• D_raw_unfilled - [3 x 1] struct array containing raw, unfilled, unmerged behavioral data
• D_zscored_unfilled - [3 x 1] struct array containing z-scored, unfilled, unmerged behavioral data
• D_als_filled - [3 x 1] struct array containing z-scored, ALS infilled, unmerged behavioral data
• D_als_filled_merged - [3 x 1] struct array containing z-scored, ALS infilled, merged behavioral data

• data - [nFlies x nBehaviors] behavior data from the decathlon assays
• fields - [nBehaviors x 1] behavior labels with format: Assay metric (day)
• n - [nBehaviors x nBehaviors] sample size for pairwise comparisons of behavior
• imputed - [nFlies x nBehaviors] logical array indicating ALS infilled data points
• meta - struct of experimental meta data for each data point and metric

• D_us - [2 x 1] unsupervised behavior data for inbred (1) and outbred (2) flies

• pdfs - [nFlies x nModes] probability density functions of behavioral modes for individual flies
• pdf_labels - [nModes x 1] labels for mode probabilities
• trans - [nFlies x nModes²] transition probabilities between modes for individual flies
• trans_labels - [nModes² x 1] transition probability labels with format: P(mode₀,mode₁)
• strain - [nFlies x 1] array of experimental group labels (inbred = BK-iso, outbred = Nex)
• ID - [nFlies x 1] fly ID numbers

• D_seq - [3 x 1] RNAseq reads and meta data for inbred (1-2) and outbred (3) flies

• data - [nGenes x nFlies] raw read counts of each gene for individual flies
• geneID - [nGenes x 1] cell array of flyBase gene IDs
• ID - [nFlies x 1] array of fly ID numbers

• D_thermo - struct of all screen data

• gal4 - [nGenotypes x 1] cell array of Gal4 driver lines
• effector - [nGenotypes x 1] cell array of UAS-effectors
• clumpiness - struct of clumpiness scores (nBlocks x nFlies) broken down by experiment block
• switchiness - struct of switchiness scores (nBlocks x nFlies) broken down by experiment block
• speed - struct of speed scores (nBlocks x nFlies) broken down by experiment block
• bouts - struct of movement bout clumpiness scores (nBlocks x nFlies) broken down by experiment block

1. Stable low/permissive temp. (1hr)
2. Ramp up low to high temp. (1hr)
3. Stable high/restrictive temp. (1hr)
4. Ramp down high to low temp. (1hr)

• D_babam - struct of BABAM Gal4 screen behavioral data
• D_dgrp_behavior - struct of DGRP behavioral data
• D_dgrp_phys - struct of DGRP physiological data
• DN_ctl - struct of control (unstimulated) descending neuron screen behavioral data
• DN_exp - struct of experimental (stimulated) descending neuron screen behavioral data

• data - [nLines x nBehaviors] array of line average behavioral measures
• fields - [nBehaviors x 1] cell array of behavior labels

• data - [nLines x nBehaviors] array of line average behavioral measures
• fields - [nBehaviors x 1] cell array of behavior labels
• imputed - [nLines x nBehaviors] logical array indicating values imputed by ALS infilling

• lines - [nLines x 1] cell array of line IDs
• DNs - [nLines x 1] cell array of descending neurons affected by each line
• on_pdfs - [nLines x 1] cell array of individual fly pdfs (nFLies x nBehaviors) for each line (stimulated)
• off_pdfs - [nLines x 1] cell array of individual fly pdfs (nFLies x nBehaviors) for each line (unstimulated)
• line_avg_on_pdfs - [nLines x nBehaviors] per line average PDF of behavioral modes (stimulated)
• line_avg_off_pdfs - [nLines x nBehaviors] per line average PDF of behavioral modes (unstimulated)

• D_rnaseq_models - struct of rnaseq gene by behavior linear model p-values and labels
• D_enrichment_results - struct bootstrapped KEGG pathway enrichment analysis results

• pvals - cell array of model p-values, each element [nGenes x nBehaviors]
• fbgn - [nGenes x 1] cell array of FlyBase gene numbers
• kegg - [nGenes x 1] cell array of KEGG gene IDs
• gene_symbols - [nGenes x 1] cell array of gene symbols
• gene_names - [nGenes x 1] cell array of FlyBase gene names
• metric_labels - [nBehaviors x 1] cell array of behavior metric labels

