Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.

ABSTRACT:

Motivation

Gene expression is characterized by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data are prone to technical variability, including low and variable capture efficiency of transcripts from individual cells.

Results

Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to

SUBMITTER: Tang W 

PROVIDER: S-EPMC10318389 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature