To avoid dependency conflicts, we recommend the the use of a dedicated virtual or conda <https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage- environments.html> environment. In a terminal run the command:
$ conda create -n bigfish_env python=3.6
$ source activate bigfish_env
We recommend two options to then install Big-FISH in your virtual environment.
Use the package manager pip to install Big-FISH. In a terminal run the command:
$ pip install big-fish
Clone the project's Github repository <https://github.com/fish-quant/big- fish> and install it manually with the following commands:
$ git clone git@github.com:fish-quant/big-fish.git
$ cd big-fish
$ pip install .
Several examples are available as Jupyter notebooks <https://github.com/fish- quant/big-fish-examples/tree/master/notebooks>:
- Read and write images.
- Normalize and filter images.
- Project in two dimensions.
- Segment nuclei and cells.
- Detect spots.
- Extract cell level results.
- Analyze coordinates.
You can also run these example online with mybinder <https://mybinder.org/v2/ gh/fish-quant/fq-imjoy/binder?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252F github.com%252Ffish-quant%252Fbig-fish-examples%26urlpath%3Dtree%252Fbig-fish- examples%252Fnotebooks%26branch%3Dmaster>. The remote server can take a bit of time to start.
stack/io stack/preprocessing stack/augmentation
detection/spots detection/dense detection/subpixel detection/cluster detection/colocalization
segmentation/nucleus segmentation/cell segmentation/postprocessing
classification/extraction classification/features
plot/plot_image plot/plot_detection plot/plot_segmentation plot/plot_coordinate
utils/utils
If you have any question relative to the package, please open an issue on Github.
If you exploit this package for your work, please cite:
Arthur Imbert, Wei Ouyang, Adham Safieddine, Emeline Coleno, Christophe
Zimmer, Edouard Bertrand, Thomas Walter, Florian Mueller. FISH-quant v2:
a scalable and modular analysis tool for smFISH image analysis. bioRxiv
(2021) https://doi.org/10.1101/2021.07.20.453024