napari-clusters-plotter

A plugin to use with napari for clustering objects according to their properties

    Workflow step:
    Clustering
    ClusteringCentroid-based clustering
    ClusteringHierarchical clustering
    Visualization
    VisualizationPlotting
    Object feature extraction
    Object feature extractionShape features extraction

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A plugin to use with napari for clustering objects according to their properties.


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


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Usage

Starting point

For clustering objects according to their properties, the starting point is a grey-value image and a label image representing a segmentation of objects. For segmenting objects, you can for example use the Voronoi-Otsu-labelling approach in the napari plugin napari-segment-blobs-and-things-with-membranes.

Measurements

The first step is deriving measurements from the labelled image and the corresponding pixels in the grey-value image. You can use the menu Tools > Measurement > Measure intensity, shape and neighbor counts (ncp) for that. Just select the image, the corresponding label image and the measurements to analyse and click on Run. A table with the measurements will open:

Afterwards, you can save and/or close the measurement table. Also, close the Measure widget. If you are uploading your own measurements make sure that there is a column that specifies the which measurement belongs to which label by adding a column with the name "label". If you don't specify this column it will be assumed that measurements start at 1 and each column describes the next label.

Time-Lapse Measurements

If you have 3D time-lapse data this will automatically be detected. In case you have 2D time-lapse data you need to convert it into a suitable shape using the function: Tools > Utilities > Convert 3D stack to 2D time-lapse (time-slicer), which can be found in the napari time slicer. Note that tables for time-lapse data will include an additional column named "frame", which indicates which slice in time the given row refers to. If you want to import your own csv files for time-lapse data make sure to include this column! If you have tracking data where each column specifies measurements for a track instead of a label at a specific time point, this column must not be added.

Plotting

Once measurements were made, these measurements were saved in the properties of the labels layer which was analysed. You can then plot these measurements using the menu Tools > Measurement > Plot measurement (ncp).

In this widget, you can select the labels layer which was analysed and the measurements which should be plotted on the X- and Y-axis. If you cannot see any options in axes selection boxes, but you have performed measurements, click on Update Axes/Clustering Selection Boxes to refresh them. Click on Run to draw the data points in the plot area.

You can also manually select a region in the plot. To use lasso (freehand) tool use left mouse click, and to use a rectangle - right click. The resulting manual clustering will also be visualized in the original image. To optimize visualization in the image, turn off the visibility of the analysed labels layer.

Hold down the SHIFT key while annotating regions in the plot to manually select multiple clusters.

Time-Lapse Plotting

When you plot your time-lapse datasets you will notice that the plots look slightly different. Datapoints of the current time frame are highlighted in white and you can see the datapoints move through the plot if you press play:

You can also manually select groups using the lasso tool and plot a measurement per frame and see how the group behaves in time. Furthermore, you could also select a group in time and see where the datapoints lie in a different feature space:

Dimensionality reduction: UMAP, t-SNE or PCA

For getting more insights into your data, you can reduce the dimensionality of the measurements, e.g. using the UMAP algorithm, t-SNE or PCA. To apply it to your data use the menu Tools > Measurement > Dimensionality reduction (ncp). Select the label image that was analysed and in the list below, select all measurements that should be dimensionality reduced. By default, all measurements are selected in the box. If you cannot see any measurements, but you have performed them, click on Update Measurements to refresh the box. You can read more about parameters of both algorithms by hovering over question marks or by clicking on them. When you are done with the selection, click on Run and after a moment, the table of measurements will re-appear with two additional columns representing the reduced dimensions of the dataset. These columns are automatically saved in the properties of the labels layer so there is no need to save them for usage in other widgets unless you wish to do so.

Afterwards, you can again save and/or close the table. Also, close the Dimensionality Reduction widget.

Clustering

If manual clustering, as shown above, is not an option, you can automatically cluster your data, using these implemented algorithms:

Therefore, click the menu Tools > Measurement > Clustering (ncp), again, select the analysed labels layer. This time select the measurements for clustering, e.g. select only the UMAP measurements. Select the clustering method KMeans and click on Run. The table of measurements will reappear with an additional column ALGORITHM_NAME_CLUSTERING_ID containing the cluster ID of each datapoint.

Afterwards, you can again save and/or close the table. Also, close the clustering widget.

Plotting clustering results

Return to the Plotter widget using the menu Tools > Measurement > Plot measurement (ncp). Select UMAP_0 and UMAP_1 as X- and Y-axis and the ALGORITHM_NAME_CLUSTERING_ID as Clustering, and click on Run.

Example of k-means clustering results:

Installation

  • Get a python environment, e.g. via mini-conda. If you never used python/conda environments before, please follow the instructions here first. It is recommended to install python 3.9 to your new conda environment from the start. The plugin is not yet supported with Python 3.10. Create a new environment, for example, like this:
conda create --name ncp-env python=3.9
  • Activate the new environment via conda:
conda activate ncp-env
conda install -c conda-forge pyopencl
python -m pip install "napari[all]"

Afterwards, you can install napari-clusters-plotter via pip:

pip install napari-clusters-plotter

Troubleshooting installation

  • If the plugin does not appear in napari 'Plugins' menu, and in 'Plugin errors...' you can see such an error:
ImportError: DLL load failed while importing _cl

Try downloading and installing a pyopencl with a lower cl version, e.g. cl12 : pyopencl=2020.1. However, in this case, you will need an environment with a lower python version (python=3.8).

  • Error: Could not build wheels for hdbscan which use PEP 517 and cannot be installed directly

Install hdbscan via conda before installing the plugin:

conda install -c conda-forge hdbscan
  • ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

Similar to the above-described error, this error can occur when importing hdbscan through pip or in the wrong order. This can be fixed by installing packages separately through conda and in the following order:

conda install -c conda-forge napari pyopencl hdbscan
pip install napari-clusters-plotter
  • WARNING: No ICDs were found or LogicError: clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR

Make your system-wide implementation visible by installing either of the following conda packages:

conda install -c conda-forge ocl-icd-system
conda install -c conda-forge ocl_icd_wrapper_apple

Contributing

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

License

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

Acknowledgements

This project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence "Physics of Life" of TU Dresden. This project has been made possible in part by grant number 2021-240341 (Napari plugin accelerator grant) from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.

Issues

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

How to cite this plugin

If you use this plugin in your work, please cite it using the following citation. Don’t forget to cite napari too!

Zigutyte L., Savill R., Müller J., Zoccoler M., Haase R. (2021). napari-clusters-plotter (version 0.5.1). DOI: 10.5281/zenodo.5884657 URL: https://github.com/BiAPoL/napari-clusters-plotter
Download

Version:

  • 0.5.2

Release date:

  • 19 August 2022

First released:

  • 15 November 2021

License:

  • BSD-3-Clause

Supported data:

GitHub activity:

  • Stars: 37
  • Forks: 5
  • Issues + PRs: 19

Python versions supported:

Operating system:

Requirements:

  • napari-plugin-engine (>=0.1.4)
  • numpy (>=1.21)
  • pyopencl
  • pyclesperanto-prototype
  • scikit-learn
  • pandas
  • umap-learn
  • napari-tools-menu
  • napari-skimage-regionprops (>=0.3.1)
  • hdbscan

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