{"name":"napari-clusters-plotter","display_name":"napari-clusters-plotter","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-clusters-plotter.PlotterWidget","title":"PlotterWidget","python_name":"napari_clusters_plotter._plotter:PlotterWidget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-clusters-plotter.DimensionalityReductionWidget","title":"DimensionalityReductionWidget","python_name":"napari_clusters_plotter._dimensionality_reduction:DimensionalityReductionWidget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-clusters-plotter.ClusteringWidget","title":"ClusteringWidget","python_name":"napari_clusters_plotter._clustering:ClusteringWidget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"napari-clusters-plotter.PlotterWidget","display_name":"PlotterWidget","autogenerate":false},{"command":"napari-clusters-plotter.DimensionalityReductionWidget","display_name":"DimensionalityReductionWidget","autogenerate":false},{"command":"napari-clusters-plotter.ClusteringWidget","display_name":"ClusteringWidget","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"napari-clusters-plotter","version":"0.7.3","dynamic":null,"platform":null,"supported_platform":null,"summary":"A plugin to use with napari for clustering objects according to their properties","description":"# napari-clusters-plotter\n\n[](https://github.com/lazigu/napari-clusters-plotter/raw/master/LICENSE)\n[](https://pypi.org/project/napari-clusters-plotter)\n[](https://python.org)\n[](https://anaconda.org/conda-forge/napari-clusters-plotter)\n[](https://github.com/BiAPoL/napari-clusters-plotter/actions)\n[](https://codecov.io/gh/BiAPoL/napari-clusters-plotter)\n[](https://github.com/pre-commit/pre-commit)\n[](https://anaconda.org/conda-forge/napari-clusters-plotter)\n[](https://www.napari-hub.org/plugins/napari-clusters-plotter)\n[](https://doi.org/10.5281/zenodo.7011471)\n\nA napari-plugin for clustering objects according to their properties.\n\n![](\"https://github.com/BiAPoL/napari-clusters-plotter/raw/main/images/screencast2_timelapse.gif\")
\n\n----------------------------------\n\nJump to:\n- [Usage](#usage)\n - [Starting point](#starting-point)\n - [Measurements](#measurements)\n - [Time-Lapse Measurements](#time-lapse-measurements)\n - [Plotting](#plotting)\n - [Time-Lapse Plotting](#time-lapse-plotting)\n - [Dimensionality reduction: UMAP, t-SNE or PCA](#dimensionality-reduction-umap-t-sne-or-pca)\n - [Clustering](#clustering)\n - [Plotting clustering results](#plotting-clustering-results)\n- [Installation](#installation)\n- [Troubleshooting installation](#troubleshooting-installation)\n- [Contributing](#contributing)\n- [License](#license)\n- [Acknowledgements](#acknowledgements)\n\n\n## Usage\n\n### Starting point\nFor clustering objects according to their properties, the starting point is a [grey-value image](example_data/blobs.tif) and a label image\nrepresenting a segmentation of objects. For segmenting objects, you can for example use the\n[Voronoi-Otsu-labelling approach](https://github.com/haesleinhuepf/napari-segment-blobs-and-things-with-membranes#voronoi-otsu-labelling)\nin the napari plugin [napari-segment-blobs-and-things-with-membranes](https://www.napari-hub.org/plugins/napari-segment-blobs-and-things-with-membranes).\n\n\n\nIn case you have 2D time-lapse data you need to convert it into a suitable shape using the menu `Tools > Utilities > Convert 3D stack to 2D time-lapse (time-slicer)` ([documentation](https://www.napari-hub.org/plugins/napari-time-slicer)).\n\n### Measurements\nThe first step is deriving measurements from the labeled image and the corresponding pixels in the grey-value image.\nUse the menu `Tools > Measurement tables > Regionprops (scikit-image, nsr)` to get to the measurement widget ([documentation](https://www.napari-hub.org/plugins/napari-skimage-regionprops)).\nSelect the image, the corresponding label image and the measurements to analyse and click on `Run`.\nA table with the measurements will open and afterwards, you can save and/or close the measurement table.\nAt this point it is recommended to close the table and the Measure widget to free space for following steps.\n\nYou can also load your own measurements you can do this using the menu `Tools > Measurement tables > Load from CSV (nsr)`.\nIf you load custom measurements, please make sure that there is a `label` column that specifies the which measurement belongs to which labeled object.\nTables for time-lapse data need to include an additional column named `frame`.