• experiment - experimental batch name
• isogenic - boolean indicating whether batch was isogenic
• prob_gene_given_cat - [nPathways x nGenes] array of the combined probability of a gene and pathway both being significant
• cat_labels - [nPathways x 1] KEGG pathways significantly enriched in observed data
• gene_kegg_ids - [nGenes x 1] KEGG gene IDs associated with any significantly enriched pathway
• gene_labels - [nGenes x 1] gene names associated with any significantly enriched pathway
• gene_fb_gene_num - [nGenes x 1] cell array of FlyBase gene numbers associated with any significantly enriched pathway
• avg_min_pval - [nPathways x 1] -log10[minimum p-value] of enriched KEGG pathways
• shuffled_avg_min_pval - [nPathways x 1] -log10[minimum p-value] of enriched KEGG pathways in shuffled data
• avg_metrics_hit - [nPathways x 1] average number of metrics across bootstrap replicates associated with enriched pathways
• cat_id - [nPathways x 1] KEGG pathway ID numbers
• cat_genes - [nPathways x 1] cell array of lists of complete KEGG gene IDs associated with each enriched pathway (including those without significant gene by behavior model)
• p_metric - [nPathways x nBehaviors] array of probability of pathway both being associated with a behavior
• metric_labels - [nBehaviors x 1] behavioral metric labels
• apriori_cat_x_gene - [nAprioriGroups x 1] cell array of heatmaps of combined probability of a gene and pathway both being significant split by a priori groups.

Description: load decathlon data structs from file decathlon_final_data.mat

Inputs:

• fdir - parent directory containing decathlon_final_data.mat
• var_name - variable name of struct to load (optional)

Outputs:

• D - decathlon data struct containing behavioral data, behavior names, and experimental meta data

Variable Names:

• D_raw_unfilled - [3 x 1] struct array containing raw, unfilled, unmerged behavioral data
• D_zscored_unfilled - [3 x 1] struct array containing z-scored, unfilled, unmerged behavioral data
• D_als_filled - [3 x 1] struct array containing z-scored, ALS infilled, unmerged behavioral data
• D_als_filled_merged - [3 x 1] struct array containing z-scored, ALS infilled, merged behavioral data

Description: Permutes the field order of decathlon data structs to match and (optionally) collapses metrics in each struct into distilled form via PCA.

Inputs:

• D - array of decathlon data structs
• varargin - Name-Value pair options to specify how to pair and collapse fields.

Outputs:

• D_paired - array of paired/collapsed decathlon data structs

Name-Value Pairs:

• 'CollapseFields' - set fields to collapse into distilled metrics [ 'none' (default) | 'circadian' | 'all' ]
• 'CollapseMode' - set method of collapsing metrics [ 'PCA' (default) | 'average' ]
• 'Trim' - toggle trimming day of testing from field names [ false (default) | true ]
• 'PCs' - number of PCs to collapse each a priori group [ positive scalar or array (nGroups x 1) ]

Description: Infill missing values of decathlon behavioral matrices.

Inputs:

• D - array of decathlon data structs
• varargin - Name-Value pair options to specify imputation method and standardization.

Outputs:

• D_impute - array of infilled decathlon data structs

Name-Value Pairs:

• 'ImputeMode' - set imputation method [ 'als' (default) | 'mean |' 'regression' | 'knn' ]
• 'Standardize' - toggle z-scoring by experimental batch and behavioral metric [ true | false ]

Description: Concatenate data from one or more decathlon data structs into a single struct.

Inputs:

• D - array of decathlon data structs
• varargin - Name-Value pair options to specify any pre-processing for structs prior to concatenation.

Outputs:

• D_cat - concatenated decathlon data struct

Name-Value Pairs:

• 'ImputeMode' - set imputation method [ 'als' (default) | 'mean |' 'regression' | 'knn' ]
• 'Standardize' - toggle z-scoring by experimental batch and behavioral metric [ true | false ]
• 'CollapseFields' - set fields to collapse into distilled metrics [ 'none' (default) | 'circadian' | 'all' ]
• 'CollapseMode' - set method of collapsing metrics [ 'PCA' (default) | 'average' ]
• 'Trim' - toggle trimming day of testing from field names [ false (default) | true ]
• 'PCs' - number of PCs to collapse each a priori group [ positive scalar or array (nGroups x 1) ]

Note: this function is used to combine the first and second isogenic decathlons into a single dataset.

