresholds, etc.) are all defined as UPPERCASE_VARIABLES at the beginning of the scripts;
• Analyses that compute numbers happen in scripts, notebooks are for plotting

Installation with notebook clean hook

To commit notebooks without content, install nb-clean:

python3 -m pip install nb-clean

And in the repo local path:

nb-clean add-filter

Description of the analysis

Here is some general guideline for how to run the analyses for the different projects.

###Visual feedback effect - E0050 (fb_effect)

In this analysis, scripts are in scripts/fb_effect, notebooks in scripts/fb_effect, and utilities in ec_code/fb_effect. The overall concept is to work with information aggregated from all fish in dataframes:

• exp_df: contains info for every experiment (genotype, quality, fish adaptation, etc.)
• cells_df: contains info for every cell from every fish (coordinates, fish, ...); when then performing motor- and sensory- related analysis, additional columns are added with info on motor/sensory responsiveness, etc.
• bouts_df: info for every individual bout from all fish (time, bout stats, fish, etc,)
• trial_df: info for every individual trial from all fish (time, speed, bouts occorred, timing of the bout, fish, etc.)
• traces_df: huge matrix with traces from all cells, with column names corresponding to unique cell indexes

Steps:

• data from all individual fish is pooled together in huge flammkuchen files (using 0_create_dataframes.py)
• some preprocessing on bouts is computed, and dataframes that pool together info for every experiment, bout, trial, cell are created (using 1_preprocess_dataframes.py)
• bouts are matched by duration and an adaptation index is computed for every fish 2_bout_matching_and_adaptation.py
• motor responses are computed (both as amplitudes and reliability scores on different bout types: spontaneous, evoked, cl, ol, etc.) 3_motor_responses.py
• differences in responses to closed loop and open loop bouts are computed for every cell, expressed as a p-value defined over the interval of time of the response of maximum difference between cl and ol:4_cl_vs_ol.py
• responses to sensory stimuli (forward and backward motion, after nanning the bouts, both in amplitude and reliability) are computed for all cells: 5_sensory_resps.py

At this point all numbers are calculated. Some plotting then happens in the notebook motor_responses_plot.ipynb, where a function create the summary panels for a single cell that I reported in the TAC and the labfolder entries. Just filtering cells on their p-value was how I was parsing out the most interesting responses.