Yixf-Self / scrna-recom

collected resource for scRNA-seq analysis

scrna-recom

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

Workflow

Framework

• 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

Current wrapped tools

	Package	Tutorial
Raw Data Processing	Cell Ranger	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger
Quality Control	DoubletFinder	https://github.com/chris-mcginnis-ucsf/DoubletFinder
	Seurat	https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
	SoupX	https://rawcdn.githack.com/constantAmateur/SoupX/204b602418df12e9fdb4b68775a8b486c6504fe4/inst/doc/pbmcTutorial.html
Normalization	Seurat	https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#normalizing-the-data-1
Integration	Seurat:CCA,RPCA	https://satijalab.org/seurat/articles/integration_rpca.html
	Liger	https://github.com/welch-lab/liger
	Harmony	https://github.com/immunogenomics/harmony
Dimensional Reduction	Seurat:PCA,UMAP	https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#perform-linear-dimensional-reduction-1
Clustering	Seurat	https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#cluster-the-cells-1
Cell type annotation	SingleR	https://github.com/dviraran/SingleR
	scCATCH	https://github.com/ZJUFanLab/scCATCH
Regulon analysis	SCENIC	https://pyscenic.readthedocs.io/en/latest/
Trajectory inference	Monocle3	https://cole-trapnell-lab.github.io/monocle3/
	scVelo	https://github.com/theislab/scvelo
Cell communication	CellChat	https://github.com/sqjin/CellChat
Metabolic analysis	scMetabolism	https://github.com/wu-yc/scMetabolism
	scFEA	https://github.com/changwn/scFEA