Welcome to ResPAN’s documentation!¶
This reporsity contains code and information of data used in the paper “ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks”. Source code for ResPAN are in the ResPAN folder, scipts for reproducing benchmarking results are in the scripts folder, and data information can be found in the data folder.
ResPAN is a light structured Residual autoencoder and mutual nearest neighbor Paring guided Adversarial Network for scRNA-seq batch correction. The workflow of ResPAN contains three key steps: generation of training data, adversarial training of the neural network, and generation of corrected data without batch effect. A figure summary is shown below.
More details about ResPAN can be found in our manuscript.