Awesome

iMAP - Integration of multiple single-cell datasets by adversarial paired transfer networks

Cite this article

Wang, D., Hou, S., Zhang, L. et al. iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks. Genome Biol 22, 63 (2021). https://doi.org/10.1186/s13059-021-02280-8

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 >= 1.6.0 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/cell_lines_tutorial.md'>tutorials directory</a>.