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Neural Output Visualization and Analysis
A comprehensive R toolkit for analyzing and visualizing Multi-Electrode Array (MEA) neural data — from raw CSV discovery through PCA trajectory analysis, heatmap generation, and per-metric plotting — with publication-ready figures in a few lines of code.
Example/nova_quickstart.R) to change a single path and
generate all figures automatically
Four treatment groups traced through PCA space over 7 timepoints
(MEA Neuronal Agonists dataset).
PBS control remains near the
origin; KA, Gabazine and NMDA each drive distinct network-level
responses.
# Install from GitHub
remotes::install_github("atudoras/nova")
# Or using devtools
# install.packages("devtools")
devtools::install_github("atudoras/nova")
# Install from CRAN (coming soon)
install.packages("NOVA")library(NOVA)
# Step 1: Discover your MEA data directory structure
discovery <- discover_mea_structure("path/to/your/MEA_data")
# Step 2: Process MEA data across timepoints and grouping variables
processed <- 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"
)
# Step 3: Run enhanced PCA
pca_results <- pca_analysis_enhanced(processing_result = processed)
# Step 4: Generate PCA plots (scatter, ellipses, loadings)
pca_plots <- pca_plots_enhanced(
pca_output = pca_results,
color_variable = "Treatment",
shape_variable = "Genotype"
)
# Step 5: Plot PCA trajectories across timepoints
trajectories <- plot_pca_trajectories_general(
pca_results,
timepoint_order = c("baseline", "0min", "15min", "30min", "1h", "2h"),
trajectory_grouping = c("Genotype", "Treatment")
)
# Step 6: Create MEA heatmaps (split by genotype, filter to specific treatments)
heatmaps <- create_mea_heatmaps_enhanced(
processing_result = processed,
grouping_columns = c("Genotype", "Treatment"),
split_by = "Genotype",
filter_treatments = c("Vehicle", "Drug_A")
)
# Step 7: Plot a single metric across groups
plot_mea_metric(
data = processed$processed_data,
metric = "MeanFiringRate",
plot_type = "violin",
facet_by = "Timepoint"
)
| Feature | Description |
|---|---|
| Smart CSV row detection | find_mea_metadata_row() scans for the “Treatment” label
instead of assuming a fixed row number — handles Axion software export
variations automatically |
| Raw-data heatmaps | New use_raw = TRUE parameter in
create_mea_heatmaps_enhanced() renders un-normalized
electrode data directly |
| Per-metric plots | New plot_mea_metric() function generates bar, box,
violin, or line plots for any single MEA variable with full faceting and
error bar control |
| Heatmap filter and split | New filter_treatments, filter_genotypes,
and split_by parameters in
create_mea_heatmaps_enhanced() for focused, side-by-side
comparisons |
| Zero-code quickstart script | Example/nova_quickstart.R — set DATA_DIR
once, run the script, and all figures are saved automatically |
| Illustrated user guide | Full step-by-step HTML guide with figures available in
docs/user-guide/ |
| Function | Description | Key Parameters |
|---|---|---|
discover_mea_structure |
Scans a directory and reports all detected MEA experiments and timepoints | main_dir, verbose |
process_mea_flexible |
Reads and merges CSVs across experiments and timepoints; normalizes to baseline | main_dir, selected_timepoints,
grouping_variables, baseline_timepoint |
pca_analysis_enhanced |
Runs PCA on the processed feature matrix; returns scores, loadings, and variance explained | processing_result, scale,
center |
pca_plots_enhanced |
Generates a suite of PCA visualizations (scatter, ellipses, loadings, variance) | pca_output, color_variable,
shape_variable |
plot_pca_trajectories_general |
Draws mean PCA trajectories across timepoints for each experimental group | pca_output, timepoint_order,
trajectory_grouping |
create_mea_heatmaps_enhanced |
Creates heatmaps of MEA metrics by treatment and/or genotype; supports raw or normalized data | processing_result, grouping_columns,
split_by, filter_treatments,
filter_genotypes, use_raw |
plot_mea_metric |
Bar, box, violin, or line plot for a single MEA variable with optional faceting and filtering | data, metric, plot_type,
facet_by, filter_treatments,
filter_genotypes, error_type |
NOVA expects a straightforward directory layout that mirrors how Axion BioSystems software exports data:
MEA followed by digits (e.g., MEA001,
MEA016a)<plate>_<timepoint>.csv (e.g.,
MEA001_baseline.csv, MEA001_1h.csv)find_mea_metadata_row()baseline, 1h, DIV7, or
any custom labelMEA_data/
├── MEA001/
│ ├── MEA001_baseline.csv
│ ├── MEA001_1h.csv
│ └── MEA001_24h.csv
├── MEA002/
│ ├── MEA002_baseline.csv
│ └── MEA002_1h.csvFor the fastest path to results, open
Example/nova_quickstart.R. The only change required is
setting DATA_DIR to your data folder path. Running the
script end-to-end will:
No other configuration is needed.
A full illustrated guide covering all functions, parameters, and interpretation of outputs is available at:
The guide includes example figures, annotated code, and recommendations for common experimental designs.
If you use NOVA in published research, please cite:
Escoubas CC, Guney E, Tudoras Miravet À, Magee N, Phua R, Ruggero D, Molofsky AV, Weiss WA (2025). NOVA: a novel R-package enabling multi-parameter analysis and visualization of neural activity in MEA recordings. bioRxiv. https://doi.org/10.1101/2025.10.01.679841
Bug reports and feature requests are welcome via GitHub Issues. Pull requests should follow standard R package conventions and include tests where applicable.
For questions, contact Alex Tudoras at alex.tudorasmiravet@ucsf.edu.
NOVA is released under the GPL-3 License.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.