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

ABSTRACT: 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.

SUBMITTER: Wang D

PROVIDER: S-EPMC7891139 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Wang Dongfang D Hou Siyu S Zhang Lei L Wang Xiliang X Liu Baolin B Zhang Zemin Z

Genome biology 20210218 1

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 dis ...[more]

PMID: 33602306

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