Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis
- 1College of Mathematics and Information Sciences, Guangzhou University, Guangzhou, P. R. China
- 2Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, P. R. China
Abstract
Single-cell transcriptomic data exhibit pervasive zero inflation, while traditional models either neglect this issue or fail to capture transcriptional burst-driven bimodality, hindering accurate gene regulatory studies. This study developed a zero-inflated telegraph model that integrates technical zero correction with the stochastic gene state-switching dynamics of the classical telegraph model. Systematic validation was conducted using synthetic data, human scRNA-seq data from lupus and breast cancer patients, and mouse embryonic stem cell scRNA-seq data. The model showed superior performance: it accurately fits mRNA distributions (including bimodal patterns), reliably estimates effective transcriptional burst parameters while preventing overfitting, thus enables correction of traditional models’ regulatory inference bias. It also outperforms conventional approaches in detecting differentially expressed genes, with notable advantages in small samples, and identifies unique disease-related genes (e.g., LDLR, GZMB for lupus, FAIM2, VDR for breast cancer). This biologically interpretable and robust tool advances single-cell transcriptomic analysis. Author summary: Single-cell studies demonstrate that gene expression is an inherently stochastic process driven by transcriptional bursting. Genes randomly toggle between active and inactive states, causing variable mRNA abundance even among genetically identical cells. The widespread absence of transcripts in such data arises from both biological noise and sequencing dropouts, a phenomenon referred to as zero inflation. Many existing approaches struggle to simultaneously address zero inflation and burst-associated expression dynamics. To address this challenge, we present a zero-inflated telegraph model that integrates technical zero correction with stochastic gene regulatory dynamics. Tested on synthetic data, mouse and human single-cell transcriptomic datasets, our model produces reliable mRNA distribution fits, distinguishes biological signals from technical artifacts, and yields reasonable estimates of transcriptional burst parameters. It also performs well in differential gene detection, especially with limited samples. This framework may serve as a useful tool for single-cell transcriptomic analysis.
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BibTeX
@article{yang2026integrating,
title = {{Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis}},
author = {Chengkai Yang and Yu Liao and Ying Sheng and Feng Jiao},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014779},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014779},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis
AU - Chengkai Yang
AU - Yu Liao
AU - Ying Sheng
AU - Feng Jiao
PY - 2026
DA - 2026/09/25
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014779
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014779
AB - Single-cell transcriptomic data exhibit pervasive zero inflation, while traditional models either neglect this issue or fail to capture transcriptional burst-driven bimodality, hindering accurate gene regulatory studies. This study developed a zero-inflated telegraph model that integrates technical zero correction with the stochastic gene state-switching dynamics of the classical telegraph model. Systematic validation was conducted using synthetic data, human scRNA-seq data from lupus and breast cancer patients, and mouse embryonic stem cell scRNA-seq data. The model showed superior performance: it accurately fits mRNA distributions (including bimodal patterns), reliably estimates effective transcriptional burst parameters while preventing overfitting, thus enables correction of traditional models’ regulatory inference bias. It also outperforms conventional approaches in detecting differentially expressed genes, with notable advantages in small samples, and identifies unique disease-related genes (e.g., LDLR, GZMB for lupus, FAIM2, VDR for breast cancer). This biologically interpretable and robust tool advances single-cell transcriptomic analysis. Author summary: Single-cell studies demonstrate that gene expression is an inherently stochastic process driven by transcriptional bursting. Genes randomly toggle between active and inactive states, causing variable mRNA abundance even among genetically identical cells. The widespread absence of transcripts in such data arises from both biological noise and sequencing dropouts, a phenomenon referred to as zero inflation. Many existing approaches struggle to simultaneously address zero inflation and burst-associated expression dynamics. To address this challenge, we present a zero-inflated telegraph model that integrates technical zero correction with stochastic gene regulatory dynamics. Tested on synthetic data, mouse and human single-cell transcriptomic datasets, our model produces reliable mRNA distribution fits, distinguishes biological signals from technical artifacts, and yields reasonable estimates of transcriptional burst parameters. It also performs well in differential gene detection, especially with limited samples. This framework may serve as a useful tool for single-cell transcriptomic analysis.
SN - 1553-7358
ER -