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.
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