Metadata-Version: 2.1
Name: wot
Version: 0.5.2
Summary: Optimal transport for time-course single cell data
Home-page: https://github.com/broadinstitute/wot
Author: WOT Team
Author-email: wot@broadinstitute.org
License: BSD license
Description: # WOT: Waddington-OT #
        
        Waddington-OT uses time-course data to infer how the probability distribution
        of cells in gene-expression space evolves over time, by using the mathematical
        approach of Optimal Transport (OT).
        
        ## Install ##
        
        Waddington-OT depends on [Python 3](https://www.python.org/downloads/).
        
        
        ### Dependencies ###
        
        You can install dependencies for **wot** with [conda](https://conda.io/docs/) :
        ```sh
        conda install numpy pandas h5py cython scikit-learn scipy matplotlib
        conda install -c conda-forge pot
        ```
        
        
        ### Install the **wot** package ###
        
        ```sh
        pip install --user wot
        ```
        
        ## Usage ##
        
        ### Initializing an OT Model ###
        
        **wot** uses an OTModel as its interface for computing transport maps. 
        
        You can initialize an OT Model in python with :
        
        ```python
        ot_model = wot.ot.initialize_ot_model('matrix.txt', 'days.txt')
        ```
        
        All Optimal Transport parameters can be customized when initializing the model.
        For instance, you could explicitely specify the defaults :
        
        ```python
        ot_model = wot.ot.initialize_ot_model('matrix.txt', 'days.txt', tmap_prefix='tmaps',
            epsilon=.05, lambda1=10, lambda2=50, batch_size=50, tolerance=1e-2)
        ```
        
        You can compute all transport maps with :
        
        ```python
        ot_model.compute_all_transport_maps()
        ```
        
        ### Loading Transport Maps ###
        
        Once the transport maps have been created, you can operate on the transport maps using the TransportMapModel interface :
        
        ```python
        tmap_model = wot.tmap.TransportMapModel.from_directory('.')
        ```
        
        
        All previously computed transport maps will be available.
        
        ### Changing parameters ###
        
        
        If you want to keep the previously computed transport maps, simply initialize
        a new model with a different prefix. Any model will only affect files that use
        its `tmap_prefix`, there is no interaction between models with different prefixes.
        
        ### Using wot.commands ###
        
        All data-processing functions are located in the `wot.commands` subpackage.
        These include :
        
        - ancestor census
        - gene set scores
        - gene regulatory networks (grn)
        - local enrichment
        - optimal transport validation
        - trajectory
        - trajectory trends
        - convert matrix
        - force layout
        - wot server (interactive version of **wot**)
        
        All of these are documented on [wot's github pages website](http://broadinstitute.github.io/wot), with examples using simulated data to show how to use and plot the results of these commands.
        
        ## Documentation ##
        
        The full documentation for **wot** is available on Github Pages : <http://broadinstitute.github.io/wot>
        
        For more advanced usage, you may also browse the source code to read each
        function's documentation. Most of **wot**'s internal functions have docstrings
        with a description of their parameters, output and examples on how to use them.
        
        ## Developer Notes ##
        
        For more information about the internal functionning of **wot**, please refer
        to the [Developer Notes](developer_notes.md)
        
        [pip-install]: https://pip.pypa.io/en/stable/installing/
        
Keywords: wot
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >= 3
Description-Content-Type: text/markdown