Description: Parse decathlon behavior field names from 'Assay metric (day)' format into component parts.

Inputs:

• fields - cell array of behavior field names

Outputs:

• assay - behavior assay name
• metric - behavior metric name
• day - behavior day of testing

Description: Plot variance explained for PCA performed on bootstrapped observed and shuffled data (null model)

Inputs:

• data - 2D data matrix to bootstrap
• nreps - number of bootstrap replicates

Optional Inputs:

• ci - confidence interval [ 95 (default)]
• mode - variance explained plotting mode [ 'noncummulative' (default) | 'cummulative' ]
• max_n - number of samples to draw in each replicate [ num observations (default)]

Outputs:

• null_exp - [nReps x nPC] variance explained for bootstrapped shuffled data
• obs_exp - [nReps x nPC] variance explained for bootstrapped observed data
• nkeep - number of PCs with observed variance explained >= shuffled variance explained
• plot_handles - handles to plot line and patch objects

See: Fig. 4C and Fig. S4D, S6A, S10A.

Description: Plot pairwise correlation matrices and (optionally) sort rows and columns via hierarchical clustering.

Inputs:

• data - [nObservations x nMetrics] data matrix
• varargin - plotting name-value pairs

Name-Value Pairs:

• 'Cluster' - hierarchically cluster rows and columns [true (default) | false]
• 'Ext' - figure export file format extension ['.fig' (default) ]
• 'FontSize' - metric label font size [6 (default) ]
• 'Labels' - metric labels [ none (default) | cell array of strings ]
• 'Options' - optional arguments to MATLABs corr function [ {'rows';'pairwise'} (default)]
• 'Parent' - target axes handles to plot matrices [ none (default) ]
• 'PvalPlot' - plot p-value matrices [true (default) | false]
• 'PvalPatch' - display patch to highlight significant correlations [true (default) | false]
• 'SavePath' - export path for figures [ none(default) ]
• 'Signed' - toggle absolute value of correlations [true (default) | false]

Outputs:

• null_exp - [nReps x nPC] variance explained for bootstrapped shuffled data
• obs_exp - [nReps x nPC] variance explained for bootstrapped observed data
• nkeep - number of PCs with observed variance explained >= shuffled variance explained
• plot_handles - handles to plot line and patch objects

See: Fig. 1E, 1G, 2E, 5A, 6A and Fig. S1A, S8A, S8B, S9, S10B.

Description: Create scatter plot with bootstrapped 95% confidence interval.

Inputs:

• data - [nFlies x 2] data to be plotted
• ah - axes handle to target plot/li>

Name-Value Pairs

• 'XLim' - x-axis limit specified in standard deviations
• 'YLim' - y-axis limit specified in standard deviations
• 'Plot' - toggles plotting on and off [ true (default) | false ]

Outputs:

• fit - struct containing regression parameters

See: Fig. 1F, 1H, 2E, 3D, 5F, 5G and Fig. S8C.

Description: Pair matching behavioral metrics across decathlon experiments and overlay their kernel density estimates.

Inputs:

• D - array of decathlon structs

Name-Value Pairs

• 'Labels' - [nMetrics x 1] cell array of metric labels

Description: Plots t-SNE embeddings for individuals and behavioral metrics for all decathlon experiments. Two behavioral metric embeddings are plotted, one colored by a priori group and one colored by assay.

See: Figure 1.

Description: Sorts behavioral metrics into a priori groupings and plots pairwise correlation matrices. Additionally generates color coded a priori grouping and assay tabs.

See: Figure 1.

Description: Parses assay persistence data into unique combinations of assay x metric across days of testing. and plot the correlation of each metric to itself across days of testing.

See: supplemental figure 1.

Description: Compare error between toy ground truth and imputed datasets infilled via mean, KNN, linear, and ALS imputation methods.

See: supplemental figure 4.

Description: Plot distributions of the correlation of pairwise correlations across bootstrap replicates for all decathlon data structs as well as shuffled data.

See: supplemental figure 5.

Description: Plot PCA variance explained bootstrap for all a priori groups in all decathlon data structs.

