Work

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

Year: 2019

Type: article

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.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btz074

Authors: Jeremy Ferlic, Jiantao Shi, Thomas O McDonald, Franziska Michor

Institutions: Harvard University, Dana-Farber Cancer Institute, Broad Institute, Massachusetts Institute of Technology

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, U.S. National Library of Medicine

Awards: Statistical and Quantitative Training in Big Data Health Science, Evolution and Treatment Response of Brain, Breast, and Hematologic Malignancies