Metadata-Version: 2.1
Name: imap
Version: 1.0.0
Summary: The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch removal framework, called iMAP, based on two state-of-art deep generative models – autoencoders and generative adversarial networks.
Home-page: https://github.com/Svvord/
Author: Dongfang Wang, Siyu Hou
Author-email: housy17@mails.tsinghua.edu.cn
Maintainer: Siyu Hou
Maintainer-email: housy17@mails.tsinghua.edu.cn
License: MIT Licence
Description: 
        # iMAP - Integration of multiple single-cell datasets by adversarial paired transfer networks
        
        ### Installation
        
        #### 1. Prerequisites
        
        <ul>
            <li>Install Python >= 3.6. Typically, you should use the Linux system and install a newest version of <a href='https://www.anaconda.com/'>Anaconda</a> or <a href = 'https://docs.conda.io/en/latest/miniconda.html'> Miniconda </a>.</li>
            <li>Install pytorch >= 1.1.0. To obtain the optimal performance of deep learning-based models, you should have a Nivdia GPU and install the appropriate version of CUDA. (We tested with CUDA = 9.0)</li>
            <li> Install scanpy >= 1.5.1 for pre-processing. </li>
            <li>(Optional) Install <a href='https://github.com/slundberg/shap'>SHAP</a> for interpretation.</li>
        </ul>
        
        #### 2. Installation
        
        The iMAP python package is available for pip install(`pip install imap`). The functions required for the stage I and II of iMAP could be imported from “imap.stage1” and “imap.stage2”, respectively.
        
        ### Tutorials
        
        Tutorials and API reference are available in the <a href='tutorials'>tutorials directory</a>. 
        
Keywords: single-cell RNA-sequencing technologies,neural network,GAN,batch removal
Platform: any
Description-Content-Type: text/markdown
