confluentfucci 1.1.0

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ConfluentFUCCI

A suite of tools for analyzing large scale confluent FUCCI experiments

Overview

This repo includes an integration and automation layer for running CellPose (person-in-the-loop ML driven cell segentation) and TrackMate (cell tracking). Furthermore, a set of analysis and visualization tools for studying confluent cellular dynamics using a FUCCI stain are included.

For Users

The recommended way for trying out ConfluentFUCCI is to use our prebuilt conainer image:

docker run -it --rm \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -p 8080:8080 \
    -p 9876:9876 \
    leogold/confluentfucci:latest

This will start a container that will serve ConfluentFUCCI on localhost:8080 and a virtual desktop on localhost:9876. The app served using the above command does not require a GPU, which significantly affects segmentation time. Too speed up segmentation by leveraging your CUDA compatible GPU, please use:

docker run -it --rm \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -p 8080:8080 \
    -p 9876:9876 \
    --gpus all \
    leogold/confluentfucci:latest

Using docker-compose

To simplify deployment, please check out our docker-compose.yaml. Placing this file in the same path as your data should allow you to test the app using:

docker compose up

If a CUDA compatible GPU is availble on your system, make sure to uncomment:

#    deploy:
#      resources:
#        reservations:
#          devices:
#            - driver: nvidia
#              count: 1
#              capabilities: [ gpu ]

For Developers

This project is set up using poetry. To install the dependencies, run poetry install from the root of the project.

poetry install

To add a new dependency, run poetry add <dependency> from the root of the project.

poetry add <dependency>

Testing

This project uses pytest for testing. To run the tests, run pytest from the root of the project in the poetry shell.

poetry run pytest

There are sensible defaults for pytest setup in the pyproject.toml file. You can override these defaults by passing in command line arguments. For example, to run the tests with debug logging enabled, run pytest --log-cli-level=DEBUG from the root of the project.

poetry run pytest --log-cli-level=DEBUG