Capture Genomic Variation Signatures using Non-Negative Matrix Factorization

Contains functions for identification of copy number signatures (Geoffrey et al. (2018) ) and mutation signatures (Alexandrov et al. (2013) ) by non-negative matrix factorization, signature analysis and visualization. It can be used to capture signatures of genomic variation, compare genotype or phenotype features of different signatures and thus uncover the relationship between the mechanism of genomic variation and phenotypes in cancer.

Overview

The goal of sigminer is to provide an uniform interface for genomic variation signature analysis and visualization.

sigminer is powered by NMF package and maftools package.

Installation

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

install.packages("sigminer")
# Or
BiocManager::install("sigminer")

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

remotes::install_github("ShixiangWang/sigminer")

Citation

If you use sigminer in academic field, please cite:

Wang, Shixiang, et al. "The predictive power of tumor mutational burden 
    in lung cancer immunotherapy response is influenced by patients' sex."
    International journal of cancer (2019).

and

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

Acknowledgments

The code 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 mutational 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.

LICENSE

MIT © 2019 Shixiang Wang, Geoffrey Macintyre, Xue-Song Liu