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

• Published in: Genome Biology Vol. 22; no. 1; p. 63
• Main Authors: Wang, Dongfang, Hou, Siyu, Zhang, Lei, Wang, Xiliang, Liu, Baolin, Zhang, Zemin
• Format: Journal Article
• Language: English
• Published: England BioMed Central 18-02-2021; BMC
• Subjects: Algorithms; Cell interactions; Computational Biology - methods; Data integration; Datasets; Deep Learning; GAN; Gene expression; Generators; Genomics - methods; Humans; Integration; Method; Neural networks; Organ Specificity - genetics; Performance evaluation; Reproducibility of Results; Ribonucleic acid; RNA; RNA-Seq - methods; scRNA-seq; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Tumor microenvironment; Deep learning; scRNA-seq; GAN; Data integration
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-021-02280-8

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.