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 specif...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.展开更多
基金This work was supported by Strategic Priority Research Program of Chinese Academy of Sciences(Nos.XDB38050200 and XDA26040304).
文摘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.
