📖 Introduction


🎯 Motivation

The book is written as a guide for extracting, analyzing and visualizing mutational signatures with R/CRAN package sigminer. The README and Reference list of sigminer have given users overview and the very details of specific points (e.g., functions) in sigminer. This book will help users focus on quickly getting the mutational signature analysis done to make life easy.

In this book, we assume you have already known how to operate R.

📝 Citation

If you use sigminer or its pipeline version sigflow in published research, please cite the most appropriate paper(s):

  1. Wang, S., Li, H., Song, M., Tao, Z., Wu, T., He, Z., … & Liu, X. S. (2021). Copy number signature analysis tool and its application in prostate cancer reveals distinct mutational processes and clinical outcomes. PLoS genetics, 17(5), e1009557. https://doi.org/10.1371/journal.pgen.1009557
  2. Wang, S., Tao, Z., Wu, T., & Liu, X. S. (2021). Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis. Bioinformatics, 37(11), 1590-1592. https://doi.org/10.1093/bioinformatics/btaa895

Please properly cite the following references when you are using any corresponding features. The references are also listed in the function documentation. Very thanks to the works, sigminer cannot be created without the giants.

  1. Mayakonda, Anand, et al. “Maftools: efficient and comprehensive analysis of somatic variants in cancer.” Genome research 28.11 (2018): 1747-1756.
  2. Gaujoux, Renaud, and Cathal Seoighe. “A Flexible R Package for Nonnegative Matrix Factorization.”” BMC Bioinformatics 11, no. 1 (December 2010).
  3. Wickham, Hadley. “ggplot2.” Wiley Interdisciplinary Reviews: Computational Statistics 3.2 (2011): 180-185.
  4. Kim, Jaegil, et al. “Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors.” Nature Genetics 48.6 (2016): 600.
  5. Alexandrov, Ludmil B., et al. “Deciphering signatures of mutational processes operative in human cancer.” Cell Reports 3.1 (2013): 246-259.
  6. Degasperi, Andrea, et al. “A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies.” Nature Cancer 1.2 (2020): 249-263.
  7. Alexandrov, Ludmil B., et al. “The repertoire of mutational signatures in human cancer.” Nature 578.7793 (2020): 94-101.
  8. Macintyre, Geoff, et al. “Copy number signatures and mutational processes in ovarian carcinoma.” Nature Genetics 50.9 (2018): 1262.
  9. 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.
  10. 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

📚 Book structure

  • Part 1 (Background and Prerequisite) describes the basic concepts of mutational signature and how to install/load sigminer.
  • Part 2 (Workflows) introduces how to prepare your input data and run mutational signature analysis for different mutation data types (SBS, DBS, INDEL, Genome rearrangement, CNV) with different methods provided by sigminer.
  • Part 3 (Miscellaneous topics) describes useful utilities including builtin datasets, SBS signature conversion.

💖 Want to help?

The book’s source code is hosted on GitHub, at https://github.com/ShixiangWang/sigminer-book. Any feedback on the book is very welcome. Feel free to open an issue on GitHub or send me a pull request if you notice typos or other issues (I’m not a native English speaker ;) ).

🐜 Bug report or feature request

If you find any bugs or want to have a new feature, please file an issue.

🌵 Acknowlegment

I built this book website by imitating Biomedical knowledge mining using GOSemSim and clusterProfiler and reusing its configurations, I would like to thank Guangchuang Yu here.