sigminer: an easy-to-use and scalable toolkit for genomic alteration signature analysis and visualization in R

Overview

Genomic alterations including single nucleotide substitution (SBS), copy number alteration (CNA), etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called ‘signature’. This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.

SBS signatures:

Copy number signatures:

DBS signatures:

INDEL (i.e. ID) signatures:

Feature

• supports a standard de novo pipeline for identification of 4 types of signatures: copy number, SBS, DBS and INDEL
• supports quantify exposure for one sample based on known signatures
• supports two methods for calling copy number signatures: one is from Macintyre et al. 2018 and the other is created by us
• supports association and group analysis and visualization for signatures
• supports a bayesian variant of NMF algorithm to enable optimal inferences for the number of signatures through the automatic relevance determination technique from SignatureAnalyzer package
• supports two plot styles for signature profile: ‘default’ (like SignatureAnalyzer package) and ‘cosmic’ (like COSMIC database)
• supports two types of signatrue exposures: relative exposure (relative contribution of signatures in each sample) and absolute exposure (estimated variation records of signatures in each sample)
• supports basic summary and visualization for profile of mutation (powered by maftools) and copy number
• supports parallel computation by R packages foreach, future and NMF
• efficient code powered by R packages data.table and tidyverse
• elegant plots powered by R packages ggplot2, ggpubr, cowplot and patchwork
• well tested by R package testthat and documented by R package roxygen2, roxytest, pkgdown, and etc. for both reliable and reproducible research

Installation

You can install the stable release of sigminer from CRAN with:

install.packages("sigminer", dependencies = TRUE)
# Or
BiocManager::install("sigminer", dependencies = TRUE)

You can install the development version of sigminer from Github with:

remotes::install_github("ShixiangWang/sigminer", dependencies = TRUE)
# For Chinese users, run 
remotes::install_git("https://gitee.com/ShixiangWang/sigminer", dependencies = TRUE)

Usage

A complete documentation of sigminer can be read online at https://shixiangwang.github.io/sigminer-doc/ (For Chinese users, you can also read it at https://shixiangwang.gitee.io/sigminer-doc). All functions are well organized and documented at https://shixiangwang.github.io/sigminer/reference/index.html (For Chinese users, you can also read it at https://shixiangwang.gitee.io/sigminer/reference/index.html). For usage of a specific function fun, run ?fun in your R console to see its documentation.

Citation

Wang, Shixiang, et al. “Copy number signature analyses in prostate cancer reveal distinct etiologies and clinical outcomes” medRxiv (2020) https://www.medrxiv.org/content/early/2020/04/29/2020.04.27.20082404

Acknowledgments

If you use NMF package in R, please also cite:

Gaujoux, Renaud, and Cathal Seoighe. "A Flexible R Package for 
    Nonnegative Matrix Factorization."" BMC Bioinformatics 11, no. 1 (December 2010).

The method “M” for extracting copy number signatures was based in part on the source code from paper Copy number signatures and mutational processes in ovarian carcinoma, if you use this feature, please also cite:

Macintyre, Geoff, et al. "Copy number signatures and mutational
    processes in ovarian carcinoma." Nature genetics 50.9 (2018): 1262.

The code for extracting SBS signatures was based in part on the source code of the maftools package, if you use this feature, please also cite:

Mayakonda, Anand, et al. "Maftools: efficient and comprehensive analysis
    of somatic variants in cancer." Genome research 28.11 (2018): 1747-1756.

The code for extracting mutational signatures was based in part on the source code of the SignatureAnalyzer package, if you use this feature, please also cite:

Kim, Jaegil, et al. "Somatic ERCC2 mutations are associated with a distinct genomic
    signature in urothelial tumors." Nature genetics 48.6 (2016): 600.

References

1. Alexandrov, Ludmil B., et al. “The repertoire of mutational signatures in human cancer.” Nature 578.7793 (2020): 94-101.
2. Macintyre, Geoff, et al. “Copy number signatures and mutational processes in ovarian carcinoma.” Nature genetics 50.9 (2018): 1262.
3. Mayakonda, Anand, et al. “Maftools: efficient and comprehensive analysis of somatic variants in cancer.” Genome research 28.11 (2018): 1747-1756.
4. Gaujoux, Renaud, and Cathal Seoighe. “A Flexible R Package for Nonnegative Matrix Factorization.”" BMC Bioinformatics 11, no. 1 (December 2010).
5. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
6. Tan, Vincent YF, and Cédric Févotte. “Automatic relevance determination in nonnegative matrix factorization with the/spl beta/-divergence.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35.7 (2012): 1592-1605.
7. Kim, Jaegil, et al. “Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors.” Nature genetics 48.6 (2016): 600.
8. Bergstrom EN, Huang MN, Mahto U, Barnes M, Stratton MR, Rozen SG, Alexandrov LB: SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 2019, 20:685 https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6041-2

LICENSE

The software is made available for non commercial research purposes only under the MIT. However, notwithstanding any provision of the MIT License, the software currently may not be used for commercial purposes without explicit written permission after contacting Shixiang Wang wangshx@shanghaitech.edu.cn or Xue-Song Liu liuxs@shanghaitech.edu.cn.

MIT © 2019-2020 Shixiang Wang, Xue-Song Liu

MIT © 2018 Geoffrey Macintyre

MIT © 2018 Anand Mayakonda

Cancer Biology Group @ShanghaiTech

Research group led by Xue-Song Liu in ShanghaiTech University