A collected resource for scRNA-seq data analysis with biomedical applications

It is challenging for biomedical researchers without bioinformatics background to understand every detail in scRNA-seq data analysis and conduct data analysis for their own samples. For instance, scRNA-seq data analysis requires installation of specific software tools and running through the scripts written with programming languages such as R and Python.

Along with the recommended workflow, we also provide example computational scripts together with the software environment setting, which may facilitate researchers to conduct the data analysis locally.

Instructions with practical examples can be found at:

• https://github.com/WXlab-NJMU/scrna-recom/wiki

Complete list of tools in the paper can be found at:

• https://github.com/WXlab-NJMU/scrna-recom/blob/main/misc/tools.md

• R packages are wrapped in scrnaRecom:

  • qc: DoubletFinder, SoupX, Seurat
  • integration: Liger and Harmony
  • normalization, reduction and cluster: Seurat
  • cell annotation: singR and scCATCH
  • trajectory prediction: Monocle3
  • cell communication: CellChat
  • metabolic flux: scMetabolism

• Python packages and executations are wrapped in pyscrnarecom:

  • rawdata: CellRanger
  • qc, normalization, reduction and cluster: scanpy
  • regulon analysis: pySCENIC
  • trajectory prediction: scVelo
  • metabolic flux: scFEA