Comparative analysis of NovaSeq 6000 and MGISEQ 2000 single-cell RNA sequencing data

Abstract

Background: Single-cell RNA sequencing (scRNA-seq) technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specific biological processes. Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples. Therefore, benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA-seq data. However, recent comparing efforts were constrained in sequencing platforms or analyzing pipelines. There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity, precision, and so on.

Methods: We downloaded public scRNA-seq data that was generated by two distinct sequencers, NovaSeq 6000 and MGISEQ 2000. Then data was processed through the Drop-seq-tools, UMI-tools and Cell Ranger pipeline respectively. We calculated multiple measurements based on the expression profiles of the six platform-pipeline combinations.

Results: We found that all three pipelines had comparable performance, the Cell Ranger pipeline achieved the best performance in precision while UMI-tools prevailed in terms of sensitivity and marker calling.

Conclusions: Our work provided an insight into the selection of scRNA-seq data processing tools for two sequencing platforms as well as a framework to evaluate platform-pipeline combinations.

Author summary

We proposed that evaluating scRNA-seq data processing pipelines should aim at comparing the sequencer-pipeline combinations rather than benchmarking between either sequencers or pipelines. We compared sequencer-pipeline combinations in aspect of gene detection, dropout rates, number of markers and cell types. Based on results above we made recommendations for different purposes of research such as finding more marker genes or gaining maximum precision.

Graphical abstract

Keywords

Single-cell RNA sequencing / cell-type / data processing / pipeline / platform

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/ J-QB-022-0295.

ACKNOWLEDGEMENTS

This work was supported by Strategic Priority Research Program of Chinese Academy of Sciences (Nos. XDB38050200 and XDA26040304).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Weiran Chen, Md Wahiduzzaman, Quan Li, Yixue Li, Guangyong Zheng and Tao Huang declare that they have no conflict of interest or financial conflicts to disclose.

This article does not contain any studies with human or animal subjects performed by any of the authors.

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2022 The Author (s). Published by Higher Education Press.