Metadata-Version: 2.1
Name: testJvis-learn
Version: 0.0.2
Summary: A generalization of tSNE and UMAP to single cell multimodal data
Home-page: http://github.com/xxx
Maintainer: Van Hoan Do
Maintainer-email: vanhoan310@gmail.com
License: BSD
Description: .. image:: https://img.shields.io/pypi/v/testJvis-learn.svg
            :target: https://pypi.python.org/pypi/testJvis-learn/
            :alt: PyPI Version
        .. image:: https://anaconda.org/conda-forge/testJvis-learn/badges/version.svg
            :target: https://anaconda.org/conda-forge/testJvis-learn
            :alt: Conda-forge Version
        .. image:: https://anaconda.org/conda-forge/testJvis-learn/badges/downloads.svg
            :target: https://anaconda.org/conda-forge/testJvis-learn
            :alt: Downloads from conda-forge
        .. image:: https://img.shields.io/pypi/l/testJvis-learn.svg
            :target: https://github.com/lmcinnes/umap/blob/master/LICENSE.txt
            :alt: License
        .. image:: http://joss.theoj.org/papers/10.21105/joss.00861/status.svg
            :target: xxx_paper_link
            :alt: JOSS article for this repository
        
        ====
        Jvis
        ====
        
        Emerging single-cell genomics technologies profile multiple types of molecules
        within a single cell. A fundamental step in the analysis of the produced high
        dimensional data is their visualization using dimensionality reduction techniques
        such as t-SNE and UMAP. We developed Jvis, a dimensionality reduction framework
        that naturally generalizes t-SNE and UMAP to the joint visualization of
        multimodal omics data. Our approach  automatically learns a weighting of individual
        modalities from the data that promotes discriminative features but suppresses
        random noise. On 8 single-cell multimodal data sets across 5 different technologies,
        Jvis produced a unified embedding that better agrees with known cell states and RNA
        and protein velocity landscapes compared to unimodal representations.
        
        The details for the underlying mathematics can be found in
        insert link here.
        
        Van Hoan Do, Stefan Canzar, A generalization of tSNE and UMAP to single cell multimodal
        data, xxx.
        
        
        ----------
        Installing
        ----------
        
        Requirements:
        
        * Python 3.6 or greater
        * numpy
        * scipy
        * scikit-learn
        * numba
        
        
        **Install Options**
        
        PyPI install, presuming you have numba and sklearn and all its requirements
        (numpy and scipy) installed:
        
        .. code:: bash
        
            pip install testJvis-learn
        
        If you have a problem with pip installation then we'd suggest installing
        the dependencies manually using anaconda followed by pulling umap from pip:
        
        .. code:: bash
        
            conda install numpy scipy
            conda install scikit-learn
            conda install numba
            pip install testJvis-learn
        
        
        We also highly recommend installing the package via Conda:
        
        .. code:: bash
        
            conda install -c conda-forge testJvis-learn
        
        ---------------
        How to use Jvis
        ---------------
        
        The Jvis package inherits from sklearn TSNE, and UMAP. Therefore, all parameters of
        tSNE and UMAP are naturally extended for Jvis.
        
        An example of making use of these options:
        
        .. code:: python
        
            from Jvis import JUMAP, JTSNE
            import numpy as np
        
            # Create a toy example from a random distribution (n_cells = 20)
            rna_rand = np.random.rand(20, 10)
            adt_rand = np.random.rand(20, 15)
            data = {'rna': rna_rand, 'adt': adt_rand} # create a dictionary of modalities.
        
            # Run joint TSNE of the two "random" modalities.
            embedding_jtsne = JTSNE(n_components=2).fit_transform(data)
        
            # Run joint UMAP of the two "random" modalities.
            embedding_jumap = JUMAP(n_neighbors=30,
                                    min_dist=0.3,
                                    metric='correlation').fit_transform(data)
        
        For more realistic examples and Python scripts to reproduce the results
        in our paper are available at GitHub: xxx
        
        Tunning parameters of t-SNE and UMAP can be found here:
        https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
        
        https://umap-learn.readthedocs.io/en/latest/api.html
        
        
        -------
        License
        -------
        
        The JVis package is 3-clause BSD licensed.
        
        Note that the Jvis package is inherited from scikit-learn and UMAP
        package under 3-clause BSD license.
        
        
        
        
Keywords: dimension reduction t-sne umap manifold multimodal data
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/x-rst
Provides-Extra: plot
Provides-Extra: performance
