Benchmarking UMI-based single-cell RNA-seq preprocessing workflows

Genome Biology

Jafar S. Jabbari¹  Shian Su²  Xueyi Dong²  Yue You²  Luyi Tian²  Matthew E. Ritchie³  Peter F. Hickey⁴ 

[1] Australian Genome Research Facility, Victorian Comprehensive Cancer Centre, Melbourne, Australia;Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia;Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia;Department of Medical Biology, The University of Melbourne, Parkville, Australia;Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia;Department of Medical Biology, The University of Melbourne, Parkville, Australia;School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia;Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia;Department of Medical Biology, The University of Melbourne, Parkville, Australia;Single-Cell Open Research Endeavour (SCORE), The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia;

关键词: scRNA-seq;    Transcriptomics;    Methods comparison;    Sequencing analysis;    Preprocessing;   

DOI  :  10.1186/s13059-021-02552-3

来源: Springer

【 摘 要 】

BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied.ResultsHere, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. While the scRNA-seq preprocessing workflows compared vary in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produce clustering results that agree well with the known cell type labels that provided the ground truth in our analysis.ConclusionsIn summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

【 授权许可】

CC BY