Tutorial – current best practices in single‐cell RNA‐seq analysis

Single‐cell RNA‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one’s data. Here, researchers from Helmholtz Zentrum München detail the steps of a typical single‐cell RNA‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. They formulate current best‐practice recommendations for these steps based on independent comparison studies. The have have integrated these best‐practice recommendations into a workflow, which they apply to a public dataset to further illustrate how these steps work in practice. This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

Schematic of a typical single‐cell RNA‐seq analysis workflow

rna-seq

Raw sequencing data are processed and aligned to give count matrices, which represent the start of the workflow. The count data undergo pre‐processing and downstream analysis. Subplots are generated using the best‐practices workflow on intestinal epithelium data from Haber et al (2017).

Availability – The documented case study can be found at https://www.github.com/theislab/single-cell-tutorial.

Luecken MD, Theis FJ. (2019) Current best practices in single‐cell RNA‐seq analysis: a tutorial. Mol Syst Biol (2019)15:e8746. [article]

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