Skip to main content

Blind fluorescence unmixing

Project description

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 code. 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.

  1. Seo, J. et al. PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements. Nat Commun 13, 2475 (2022).
  2. Belghazi, M. I. et al. MINE: Mutual Information Neural Estimation. arXiv:1801.04062 [cs, stat] (2018).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

napari-PICASSO-0.0.0.tar.gz (16.6 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

napari_PICASSO-0.0.0-py3-none-any.whl (38.8 kB view details)

Uploaded Python 3

File details

Details for the file napari-PICASSO-0.0.0.tar.gz.

File metadata

  • Download URL: napari-PICASSO-0.0.0.tar.gz
  • Upload date:
  • Size: 16.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for napari-PICASSO-0.0.0.tar.gz
Algorithm Hash digest
SHA256 d426f8a2f83c372324cd83023c2cc53720132c44b6aa61b177df47540d9b2139
MD5 a17c97abb7a0e370332f3bf39a9c1fbc
BLAKE2b-256 b1865c728ee5a0f0579bbf924c6c80667a8632253e7830e4057054bbf570877c

See more details on using hashes here.

File details

Details for the file napari_PICASSO-0.0.0-py3-none-any.whl.

File metadata

  • Download URL: napari_PICASSO-0.0.0-py3-none-any.whl
  • Upload date:
  • Size: 38.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for napari_PICASSO-0.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 433e319db7eff68b0371b79001260b3e65114e8ab6f7724df04ad1c0c199df54
MD5 e661111acaa4dd53f6ced93ad526e791
BLAKE2b-256 8c7436d26a521e88795beb0d1b1b78282fe337f6c1626b9715c812c7efb69d02

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page