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

An, Sijing; Shi, Jinhui; Liu, Runyan; Wang, Jing; Hu, Shuofeng; Dong, Guohua; Ying, Xiaomin; He, Zhen

doi:10.3390/math11244901

Open AccessArticle

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

by

Sijing An

,

Jinhui Shi

,

Runyan Liu

,

Jing Wang

,

Shuofeng Hu

,

Guohua Dong

,

Xiaomin Ying

^*

and

Zhen He

^*

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

^*

Authors to whom correspondence should be addressed.

Mathematics 2023, 11(24), 4901; https://doi.org/10.3390/math11244901

Submission received: 11 October 2023 / Revised: 27 November 2023 / Accepted: 6 December 2023 / Published: 7 December 2023

(This article belongs to the Special Issue Mathematical Models and Computer Science Applied to Biology)

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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

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MDPI and ACS Style

An, S.; Shi, J.; Liu, R.; Wang, J.; Hu, S.; Dong, G.; Ying, X.; He, Z. A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data. Mathematics 2023, 11, 4901. https://doi.org/10.3390/math11244901

AMA Style

An S, Shi J, Liu R, Wang J, Hu S, Dong G, Ying X, He Z. A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data. Mathematics. 2023; 11(24):4901. https://doi.org/10.3390/math11244901

Chicago/Turabian Style

An, Sijing, Jinhui Shi, Runyan Liu, Jing Wang, Shuofeng Hu, Guohua Dong, Xiaomin Ying, and Zhen He. 2023. "A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data" Mathematics 11, no. 24: 4901. https://doi.org/10.3390/math11244901

APA Style

An, S., Shi, J., Liu, R., Wang, J., Hu, S., Dong, G., Ying, X., & He, Z. (2023). A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data. Mathematics, 11(24), 4901. https://doi.org/10.3390/math11244901

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

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