A cell marker-based clustering strategy (cmCluster) for precise cell type identification of scRNA-seq data

Abstract

Background: The precise and efficient analysis of single-cell transcriptome data provides powerful support for studying the diversity of cell functions at the single-cell level. The most important and challenging steps are cell clustering and recognition of cell populations. While the precision of clustering and annotation are considered separately in most current studies, it is worth attempting to develop an extensive and flexible strategy to balance clustering accuracy and biological explanation comprehensively.

Methods: The cell marker-based clustering strategy (cmCluster), which is a modified Louvain clustering method, aims to search the optimal clusters through genetic algorithm (GA) and grid search based on the cell type annotation results.

Results: By applying cmCluster on a set of single-cell transcriptome data, the results showed that it was beneficial for the recognition of cell populations and explanation of biological function even on the occasion of incomplete cell type information or multiple data resources. In addition, cmCluster also produced clear boundaries and appropriate subtypes with potential marker genes. The relevant code is available in GitHub website (huangyuwei301/cmCluster).

Conclusions: We speculate that cmCluster provides researchers effective screening strategies to improve the accuracy of subsequent biological analysis, reduce artificial bias, and facilitate the comparison and analysis of multiple studies.

Author summary

The importance and challenge of clustering method for scRNA-seq data is that recent methods introduce difficulty and bias in the identification of cell types and cell function explanation. We proposed a cell marker-based clustering strategy (cmCluster) to determine accurate clusters by introducing a knowledge benchmark during the fine adjustment of clustering. cmCluster helped to obtain finer cell populations for single-cell transcriptome data with both known and unknown cell type labels. And these populations will be suitable to identify cell types or acquire features especially for massive scRNA-seq data with complex or potential novel cell types.

Graphical abstract

Keywords

single-cell RNA-seq / clustering / cell markers / novel cell types

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/J-QB-022-0311

ACKNOWLEDGMENTS

This work was supported by National Major Scientific Instrument and Equipment Development Project of NSFC (81827901), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB38030100 and XDB38050200), II Phase External Project of Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences (2020YJY0217) and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Yuwei Huang, Huidan Chang, Xiaoyi Chen, Jiayue Meng, Mengyao Han, Tao Huang, Liyun Yuan and Guoqing Zhang declare that they have no conflicts of interest.

This article does not contain any studies with human or animal subjects performed by any of the authors. All procedures performed in studies were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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