scCancer

Introduction

The 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: scStatistics, scAnnotation, and scCombination.

• The scStatistics performs basic statistical analyses of raw data and quality control.
• The scAnnotation performs 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.
• The scCombination perform 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)

System Requirements

• 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).

Current version

• scCancer 2.2.1 (update at 2021.03.02)
• All version log

Installation

The detailed installation instruction can be found in the project wiki.

Usage

The vignette of scCancer can be found in the project wiki.

• Quick start
• Step by step introduction
• Other personalized settings

We provide an example data of kidney cancer from 10X Genomics, and following are the generated HTML reports:

• report-scStat.html
• report-scAnno.html

For multi-datasets, following is a generated HTML report for three kidney cancer samples integration analysis:

• report-scAnnoComb.html

Citation

Please use the following citation:

[1] 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

[2] Zeyu Chen, Yuxin Miao, Zhiyuan Tan, Qifan Hu, Yanhong Wu, Xinqi Li, Wenbo Guo, Jin Gu, scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution, Bioinformatics, Volume 40, Issue 2, February 2024, btae028, https://doi.org/10.1093/bioinformatics/btae028

License

GPL-3