Jeremy Ferlic1,2, Jiantao Shi1,2,3, Thomas O McDonald1,2,3,4, Franziska Michor1,2,3,4,5,6. 1. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. 2. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 3. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA. 4. Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA. 5. The Broad Institute of Harvard and MIT, Cambridge, MA, USA. 6. The Ludwig Center at Harvard, Boston, MA, USA.
Abstract
SUMMARY: DIFFpop is an R package designed to simulate cellular differentiation hierarchies using either exponentially-expanding or fixed population sizes. The software includes functionalities to simulate clonal evolution due to the emergence of driver mutations under the infinite-allele assumption as well as options for simulation and analysis of single cell barcoding and labeling data. The software uses the Gillespie Stochastic Simulation Algorithm and a modification of expanding or fixed-size stochastic process models expanded to a large number of cell types and scenarios. AVAILABILITY AND IMPLEMENTATION: DIFFpop is available as an R-package along with vignettes on Github (https://github.com/ferlicjl/diffpop). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: DIFFpop is an R package designed to simulate cellular differentiation hierarchies using either exponentially-expanding or fixed population sizes. The software includes functionalities to simulate clonal evolution due to the emergence of driver mutations under the infinite-allele assumption as well as options for simulation and analysis of single cell barcoding and labeling data. The software uses the Gillespie Stochastic Simulation Algorithm and a modification of expanding or fixed-size stochastic process models expanded to a large number of cell types and scenarios. AVAILABILITY AND IMPLEMENTATION: DIFFpop is available as an R-package along with vignettes on Github (https://github.com/ferlicjl/diffpop). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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