DIFFpop: a stochastic computational approach to simulate differentiation hierarchies with single cell barcoding.

Abstract

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. DIFFpop is available as an R-package along with vignettes on Github (https://github.com/ferlicjl/diffpop). Supplementary data are available at Bioinformatics online.