A Poisson-Beta model – for inferring the kinetics of stochastic gene expression from single-cell RNA-Seq data

Genetically identical populations of cells grown in the same environmental condition show substantial variabilityin gene expression profiles. Although single-cell RNA-seq provides an opportunity to explore this phenomenon, statistical methods need to be developed to interpret the variability of gene expression counts.

Researchers at the European Bioinformatics Institute, UK have developed a statistical framework for studying the kinetics of stochastic gene expression from single-cell RNA-seq data. By applying this model to a single-cell RNA-seq dataset generated by profiling mouse embryonic stem cells, they find that the inferred kinetic parameters are consistent with RNA polymerase II binding and chromatin modifications. Their results suggest that histone modifications affect transcriptional bursting by modulating both burst size and frequency.

Furthermore, they show that their model can be used to identify genes with slow promoter kinetics, which are important for probabilistic differentiation of embryonic stem cells.

Availability – The MATLAB source code, and a compiled version of the same, are available at: http://genomebiology.com/imedia/6132151659020737/supp4.zip

  • Kim JK, Marioni JC. (2013) Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data. Genome Biol 14(1), R7. [Epub ahead of print]. [abstract]