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Neural Output Visualization and Analysis

A comprehensive R toolkit for analyzing and visualizing neural data outputs, including Principal Component Analysis (PCA) trajectory plotting, Multi-Electrode Array (MEA) heatmap generation, and variable importance analysis. Provides publication-ready visualizations with flexible customization options for neuroscience research applications.

# Install from GitHub (replace 'yourusername' with your GitHub username)devtools::install_github("atudoras/NOVA")# Or install from CRAN (when available)install.packages("NOVA")

library(NOVA)# 1. Discover your MEA data structurediscovery_results<- discover_mea_structure("path/to/your/MEA_data")# 2. Process MEA data with flexible optionsprocessed_data<- process_mea_flexible(main_dir="path/to/your/MEA_data",selected_timepoints= c("baseline","0min","15min","30min","1h","2h"),grouping_variables= c("Experiment","Treatment","Genotype","Well"),baseline_timepoint="baseline")# 3. Perform enhanced PCA analysispca_results<- pca_analysis_enhanced(processing_result=processed_data)# 4. Generate comprehensive PCA plotspca_plots<- pca_plots_enhanced(pca_output=pca_results,color_variable="Treatment",shape_variable="Genotype")# 5. Create trajectory analysistrajectories<- plot_pca_trajectories_general(pca_results,timepoint_order= c("baseline","0min","15min","30min","1h","2h"),trajectory_grouping= c("Genotype","Treatment"))# 6. Generate MEA heatmapsheatmaps<- create_mea_heatmaps_enhanced(processing_result=processed_data,grouping_columns= c("Genotype","Treatment"))

The MEA package expects a specific directory structure to automatically discover and process your experimental data. Here's how to organize your files:

main_directory/├── MEA001/│   ├── MEA001_baseline.csv│   ├── MEA001_1h.csv│   ├── MEA001_3h.csv│   └── MEA001_24h.csv├── MEA002/    ├── MEA002_baseline.csv    ├── MEA002_1h.csv    └── MEA002_6h.csv

• Create a parent folder that contains all your MEA experiments
• This is themain_dir parameter you'll pass to the function

• Naming Convention: Each experiment folder must follow the patternMEA + numbers
  • Examples:MEA001,MEA012,MEA123
  • Optional letter suffix is supported:MEA016a,MEA025b
• Pattern: The function looks for folders matchingMEA\\d+ (MEA followed by digits)

• File Format: All data files must be CSV format (.csv extension)
• Naming Pattern: Files should follow one of these patterns:
  • MEAExperimentNumber_timepoint.csv (e.g.,MEA001_1h.csv)
  • MEAExperimentNumber[letter]_timepoint.csv (e.g.,MEA016a_DIV2.csv)

The function can extract various timepoint formats:

• Time-based:baseline,1h,3h,24h,0min
• Days in vitro:DIV2,DIV7,DIV14
• Custom: Any descriptive name that follows the underscore

Each CSV file must contain:

• Minimum 124 rows for basic processing (more if you have additional metadata)
• Row 121: Well identifiers (A1, A2, B1, etc.) - This is fixed
• Row 122: First metadata variable (e.g., Treatment, Genotype, Dose, etc.)
• Row 123: Second metadata variable
• Row 124: Third metadata variable
• Additional rows: You can add more metadata variables in subsequent rowsVariable names start after metadata: If you have metadata in rows 122-125, then variables would start in row 126

1. Consistent Naming: Keep experiment folder names consistent with the MEA + number pattern
2. Clear Timepoints: Use descriptive timepoint names in your CSV filenames
3. File Completeness: Ensure CSV files have the required metadata rows (121-168)
4. No Spaces: Avoid spaces in folder and file names; use underscores instead
5. Backup Data: Always keep backups of your original data files

• If no experiments are found, check that folder names matchMEA + numbers
• If timepoints aren't detected, verify filename followsexperiment_timepoint.csv pattern
• If files can't be read, ensure they're valid CSV files with proper structure
• Enableverbose = TRUE to see detailed discovery process and identify issues

See an example of a complete analysis workflow in the folder "Example".

• Flexible data discovery: Automatically detect MEA data structure
• Multi-experiment processing: Handle multiple experiments and timepoints
• Enhanced PCA analysis: Publication-ready PCA plots with trajectory analysis
• Variable importance analysis: Identify key neural variables
• MEA heatmap generation: Comprehensive electrode data visualization
• Batch effect correction: Built-in normalization options