\n\n### Plotting\n\nOnce measurements were saved in the labels layer which was analysed, you can then plot these measurements using the menu `Tools > Visualization > Plot measurements (ncp)`.\n\nIn this widget, you can select the labels layer which was analysed and the measurements which should be plotted\non the X- and Y-axis. Click on `Plot` to draw the data points in the plot area.\n\n\n\nUnder advanced options, you can also select the plot type histogram which will visualize a 2D histogram. 2D histogram visualization is recommended if you have a very high number of data points.\n\n\n\nIf you choose the same measurement for the X and the Y axis, a histogram will be shown.\n\n\n\nUnder advanced options you will also find the checkbox determining whether not-selected data points should be hidden (shown in grey) or automatically\nclustered as another cluster.\n\n### Manual clustering\n\nYou can manually select a region in the plot. To use lasso (freehand) tool use left mouse click, and to use a\nrectangle - right click. The resulting manual clustering will also be visualized in the original image. To optimize\nvisualization in the image, turn off the visibility of the analysed labels layer.\n\n![](\"https://github.com/BiAPoL/napari-clusters-plotter/raw/main/images/screencast.gif\")
\n\nHold down the SHIFT key while annotating regions in the plot to manually select multiple clusters.\n\n\n\n### Saving manual clustering\n\nManual clustering results can be saved by going to `Tools > Measurement > Show table (nsr)`, and clicking on `Save as csv`.\nThe saved table will contain a \"MANUAL_CLUSTER_ID\" column. This column is overwritten every time different clusters are manually selected.\n\n### Time-Lapse analysis\n\nWhen you plot your time-lapse datasets you will notice that the plots look slightly different.\nDatapoints of the current time frame are highlighted in bright color and you can see the datapoints move through the plot while you navigate through time:\n\n\n\nYou can also manually select groups using the lasso tool and plot a measurement per frame and see how the group behaves in time.\nFurthermore, you could also select a group in time and see where the datapoints lie in a different feature space:\n\n\n\nIf you have custom measurements from tracking data where each column specifies measurements for a track instead of a label at a specific time point, the `frame` column must not be added.\n\n### Dimensionality reduction\n\nFor getting more insights into your data, you can reduce the dimensionality of the measurements, using these algorithms:\n* [Uniform Manifold Approximation Projection (UMAP)](https://umap-learn.readthedocs.io/en/latest/)\n* [t-distributed stochastic neighbor embedding (t-SNE)](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)\n* [Principal Component Analysis (PCA)](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n* [Non-linear dimensionality reduction through Isometric Mapping (Isomap)](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html)\n* [Multi-dimensional Scaling (MDS)](https://scikit-learn.org/stable/modules/manifold.html#multidimensional-scaling)\n\nTo apply them to your data use the menu `Tools > Measurement post-processing > Dimensionality reduction (ncp)`.\nSelect the label image that was analysed and in the list below, select all measurements that should be dimensionality reduced.\nBy default, all measurements are selected in the box.\nYou can read more about parameters of both algorithms by hovering over question marks or by clicking on them.\nWhen 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.\nThese columns are automatically saved in the labels layer and can be further processed by other plugins.\n\n\n\nAfterwards, you can again save and/or close the table.\n\n### Clustering\nIf manual clustering, as shown above, is not an option, you can automatically cluster your data, using these implemented algorithms:\n* [k-means clustering (KMEANS)](https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a)\n* [Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN)](https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html)\n* [Gaussian Mixture Model (GMM)](https://scikit-learn.org/stable/modules/mixture.html)\n* [Mean Shift (MS)](https://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py)\n* [Agglomerative clustering (AC)](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html)\n\nTherefore, click the menu `Tools > Measurement post-processing > Clustering (ncp)`,\nselect the analysed labels layer.