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```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) ``` # sigminer: an easy-to-use and scalable toolkit for genomic alteration signature analysis and visualization in R [](https://cran.r-project.org/package=sigminer) [](https://www.tidyverse.org/lifecycle/#stable)  [](https://codecov.io/github/ShixiangWang/sigminer?branch=master) [](https://cran.r-project.org/package=sigminer) [](https://github.com/ShixiangWang/sigminer/issues?q=is%3Aissue+is%3Aclosed) ## 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. ### Feature - supports a standard *de novo* pipeline for identification of two types of signatures: SBS signature and copy number signature - 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: ```{r, eval=FALSE} install.packages("sigminer") # Or BiocManager::install("sigminer") ``` You can install the development version of **sigminer** from Github with: ```{r, eval=FALSE} remotes::install_github("ShixiangWang/sigminer") # For Chinese users, run remotes::install_git("https://gitee.com/ShixiangWang/sigminer") ``` ## Usage A complete documentation of **sigminer** can be read online at (For Chinese users, you can also read it at ). All functions are well organized and documented at (For Chinese users, you can also read it at ). For usage of a specific function `fun`, run `?fun` in your R console to see its documentation. ## Citation *** ***Copy number signature analyses in prostate cancer reveal distinct etiologies and clinical outcomes, under submission*** *** Currently, **sigminer** has not been published. If you use **sigminer** in academic field, please cite our recent work: ``` 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 the paper implementing NMF in R: ``` 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. ``` 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. ## LICENSE The software is made available for non commercial research purposes only under the [MIT](https://github.com/ShixiangWang/sigminer/blob/master/LICENSE.md). 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 or Xue-Song Liu . MIT © 2019-2020 Shixiang Wang, Xue-Song Liu MIT © 2018 Geoffrey Macintyre MIT © 2018 Anand Mayakonda *** **[Cancer Biology Group](https://github.com/XSLiuLab) @ShanghaiTech** **Research group led by Xue-Song Liu in ShanghaiTech University**