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.
- R version: >= 3.5.0
Firstly, please install or update the package
devtools by running
scCancer can be installed via
❗ ❗ ❗
- A dependent package
NNLMwas removed from the CRAN repository recently, so an error about it may be reported during the installation. If so, you can install its a formerly available version by following codes or install manually from its archive.
install.packages("RcppArmadillo") install.packages("RcppProgress") install.packages('http://lifeome.net/software/sccancer/packages/NNLM_0.4.3.tar.gz', type='source')
- Some dependent packages on GitHub (as follows) may not be able to install automatically, if you encounter such errors, please refer to their GitHub and install them via corresponding commands.
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, Jin Gu. 2019. bioRxiv doi: https://doi.org/10.1101/800490