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 structure
discovery_results <- discover_mea_structure("path/to/your/MEA_data")

# 2. Process MEA data with flexible options
processed_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 analysis
pca_results <- pca_analysis_enhanced(processing_result = processed_data)

# 4. Generate comprehensive PCA plots
pca_plots <- pca_plots_enhanced(
  pca_output = pca_results,
  color_variable = "Treatment",
  shape_variable = "Genotype"
)

# 5. Create trajectory analysis
trajectories <- plot_pca_trajectories_general(
  pca_results,
  timepoint_order = c("baseline", "0min", "15min", "30min", "1h", "2h"),
  trajectory_grouping = c("Genotype", "Treatment")
)

# 6. Generate MEA heatmaps
heatmaps <- 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 the main_dir parameter you'll pass to the function

• Naming Convention: Each experiment folder must follow the pattern MEA + numbers
  • Examples: MEA001, MEA012, MEA123
  • Optional letter suffix is supported: MEA016a, MEA025b
• Pattern: The function looks for folders matching MEA\\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 rows Variable 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 match MEA + numbers
• If timepoints aren't detected, verify filename follows experiment_timepoint.csv pattern
• If files can't be read, ensure they're valid CSV files with proper structure
• Enable verbose = 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