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

作者：Dongfang Wang, Siyu Hou, Lei Zhang, Xiliang Wang, Baolin Liu, Zemin Zhang · 发表于：Genome biology · 年份：2021 · DOI：10.1186/s13059-021-02280-8 · 被引用次数：63 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Cancer Genomics and Diagnostics

The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.