napari-colocalization¶
Under construction — pre-alpha
APIs, UI, and outputs may change without notice. Not recommended for production analysis yet; use at your own risk and please report rough edges via the issue tracker.
Interactive intensity-colocalization analysis for napari. Pick two channels (or one multi-channel image), optionally restrict the analysis to a region drawn as shapes or labels, choose your metric, and get a results table plus an intensity-vs-intensity density plot — all without leaving napari.
Features¶
- Four correlation metrics: Pearson (PCC), Spearman rank (SRCC), Li's Intensity Correlation Quotient (ICQ), and Manders' coefficients M1/M2 (MCC).
- Pairwise or all-to-all mode: analyse two grayscale layers, or every channel pair within a single multi-channel layer.
- 2D and 3D support natively (no time-series for now).
- Region-restricted analysis via a Shapes or Labels layer — each non-zero region is reported on its own row.
- Manders thresholds: choose Costes auto (iterative regression-based) or Manual.
- Interactive results: in-widget table, density plot of the selected row, multi-row selection that highlights all matching shapes/labels in the viewer.
- CSV export of the current table, plus figure export of the density plot (PNG / PDF / SVG / TIFF, configurable size and DPI).
Installation¶
If napari isn't already installed, install both at once:
Where next?¶
- Usage guide — every control in the widget, in order.
- Metrics — what PCC, SRCC, ICQ and MCC mean, when to use which, and how the Costes auto-threshold works.
- Python API — calling the pure-compute layer
(
pearson,spearman,manders,costes_threshold,analyse_pairwise,analyse_all_to_all) from scripts or notebooks. Reference is auto-generated from the source docstrings.
Source code¶
The plugin lives at github.com/DBI-INFRA/napari-colocalization. File issues or feature requests on the tracker there.