1. How to analyse the data from the MIC screen
   1. process_freely_walking_data
      1. Inputs
      2. Outputs
      3. Description of process_data_features
      4. Processing of other protocols
   2. process_screen_data
      1. Inputs
      2. Outputs
   3. Making p-value heatmap using make_summary_heat_maps_p27

How to analyse the data from the MIC screen

process_freely_walking_data

Inputs

Requires string of the date for which you want to analyse the data. It will process all of the data from experiments conducted with any protocol that are within that day.

Runs the function process_data_features

Outputs

• Exports a text file of the number of flies ran per protocol and per strain. [TODO: name of text file]
• Results .mat file
  • Contains: [LOG, feat, trx, comb_data, n_fly_data]
  • per vial
• Figures:
  • Acclim timeseries
  • Feat_overview timeseries
  • Timeseries per behavioural metric per vial.

Description of process_data_features

Processes the tracked data from FlyTracker.

Loads: - LOG (metadata about the experiment.) - feat (from FlyTracker) - trx (from FlyTracker)

• Runs the function make_overview which generates a plot of histograms of the general behaviour of the flies over the entire length of the protocol.
• Runs the function plot_all_features_filt which generates a plot of timeseries data for all flies over the entire length of the protocol. fv_data, av_data and dist_data.
• Runs the function plot_all_features_acclim which generates a plot of timeseries data for all flies during the 5 minutes of acclimatisation in the dark. fv_data, av_data and dist_data.

The data from all flies is combined into an easier to manipulate ‘struct’ called DATA through the function comb_data_one_cohort_cond

• Runs the function plot_allcond_onecohort_tuning which generates a [(n_conditions/2) x 2] subplot figure of the timeseries data during each condition (mean+SEM of all the flies in the vial) as well as tuning curves per condition.

Processing of other protocols

Data from protocol_30 and protocol_31 run the functions plot_errorbar_tuning_curve_diff_contrasts and plot_errorbar_tuning_diff_speeds, respectively. Which plot tuning curve plots in addition to the timeseries plots.

• Runs the function generate_movie_from_ufmf which makes individual movies each rep of each condition per experiment.

Data from protocol_25 - individual flies used the script single_lady_analysis.m.

process_screen_data

This function works by using the .mat results files that were generated after running process_freely_walking_data. It heavily relies upon the structured format of the data into the same ‘DATA’ struct created by comb_data_one_cohort_cond but instead uses comb_data_across_cohorts_cond to generate the structure across all flies from multiple cohorts.

• Runs the function comb_data_across_cohorts_cond to generate the struct DATA.
• Runs the funciton plot_allcond_acrossgroups_tuning to create [(n_conditions/2) x 2] subplot figures for each strain versus the empty split control flies. It creates 5 of these subplot figures per strain, one for each data type [‘fv_data’, ‘av_data’, ‘curv_data’, ‘dist_data’, ‘dist_data_delta’].

Inputs

• String of the protocol e.g. ‘protocol_27’
• ‘.mat’ results files from process_data_features.

Outputs

• 5 x figures per strain (timeseries per condition)
• Text file and 2 plots of the number of vials per strain and the number of flies per strain.

Making p-value heatmap using make_summary_heat_maps_p27

This function generates a red-blue heatmap of the p-value for different behavioural metrics across each condition compared to the empty-split control flies.

• It combines all of the data for protocol_27 in the same way as before using the function comb_data_across_cohorts_cond.
• It then uses the function make_pvalue_heatmap_across_strains to generate arrays of all of the p-values.
• A False-Detection Rate adjustment is performed using fdr_bh.
• The data is plotted altogether using the function plot_pval_heatmap_strains.