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Single-cell RNA sequencing data have complex features such as dropout events,over-dispersion, and high-magnitude outliers, resulting in complicatedprobability distributions of mRNA abundances that are statisticallycharacterized in terms of a zero-inflated negative binomial (ZINB) model. Herewe provide a mesoscopic kinetic foundation of the widely used ZINB model basedon the biochemical reaction kinetics underlying transcription. Using multiscalemodeling and simplification techniques, we show that the ZINB distribution ofmRNA abundance and the phenomenon of transcriptional bursting naturally emergefrom a three-state stochastic transcription model. We further reveal anontrivial quantitative relation between dropout events and transcriptionalbursting, which provides novel insights into how and to what extent the burstsize and burst frequency could reduce the dropout rate. Three differentbiophysical origins of over-dispersion are also clarified at the single-celllevel.

Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data