Preprocessing method was found to be less important than other steps in the scRNA-seq analysis process

Single-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.

Researchers at the Walter and Eliza Hall Institute of Medical Research systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell RangerOptimussalmon alevinalevin-frykallisto bustools, dropSeqPipescPipezUMIscelseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. The researchers 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 the analysis.

Overview of scRNA-seq preprocessing workflows and study design

Fig. 1

(A) A typical preprocessing workflow begins with raw sequences in FASTQ files that are subject to cell barcode (CB) detection, alignment, UMI correction, count matrix generation, and quality control. (B) Summary of benchmarking study, showing the datasets analyzed, the selected preprocessing workflows and methods for normalization and clustering that were compared. Workflows and methods used in analysis are listed in boxes with solid borders, while evaluation metrics are shown in boxes with dashed borders. In total, 3870 combinations of datasets × preprocessing workflows × downstream analysis methods were generated in this study

In summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. This study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

You Y, Tian L, Su S et al. (2021) Benchmarking UMI-based single-cell RNA-seq preprocessing workflows. Genome Biol 22, 339. [article]

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