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.
Project description
iMAP - Integration of multiple single-cell datasets by adversarial paired transfer networks
Installation
1. Prerequisites
- Install Python >= 3.6. Typically, you should use the Linux system and install a newest version of Anaconda or Miniconda .
- 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)
- Install scanpy >= 1.5.1 for pre-processing.
- (Optional) Install SHAP for interpretation.
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 tutorials directory.
Project details
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