Package: napari-mm3
napari-mm3A plugin for mother machine image analysis by theJun Lab.Reference:Tools and methods for high-throughput single-cell imaging with the mother machine. Ryan Thiermann, Michael Sandler, Gursharan Ahir, John T. Sauls, Jeremy W. Schroeder, Steven D. Brown, Guillaume Le Treut, Fangwei Si, Dongyang Li, Jue D. Wang, Suckjoon Jun. bioRxiv 2023.03.27.534286Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.https://github.com/junlabucsd/napari-mm3/assets/40699438/1b3e6121-f5e1-475f-aca3-c6ed1b5bab3aInstallationWe describe installation with mamba, a faster version of conda which we recommend. Installation with conda is the exact same, except replacemambawithcondaRun the following command:mamba create -n napari-mm3 -c conda-forge conda-build tensorflow napariNow, you need to install our code (please let us know if this causes problems -- it has been a pain point in the past). To do so, clone the repository:git clone https://github.com/junlabucsd/napari-mm3.gitThen, run the following commands from within your conda environment:cd napari-mm3
pip install -e .This supplies you with the latest, most recent version of our code.If you would like to have a more stable version, simply runpip install napari-mm3. In general, we recommend going off of the github version.napari-MM3 can use thepython-bioformatslibrary to import various image file formats. It can be installed with pip:pip install python-bioformatsIf your raw images are in the .nd2 format, they will be read in with the nd2reader package. In this case, Bio-Formats is not required.NOTES:
Not running the conda command above 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.
Usingpip -e .instead ofmamba develop .is a deliberate choice, the former did not seem to register the plugin with napari.ContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.Usage guideBrief video introduction:availableherea. PreprocessingTIFFConverter-- Turn your nd2 microscopy data, or other format via bioformats, into TIFFs. If your data is already exported as individual TIFF files, skip to theCompilewidget. Take note of theinput image guidelines.Compile-- Locate traps, separate their timelapses into their own TIFFs, and return metadata.PickChannels-- User guided selection of empty and full traps.b. SegmentationWith Otsu's method: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 U-Net:Annotate-- annotate images for ML (U-Net or similar) training purposes.Train U-Net-- Train a U-Net model for cell segmentation.SegmentUnet-- Run U-Net segmentation.c. TrackingTrack-- Acquire individual cell properties and track lineages.d. Fluorescence data analysisSubtract-- 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. Focus trackingFoci-- We use this to trackfoci(bright fluorescent spots) inside of cells.f. Extracting data and plottingThe notebookheredemonstrates how to extract, filter and visualize the lineage data output by theTrackwidget.g. Outputs, inputs, and file structureFinally, to better understand the data formats, you may wish to refer to the following documents:Input image guidelinesFile structureOutput data structureLicenseDistributed under the terms of theBSD-3license,
"napari-mm3" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.