A Bayesian framework for inferring heterogeneity of cellular processes using single-cell data

Wenlong He, Peng Xia, Xinan Zhang, Tianhai Tian

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

1 Citation (Scopus)


The advances of single-cell technologies provide un-precedented opportunities to investigate heterogeneity of cellular processes. Although population modelling is a powerful tool to describe different cellular dynamics in a number of cells, it is a challenge and computational burden to infer different sets of parameters in the mathematical models based on the observations in different cells. To address this issue, this work proposes a population Monte Carlo framework to increase the acceptance rates in the approximate Bayesian computation algorithms. We first test a prior distribution and distinct tolerance threshold sequence for the dataset of the first cell. After applying the Bayesian inference methods, we obtain the estimated model parameters of the first cell. This inferred parameter set will be used to construct the prior distribution and tolerance threshold sequence of the following cells. This adaptive approach will be repeatedly applied for the inference of the following cells. The accuracy and efficacy of the proposed algorithms is rigorously examined by using two mathematical models for genetic networks. Numerical results shows that the proposed algorithm improve the efficiency substantially and provide a powerful tool to obtain more accurate inference results for large-scale regulatory networks.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781665401265
Publication statusPublished - 2021
EventIEEE International Conference on Bioinformatics and Biomedicine 2021 - Virtual, Online, United States of America
Duration: 9 Dec 202112 Dec 2021
https://ieeexplore.ieee.org/xpl/conhome/9669261/proceeding (Proceedings)

Publication series

NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021


ConferenceIEEE International Conference on Bioinformatics and Biomedicine 2021
Abbreviated titleBIBM 2021
Country/TerritoryUnited States of America
CityVirtual, Online
Internet address


  • Approximate Bayesian Computation
  • Population Monte-Carlo
  • Single-cell data

Cite this