Metadata-Version: 1.1
Name: neuralee
Version: 0.1.3
Summary: Scalable Visualization of Massive Single-Cell DataUsing Neural Networks
Home-page: https://github.com/HibearME/NeuralEE
Author: Jiankang Xiong
Author-email: hibearme@163.com
License: MIT license
Description: ========
        NeuralEE
        ========
        .. image:: https://travis-ci.org/HiBearME/NeuralEE.svg?branch=master
            :target: https://travis-ci.org/HiBearME/NeuralEE
            :alt: Build Status
        .. image:: https://readthedocs.org/projects/neuralee/badge/?version=latest
            :target: https://neuralee.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
              
        This is an applicable version for NeuralEE.
        
        1. The datasets loading and preprocessing module is modified from
           `scVI <https://github.com/YosefLab/scVI>`_.
        2. Define NeuralEE class and some auxiliary function, mainly for cuda
           computation, except like entropic affinity calculation which is 
           quite faster computed on cpu.
        3. General elastic embedding algorithm on cuda is given based on matlab code
           from `Max Vladymyrov <https://eng.ucmerced.edu/people/vladymyrov>`_.
        4. Add some demos of notebook helping to reproduce.
        
        ------------
        Installation
        ------------
        
        1. Install Python 3.7. 
        
        2. Install `PyTorch <https://pytorch.org>`_. If you have an NVIDIA GPU, be sure
           to install a version of PyTorch that supports it. NeuralEE runs much faster
           with a discrete GPU.  
        
        3. Install NeuralEE through pip or from GitHub:
        
        .. code-block:: bash
        
            pip install neuralee
        
        .. code-block:: bash
        
            git clone git://github.com/HiBearME/NeuralEE.git
            cd NeuralEE
            python setup.py install --user
        
        -------------------
        How to use NeuralEE
        -------------------
        
        .. code-block:: python
        
            from neuralee.dataset import CortexDataset 
            from neuralee.embedding import NeuralEE
            
            import torch
            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
            
            # preprocess dataset
            cortex_dataset = CortexDataset(save_path='../data/')
            cortex_dataset.log_shift()
            cortex_dataset.subsample_genes(558)  
            cortex_dataset.standardscale()
        
            cortex_dataset.affinity()
            NEE = NeuralEE(cortex_dataset, device=device)
        
            results = NEE.EE()  # Elastic embedding results.
            results_Neural = NEE.fine_tune()  # NeuralEE results.
        
            # with 'mini-batch' trick
            cortex_dataset.affinity_split(N_small=0.25)
            NEE = NeuralEE(cortex_dataset, device=device)
            results_Neural_with4batches = NEE.fine_tune()
        
        Reproduction. Reference from
        `notebook <https://github.com/HiBearME/NeuralEE/tree/master/tests/notebooks>`_
        files.
        
        --------
        Examples
        --------
        
        HEMATO
        
        .. image:: https://raw.githubusercontent.com/HiBearME/NeuralEE/master/img/hemato.png
            :alt: NeuralEE of HEMATO
        
        BRAIN LARGE
        
        .. image:: https://raw.githubusercontent.com/HiBearME/NeuralEE/master/img/brainlarge.png
            :alt: NeuralEE of BRAIN LARGE
        
        
Keywords: neuralee
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
