Bayesian Detection of Abnormal Asynchrony of Division between Sister Cells in Mutant Caenorhabditis elegans Embryos

Wei Liang, Yuxiao Yang, Yusi Fang, Zhongying ZHAO, Jie Hu

Research output: Contribution to journalJournal articlepeer-review


Cell division timing is critical for cell fate specification and morphogenesis during embryogenesis, but how division timings are regulated among cells during development is poorly understood. In this article, we focus on the comparison of asynchrony of division, that is, difference of lifetime, between sister cells (ADS) among wild-type and mutant individuals of Caenorhabditis elegans. On the one hand, due to extreme imbalance between wild-type individuals and mutant-type samples, direct comparison of two distributions of ADS between wild type and mutant type is not feasible. On the other hand, we originally found that the ADS is correlated with the lifespan of the corresponding mother cell in wild type. Hence, a semiparametric Bayesian quantile regression method where lifetime of the mother cell is taken as covariate is developed to estimate the 95% confidence curve of ADS in wild type and then ADS of mutant type is classified as abnormal if outside the corresponding confidence interval. A high accuracy of our method is demonstrated by a large-scale simulation study. Real data analysis shows that ADS is related to gene function and expression quantitatively.

Original languageEnglish
Pages (from-to)495-505
Number of pages11
JournalJournal of Computational Biology
Issue number5
Publication statusPublished - May 2019

Scopus Subject Areas

  • Modelling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

User-Defined Keywords

  • ADS
  • Bayesian statistics
  • quantile regression
  • semiparametric model


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