napari-mm3 0.0.10

a plugin for mother machine image analysis

napari-mm3

A plugin for Mother Machine Image Analysis by Jun Lab.

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This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

Load up a new environment. We run the following command, replacing environment-name-here with a name of your choosing:

conda create -y -n environment-name-here python=3.9 napari tensorflow

Now, to install our code: if you would like to have the latest version, do the following.

1. You can clone the repository with git clone [e-mail]:junlabucsd/napari-mm3.git (SSH) or git clone https://github.com/junlabucsd/napari-mm3.git (https)
2. With your environment active, run pip install -e . from inside your cloned repo.

If you would like to have a more stable verison, simply run pip install napari-mm3.

NOTE: Not running the conda command and trying to install things in a different way may lead to difficult issues with PyQt5. We recommend following the above commands to simplify the situation.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

Usage guide

Brief video introduction: available here

a. Preprocessing

• nd2ToTIFF -- Turn your nd2 microscopy data into TIFFs. If your data is not in the nd2 format, follow the input image guidelines. Make sure to set 'image source' in Compile to 'Other'.

• Compile -- Locate traps, separate their timelapses into their own TIFFs, and return metadata.

b. Segmentation

With Otsu:

• PickChannels -- User guided selection of empty and full traps.

• Subtract -- Remove (via subtraction) empty traps from the background of traps that contain cells; run this on the phase contrast channel.

• SegmentOtsu -- Use Otsu segmentation to segment cells.

With UNet:

• Annotate -- annotate images for ML (U-Net or similar) training purposes; you can generate a model via TODO.

• SegmentUnet -- Run U-Net segmentation (you will need to supply your own model)

c. Tracking

• Track -- Acquire individual cell properties and track lineages.

d. Fluorescence data analysis

• PickChannels -- If you've already done this (e.g. for otsu segmentation), no need to do it again. User guided selection of empty and full traps.

• Subtract -- Remove (via subtraction) empty traps from the background of traps that contain cells. This time, run this on your fluorescence channels.

• Colors -- Calculate fluorescence information.

e. (Uncommon) Foci tracking

• Foci -- We use this to track `foci' (bright fluorescent spots) inside of cells.

f. Outputs, inputs, and file structure

Finally, to better understand the data formats, you may wish to refer to the following documents:

• Input image guidelines

• File structure

• Output file structure

License

Distributed under the terms of the BSD-3 license, "napari-mm3" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.