RT Journal Article
SR Electronic
T1 Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 827840
DO 10.1101/827840
A1 Chen Jia
YR 2019
UL http://biorxiv.org/content/early/2019/11/01/827840.abstract
AB Single-cell RNA sequencing data have complex features such as dropout events, over-dispersion, and high-magnitude outliers, resulting in complicated probability distributions of mRNA abundances that are statistically characterized in terms of a zero-inflated negative binomial (ZINB) model. Here we provide a mesoscopic kinetic foundation of the widely used ZINB model based on the biochemical reaction kinetics underlying transcription. Using multiscale modeling and simplification techniques, we show that the ZINB distribution of mRNA abundance and the phenomenon of transcriptional bursting naturally emerge from a three-state stochastic transcription model. We further reveal a nontrivial quantitative relation between dropout events and transcriptional bursting, which provides novel insights into how and to what extent the burst size and burst frequency could reduce the dropout rate. Three different biophysical origins of over-dispersion are also clarified at the single-cell level.