Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data

Wenlong He, Peng Xia, Xinan Zhang () and Tianhai Tian ()
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Wenlong He: School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
Peng Xia: School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
Xinan Zhang: School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
Tianhai Tian: School of Mathematics, Monash University, Clayton, VIC 3800, Australia

Mathematics, 2022, vol. 10, issue 24, 1-15

Abstract: The rapid progress in biological experimental technologies has generated a huge amount of experimental data to investigate complex regulatory mechanisms. Various mathematical models have been proposed to simulate the dynamic properties of molecular processes using the experimental data. However, it is still difficult to estimate unknown parameters in mathematical models for the dynamics in different cells due to the high demand for computing power. In this work, we propose a population statistical inference algorithm to improve the computing efficiency. In the first step, this algorithm clusters single cells into a number of groups based on the distances between each pair of cells. In each cluster, we then infer the parameters of the mathematical model for the first cell. We propose an adaptive approach that uses the inferred parameter values of the first cell to formulate the prior distribution and acceptance criteria of the following cells. Three regulatory network models were used to examine the efficiency and effectiveness of the designed algorithm. The computational results show that the new method reduces the computational time significantly and provides an effective algorithm to infer the parameters of regulatory networks in a large number of cells.

Keywords: population model; parameter inference; heterogeneity; regulatory network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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