semvec/pypi-packages huggingface dataset:
imap 1.0.0
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.
License: MIT Licence
Keywords: single-cell RNA-sequencing technologies,neural network,GAN,batch removal

# 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>.