napari-racc 0.2.0

Regression adjusted colocalisation colour mapping for napari

napari-racc

napari-racc is a napari plugin for Regression Adjusted Colocalisation Colour Mapping (RACC), a qualitative visualization method for 2D and 3D fluorescence microscopy data.

The plugin takes two image layers, computes the RACC index in 3D whenever the inputs are volumes, and adds interactive overlay, RACC, side-by-side, MIP, and scatter-plot views to the napari viewer.

Screenshots

3D side-by-side view with the thresholded channel overlay on the left and the RACC volume on the right.

3D-derived maximum-intensity projection view.

Scrollable RACC controls with manual thresholds, Costes thresholding, display scale controls, probe colors, scatter diagnostics, result export, and view switching.

Features

• two-channel RACC calculation from napari Image layers
• live threshold, theta, percentile, and Costes threshold controls
• transparent zero-valued RACC voxels for clean volume rendering
• thresholded RGB overlay volume with selectable probe colors
• side-by-side overlay/RACC and 3D-derived MIP views
• scatter histogram with visible axes, regression, threshold, and percentile-band overlays
• XY and Z display scale controls for metadata-light TIFF stacks
• scrollable control panel with expandable input layer selectors
• export of the numeric RACC result stack as TIFF

Installation

Install from PyPI:

pip install napari-racc

For local development:

git clone https://github.com/rensutheart/napari-racc.git
cd napari-racc
uv venv --python 3.11
source .venv/bin/activate
uv pip install -e ".[dev]"

Fish shell users should activate the environment with:

source .venv/bin/activate.fish

Usage

1. Open napari.
2. Open two image stacks or use File > Open Sample > RACC.
3. Start the widget from Plugins > RACC (napari-racc).
4. Select channel 1 and channel 2.
5. Adjust thresholds manually or press Costes thresholds.
6. Press Run RACC.
7. Use Overlay, RACC, 3D side by side, and MIPs to switch views.
8. Press Export RACC TIFF to save the numeric RACC result stack for use in other software.

RACC is calculated over the full 3D volume when 3D inputs are used. The MIP view is derived from the 3D calculation; it is not a 2D recalculation.

Development

python -m npe2 validate src/napari_racc/napari.yaml
python -m ruff check src
python -m pytest
python -m build

Launch one example dimensionality at a time:

python scripts/launch_racc_examples.py --example 3d
python scripts/launch_racc_examples.py --example 2d

Do not launch the napari viewer with QT_QPA_PLATFORM=offscreen; napari needs a real Qt/OpenGL context for the viewer on macOS.

Citation

If you use this plugin or the RACC method in research, cite:

Theart RP, Loos B, Niesler TR. Regression adjusted colocalisation colour mapping (RACC): A novel biological visual analysis method for qualitative colocalisation analysis of 3D fluorescence micrographs. PLOS ONE 14(11): e0225141. https://doi.org/10.1371/journal.pone.0225141

License And Patent Notice

This software is licensed under the PolyForm Noncommercial License 1.0.0. It is source-available for noncommercial research, education, and evaluation use, but it is not an OSI open-source license.

Use of the RACC method may be covered by patent rights, including US patent application US20220189129A1 and related patent family members. Commercial, clinical, diagnostic, or for-profit service use requires a separate license from the rights holder. See LICENSE, NOTICE, and PATENTS.md.