Note: empty plots are due to uncollapsable a priori groups composed of a single metric.

See: supplemental figure 6A.

Description: Plot distilled matrix PC behavioral metric loadings for inbred and outbred flies.

See: supplemental figure 6B.

Description: Plot bootstrapped behavioral r-value and p-value distributions for inbred, outbred, and shuffled datasets. Additionally estimates the false discovery rate for significant behavioral correlations.

See: supplemental figure 7.

Description: Plot correlation and p-value matrices for significantly correlated pairs of clumpiness and switchiness a priori group principle components. Plot loadings for significant PCs.

See: supplemental figure 8.

Description: Plot toy dataset covariance matrices with varying effective dimensionality and cluster size. Plot histograms of number of connected components across a parameter sweep of correlation thresholds.

See: supplemental figure 9.

Description: Create scatter plots and correlation bar plots for Y-Maze turn clumpiness and switchiness from the thermogenetic gal4 LDM screen.

See: Figure 3D-E.

Description: Plot heatmaps of the reads per million normalized (RPM) gene expression of all transcripts sequenced. Quantile normalize expression data and create expression scree plots. Plot p-value heatmaps for the gene by behavior linear models for all transcripts passing the minimum RPM threshold.

See: Figure 4B-D.

Description: Plot panels from the boostrapped KEGG enrichment analysis.

1. Bootstrapped shuffled and unshuffled average minimum p-value bar plots.
2. Bootstrapped average number metrics hit by average minimum p-value scatter plots.
3. Average minimum p-value paired dot plots.
4. KEGG pathway by gene boostrapped heatmaps split by a priori groups.

See: Figure 4E-G.

Description: Load unsupervised behavior t-SNE embeddings, pre-process, and summarize with a collection of visualizations.

Note: requires dependencies from Motion-mapper.

See: Figure 2B-E,I.

Description: Fit a 2-component GMM to embedding speed data to identify threshold between state pauses and state transitions.

Inputs:

• z_speed - [nFlies x 1] cell array of individual fly t-SNE embedding speed time series
• plot_bool - [ true | false ] toggle plotting of GMM fit and threshold

Outputs:

• sigma - [nComponents x 1] array of component standard deviations
• z_thresh - t-SNE speed threshold

Description: Plot t-SNE density.

Inputs:

• density - [nBins x nBins] numeric array of t-SNE embedding PDF
• idx_map - [nBins x nBins] array of watershed indices

Name-Value Pairs:

• 'Parent' - target axes for plot
• 'CLim' - color axis limits
• 'Numbered' - toggles displaying text labels over each watershed [ false (default) | true ]
• 'OutlineDensity' - toggles displaying watershed boundaries [ false (default) | true ]

See: Figure 2B.

Description: Plots behavioral mode PDF heatmap (C) and watershed density map colored by average mode density (D).

Inputs:

• idxMap - [nBins x nBins] pixel map of watershed mode identities
• pdfs - [nFlies x nModes] array of individual fly behavior mode probability density functions

See: Figure 2C-D.

Description: Plots separate t-SNE densities for all individual flies.

Inputs:

• embeddings - struct containing embedding data for all flies
• sigma - standard deviation of density estimate kernel
• numPoints - number of bins (X and Y) to estimate density
• rangeVals - [2 x 1] numeric array of min and max bins

• z_data - [nFlies x 1] cell array of individual fly t-SNE trajectories (each nFrames x 2)
• z_speed - [nFlies x 1] cell array of individual fly t-SNE speed (each nFrames x 1)
• label - [nFlies x 1] cell array of unique fly labels (strain + ID no.)
• strain - [nFlies x 1] cell array of fly line labels
• ID - [nFlies x 1] numeric array of fly ID numbers
• z_thresh - [nFlies x 1] t-SNE speed threshold defining boundary between mode pauses and transistions

See: Figure 2D.

Description: Plots behavioral mode PDF correlation matrices split by inbred/outbred flies.

Inputs:

• pdfs - [nFlies x nModes] array of individual fly behavior mode probability density functions

Outputs:

• cluster_perm - [nModes x 1] hierarchical clustering permutation of behavior modes

See: Figure 2E.

Description: Plots separate t-SNE densities for each fly line.