\nSelect the measurements for clustering, e.g. select _only_ the `UMAP` measurements.\nSelect the clustering method `KMeans` and click on `Run`.\nThe table of measurements will reappear with an additional column `ALGORITHM_NAME_CLUSTERING_ID` containing the cluster\nID of each datapoint.\n\n\n\nAfterwards, you can save and/or close the table.\n\n### Plotting clustering results\n\nReturn to the Plotter widget using the menu `Tools > Visualization > Plot measurement (ncp)`.\nSelect `UMAP_0` and `UMAP_1` as X- and Y-axis and the `ALGORITHM_NAME_CLUSTERING_ID` as `Clustering`, and click on `Plot`.\n\n\n\n## Installation\n### Devbio-napari installation\n\nThe easiest way to install this plugin is to install the [devbio-napari](https://github.com/haesleinhuepf/devbio-napari) plugin collection. The napari-clusters-plotter is part of it.\n\n### Minimal installation\n* Get a python environment, e.g. via [mini-conda](https://docs.conda.io/en/latest/miniconda.html).\n If you never used mamba/conda environments before, please follow the instructions\n [in this blog post](https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html) first.\n\n* Create a new environment, for example, like this:\n\n```\nmamba create --name ncp-env python=3.9\n```\n\n* Activate the new environment via conda:\n\n```\nmamba activate ncp-env\n```\n\n* Install [napari], e.g. via [conda]:\n\n```\nmamba install -c conda-forge napari\n```\n\nAfterwards, you can install `napari-clusters-plotter`, e.g. via [conda]:\n\n```\nmamba install -c conda-forge napari-clusters-plotter\n```\n\n## Troubleshooting installation\n\n- `Error: Could not build wheels for hdbscan which use PEP 517 and cannot be installed directly`\n\nThis can happen if you used pip for the installation. To solve this error, install hdbscan via conda before installing the plugin:\n\n```\nmamba install -c conda-forge hdbscan\n```\n\n- `ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject`\n\nSimilar 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:\n```bash\nmamba install -c conda-forge napari hdbscan\npip install napari-clusters-plotter\n```\n\n## Contributing\n\nContributions are very welcome. Tests can be run with [pytest], please ensure\nthe coverage at least stays the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"napari-clusters-plotter\" is free and open source software\n\n## Acknowledgements\nThis project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence \"Physics of Life\" of TU Dresden.\nThis project has been made possible in part by grant number [2021-240341 (Napari plugin accelerator grant)](https://chanzuckerberg.com/science/programs-resources/imaging/napari/improving-image-processing/) from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.\n\n## Issues\n\nIf you encounter any problems, please [file an issue](https://github.com/BiAPoL/napari-clusters-plotter/issues) along\nwith a detailed description.\n\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n\n[napari]: https://github.com/napari/napari\n[pytest]: https://docs.pytest.org/en/7.0.x/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n[conda]: https://docs.conda.io/projects/conda/en/latest/\n","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/BiAPoL/napari-clusters-plotter","download_url":null,"author":"Laura Zigutyte, Ryan Savill, Johannes Müller, Marcelo Zoccoler, Thorsten Wagner, Robert Haase","author_email":"zigutyte@gmail.com, robert.haase@tu-dresden.de","maintainer":null,"maintainer_email":null,"license":"BSD-3-Clause","classifier":["Development Status :: 2 - Pre-Alpha","Intended Audience :: Developers","Framework :: napari","Topic :: Software Development :: Testing","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3.7","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Operating System :: OS Independent","License :: OSI Approved :: BSD License"],"requires_dist":["napari-plugin-engine >=0.1.4","numpy <=1.23.5,>=1.21","scikit-learn","matplotlib","pandas","umap-learn","napari-tools-menu","napari-skimage-regionprops >=0.3.1","hdbscan","joblib"],"requires_python":">=3.7","requires_external":null,"project_url":["Bug Tracker, https://github.com/BiAPoL/napari-clusters-plotter/issues","Documentation, https://github.com/BiAPoL/napari-clusters-plotter","Source Code, https://github.com/BiAPoL/napari-clusters-plotter","User Support, https://github.com/BiAPoL/napari-clusters-plotter/issues"],"provides_extra":null,"provides_dist":null,"obsoletes_dist":null},"npe1_shim":true}