Current best practices in single-cell RNA-seq analysis: a tutorial

Mol Syst Biol. 2019 Jun 19;15(6):e8746. doi: 10.15252/msb.20188746.


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, we 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. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

Keywords: analysis pipeline development; computational biology; data analysis tutorial; single‐cell RNA‐seq.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Gene Expression Profiling / methods*
  • Guidelines as Topic
  • High-Throughput Nucleotide Sequencing
  • Internet
  • Sequence Analysis, RNA
  • Single-Cell Analysis / methods*
  • Workflow