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A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data

Author

Listed:
  • Sijing An

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

  • Jinhui Shi

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

  • Runyan Liu

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

  • Jing Wang

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

  • Shuofeng Hu

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

  • Guohua Dong

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

  • Xiaomin Ying

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

  • Zhen He

    (Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China)

Abstract

Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is essential for characterizing cellular heterogeneity. However, batch information caused by batch effects is often confused with the intrinsic biological information in scRNA-seq data, which makes accurate clustering quite challenging. A Deep Adaptive Clustering with Adversarial Learning method (DACAL) is proposed here. DACAL jointly optimizes the batch correcting and clustering processes to remove batch effects while retaining biological information. DACAL achieves batch correction and adaptive clustering without requiring manually specified cell types or resolution parameters. DACAL is compared with other widely used batch correction and clustering methods on human pancreas datasets from different sequencing platforms and mouse mammary datasets from different laboratories. The results demonstrate that DACAL can correct batch effects efficiently and adaptively find accurate cell types, outperforming competing methods. Moreover, it can obtain cell subtypes with biological meanings.

Suggested Citation

  • Sijing An & Jinhui Shi & Runyan Liu & Jing Wang & Shuofeng Hu & Guohua Dong & Xiaomin Ying & Zhen He, 2023. "A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data," Mathematics, MDPI, vol. 11(24), pages 1-13, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4901-:d:1296042
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