PyClone: statistical inference of clonal population structure in cancer. We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone’s accuracy.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Zeng, Li; Warren, Joshua L.; Zhao, Hongyu: Phylogeny-based tumor subclone identification using a Bayesian feature allocation model (2019)
- Zhou, Tianjian; Sengupta, Subhajit; Müller, Peter; Ji, Yuan: TreeClone: reconstruction of tumor subclone phylogeny based on mutation pairs using next generation sequencing data (2019)
- Behr, Merle; Holmes, Chris; Munk, Axel: Multiscale blind source separation (2018)
- Xie, Fangzheng; Zhou, Mingyuan; Xu, Yanxun: Baycount: a Bayesian decomposition method for inferring tumor heterogeneity using RNA-seq counts (2018)
- Della Vedova, Gianluca; Patterson, Murray; Rizzi, Raffaella; Soto, Mauricio: Character-based phylogeny construction and its application to tumor evolution (2017)
- Zhou, T., Müller, P., Sengupta, S., Ji, Y.: PairClone: A Bayesian Subclone Caller Based on Mutation Pairs (2017) arXiv
- Ji, Yuan; Sengupta, Subhajit; Lee, Juhee; Müller, Peter; Gulukota, Kamalakar: Estimating latent cell subpopulations with Bayesian feature allocation models (2015)
- Lee, Juhee; Müller, Peter; Gulukota, Kamalakar; Ji, Yuan: A Bayesian feature allocation model for tumor heterogeneity (2015)