Inputs:

• embeddings - struct containing embedding data for all flies
• sigma - standard deviation of density estimate kernel
• numPoints - number of bins (X and Y) to estimate density
• rangeVals - [2 x 1] numeric array of min and max bins
• clim - [2 x 1] color axis limits

• z_data - [nFlies x 1] cell array of individual fly t-SNE trajectories (each nFrames x 2)
• z_speed - [nFlies x 1] cell array of individual fly t-SNE speed (each nFrames x 1)
• label - [nFlies x 1] cell array of unique fly labels (strain + ID no.)
• strain - [nFlies x 1] cell array of fly line labels
• ID - [nFlies x 1] numeric array of fly ID numbers
• z_thresh - [nFlies x 1] t-SNE speed threshold defining boundary between mode pauses and transistions

Description: Plot individual X and Y t-SNE trajectory timeseries for all individuals.

Inputs:

• embeddings - struct containing embedding data for all flies

• z_data - [nFlies x 1] cell array of individual fly t-SNE trajectories (each nFrames x 2)
• z_speed - [nFlies x 1] cell array of individual fly t-SNE speed (each nFrames x 1)
• label - [nFlies x 1] cell array of unique fly labels (strain + ID no.)
• strain - [nFlies x 1] cell array of fly line labels
• ID - [nFlies x 1] numeric array of fly ID numbers
• z_thresh - [nFlies x 1] t-SNE speed threshold defining boundary between mode pauses and transistions

Description: Assigns watershed mode to each frame based on t-SNE embedding position to generate a mode time series for each fly.

Inputs:

• density - [nBins x nBins] numeric array of t-SNE embedding PDF
• xx - [nBins x 1] center of each point embedding bin
• z - [nFlies x 1] cell array of t-SNE trajectories

Outputs:

• individual_modes - [nFlies x 1] cell array of mode assignments for each individual (nFrames x 1)
• unique_modes - [nModes x 1] numeric array of unique mode identities
• mode_pdfs - [nFlies x nModes] mode probability density functions for each fly
• idxMap - [nFlies x 1] cell array of fly line labels

Description: Segments individual mode time series into discrete bouts populated by a single mode.

Inputs:

• fID - [nFrames x 1] array of mode identity assigned to each frame
• modes - [nModes x 1] unique modes
• tDur - minimum bout length threshold (frames)

Outputs:

• starts - frame indices of mode bout starts (unfiltered)
• stops - frame indices of mode bout stops (unfiltered)
• durations - length in frames of each mode bout (unfiltered)
• sampleFrames - frame indices of mode bout starts (filtered)
• sampleDurations - length in frames of each mode bout (unfiltered)

Description: Generate tiled movie composed of individual movement bout.

Inputs:

• fID - [nFrames x 1] array of mode identity assigned to each frame
• modes - [nModes x 1] unique modes
• tDur - minimum bout length threshold (frames)

Outputs:

• starts - frame indices of mode bout starts (unfiltered)
• stops - frame indices of mode bout stops (unfiltered)
• durations - length in frames of each mode bout (unfiltered)
• sampleFrames - frame indices of mode bout starts (filtered)
• sampleDurations - length in frames of each mode bout (unfiltered)

Description: Segments individual mode time series into discrete bouts populated by a single mode.

Inputs:

• fID - [nFrames x 1] array of mode identity assigned to each frame
• modes - [nModes x 1] unique modes
• tDur - minimum bout length threshold (frames)

Outputs:

• starts - frame indices of mode bout starts (unfiltered)
• stops - frame indices of mode bout stops (unfiltered)
• durations - length in frames of each mode bout (unfiltered)
• sampleFrames - frame indices of mode bout starts (filtered)
• sampleDurations - length in frames of each mode bout (unfiltered)

Description: Segments individual mode time series into discrete bouts populated by a single mode.

Inputs:

• fID - [nFrames x 1] array of mode identity assigned to each frame
• modes - [nModes x 1] unique modes
• tDur - minimum bout length threshold (frames)

Outputs:

• starts - frame indices of mode bout starts (unfiltered)
• stops - frame indices of mode bout stops (unfiltered)
• durations - length in frames of each mode bout (unfiltered)
• sampleFrames - frame indices of mode bout starts (filtered)
• sampleDurations - length in frames of each mode bout (unfiltered)