title: PurBayes: estimating tumor cellularity and subclonality in next-generation sequencing data.
abstract: CONCLUSIONS
  We have developed a novel Bayesian method, PurBayes, to estimate tumor purity and detect intratumor heterogeneity based on next-generation sequencing data of paired tumor-normal tissue samples, which uses finite mixture modeling methods. We demonstrate our approach using simulated data and discuss its performance under varying conditions.
  
  
  BACKGROUND
  PurBayes is implemented as an R package, and source code is available for download through CRAN at http://cran.r-project.org/package=PurBayes.
  
  
  BACKGROUND
  larson.nicholas@mayo.edu
  
  
  BACKGROUND
  Supplementary data are available online at Bioinformatics online.
authors.affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55901, USA. larson.nicholas@mayo.edu
authors.forename: Nicholas B
authors.initials: NB
authors.lastname: Larson
authors.forename: Brooke L
authors.initials: BL
authors.lastname: Fridley
mesh_terms: D001499:Bayes Theorem; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D012984:Software