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

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…

• SV
  Silicon Valley Community Foundation

  Award: 182835
• CZ
  Chan Zuckerberg Initiative

  Award: 182835
• H
  Helmholtz-Gemeinschaft

  Award: ZT‐I‐0007
• DF
  Deutsche Forschungsgemeinschaft

  Awards: ZT-I-0007, Subproject A17
• BF
  Bundesministerium für Bildung und Forschung

  Awards: 01IS18053A, 01IS18036A