scCancer package focuses on processing and analyzing droplet-based scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features.
The workflow of
scCancer mainly consists of three modules:
scStatisticsperforms basic statistical analyses of raw data and quality control.
scAnnotationperforms functional data analyses and visualizations, such as low dimensional representation, clustering, cell type classification, cell malignancy estimation, cellular phenotype analyses, gene signature analyses, cell-cell interaction analyses, etc.
scCombinationperform multiple samples data integration, batch effect correction and analyses visualization.
After the computational analyses, detailed and graphical reports were generated in user-friendly HTML format.
(Click to view larger workflow picture)
- R version: >= 3.5.0 (suggest: R 3.6, not 4.0)
- Hint: For R (version>=4.0) under Windows system, the Rtools needs to be updated to version 4.0 from https://cran.r-project.org/bin/windows/Rtools/. So, if you are not familiar with R environment configuration, we don't suggest to use R (>=4.0).
- scCancer 2.2.1 (update at 2021.03.02)
- All version log
The detailed installation instruction can be found in the project wiki.
The vignette of
scCancer can be found in the project wiki.
We provide an example data of kidney cancer from 10X Genomics, and following are the generated HTML reports:
For multi-datasets, following is a generated HTML report for three kidney cancer samples integration analysis:
Please use the following citation:
Wenbo Guo, Dongfang Wang, Shicheng Wang, Yiran Shan, Changyi Liu, Jin Gu, scCancer: a package for automated processing of single-cell RNA-seq data in cancer, Briefings in Bioinformatics, bbaa127, https://doi.org/10.1093/bib/bbaa127