Blind fluorescence unmixing
Project description
napari-PICASSO
Unmix spectral spillover
Automatic Usage
You can find the PICASSO
plugin in the menu Plugins > napari-PICASSO: PICASSO
. Select sink images that have spectral spillover from corresponding source images, then click run to optimise the mixing parameters with PICASSO.
Manual Usage
Select the manual button in options pop up window. Then select sink images that have spectral spillover from corresponding source images. In the source images window, sliders for each $source$ control the mixing spillover, $m$ (top), and background, $b$ (bottom, optional).
Mixing model
$$ sink = \sum_{i} m_i(source - b_i) $$
Installation
You can install napari-PICASSO
via pip:
pip install napari-PICASSO
Details
napari-PICASSO is a napari widget to blindly unmix fluorescence images of known members using PICASSO1.
For example, if 2 fluorophores with overlapping spectra are imaged, spillover fluorescesce from a channel into an adjacent channel could be removed if you know which channel is the source of the spillover fluorescence and which channel is the sink of the spillover fluorescence.
PICASSO is an algorithm to remove spillover fluorescence by minimizing the mutual information between sink and source images. The original algorithm described by Seo et al, minimized the mutual information between pairs of sink and source images using a Nelson-Mead simplex algorithm and computing the mutual information outright with custom written MATLAB code1. The napari plugin uses a neural net to estimate and minimize the mutual information (MINE2) between pairs of sink and source images using stochastic gradient descent with GPU acceleration.
References
- Seo, J. et al. PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements. Nat Commun 13, 2475 (2022).
- Belghazi, M. I. et al. MINE: Mutual Information Neural Estimation. arXiv:1801.04062 [cs, stat] (2018).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file napari-PICASSO-0.3.0.tar.gz
.
File metadata
- Download URL: napari-PICASSO-0.3.0.tar.gz
- Upload date:
- Size: 16.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b65c468b3824419f9ba9975868a1eac777db1edf2f68c92d2e2058a6b6853ed3 |
|
MD5 | 818d125e2dcc434102809ecc7cdc57a7 |
|
BLAKE2b-256 | b4c4f867458d09a49b0db83a00e58bff1c978d57f312427dc5fe21f314b26ef3 |
File details
Details for the file napari_PICASSO-0.3.0-py3-none-any.whl
.
File metadata
- Download URL: napari_PICASSO-0.3.0-py3-none-any.whl
- Upload date:
- Size: 39.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b62551a94a00f730489d7ca40a9aae6c68035730f81ce6c804526e16479e10a2 |
|
MD5 | e6090e470b97b9b4d95ec127858ae14b |
|
BLAKE2b-256 | 928fd605fe2909c20573355f776538589064295e40939a49cbbf029d11eb66bf |