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

Sijing An, Jinhui Shi, Runyan Liu, Jing Wang, Shuofeng Hu, Guohua Dong, Xiaomin Ying () and Zhen He ()
Additional contact information
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

Mathematics, 2023, vol. 11, issue 24, 1-13

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.

Keywords: batch effect correction; clustering analysis; single-cell RNA sequencing; adversarial learning; Dirichlet process; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/24/4901/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/24/4901/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:24:p:4901-:d:1296042

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().