JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1016 10.6339/21-JDS1016 Data Science in Action Mutstats: An Ultra-fast Computational Method to Determine Clonal Status of Somatic Mutations BiDehua1 SenguptaSubhajit2 ZhouTianjian3 JiYuanyji@health.bsd.uchicago.edu1∗ Public Health Sciences, University of Chicago, Chicago, IL, USA Cytel Inc, Cambridge, MA, USA Department of Statistics, Colorado State University, Fort Collins, CO, USA Corresponding author. Email: yji@health.bsd.uchicago.edu. 2021162021193465484 Supplementary Material

We include an Appendix on the Bayes model used by the PyClone method. In addition, the simulation data can be obtained from the website https://compgenome.shinyapps.io/tumorsim. Finally, the code of the Mutstats method and the real data used in this analysis can be found in the author’s Github page https://github.com/edwardbi/Mutstats.

23120211652021 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

Tumor cell population is a mixture of heterogeneous cell subpopulations, known as subclones. Identification of clonal status of mutations, i.e., whether a mutation occurs in all tumor cells or in a subset of tumor cells, is crucial for understanding tumor progression and developing personalized treatment strategies. We make three major contributions in this paper: (1) we summarize terminologies in the literature based on a unified mathematical representation of subclones; (2) we develop a simulation algorithm to generate hypothetical sequencing data that are akin to real data; and (3) we present an ultra-fast computational method, Mutstats, to infer clonal status of somatic mutations from sequencing data of tumors. The inference is based on a Gaussian mixture model for mutation multiplicities. To validate Mutstats, we evaluate its performance on simulated datasets as well as two breast carcinoma samples from The Cancer Genome Atlas project.

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