The application provides a model for identifying
allele imbalance markers driving tumor evolution based on a hierarchical
Bayesian framework and a construction method thereof, and belongs to the field of
information technology.The application provides a construction method of a model for identifying
allele imbalance markers driving tumor evolution based on a hierarchical
Bayesian framework, a three-level Bayesian hierarchical model is constructed, global
noise, subclone specificity, regional discreteness and
allele single nucleotide mutation (SNV) site observation are jointly modeled, and a reparameterization correction allele copy number deviation is introduced.On this basis, a Markov Monte Carlo (MCMC) sampling is used to obtain a posterior distribution of parameters, and a sample comparison method of a posterior distribution probability of each parameter including a true allele imbalance coefficient and a Kullback-Leibler
divergence is provided, which can be used for identifying allele imbalance and analyzing markers driving evolution.