The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 9, Number 2 (2015), 950-968.
Bayesian detection of embryonic gene expression onset in C. elegans
To study how a zygote develops into an embryo with different tissues, large-scale 4D confocal movies of C. elegans embryos have been produced recently by experimental biologists. However, the lack of principled statistical methods for the highly noisy data has hindered the comprehensive analysis of these data sets. We introduced a probabilistic change point model on the cell lineage tree to estimate the embryonic gene expression onset time. A Bayesian approach is used to fit the 4D confocal movies data to the model. Subsequent classification methods are used to decide a model selection threshold and further refine the expression onset time from the branch level to the specific cell time level. Extensive simulations have shown the high accuracy of our method. Its application on real data yields both previously known results and new findings.
Ann. Appl. Stat., Volume 9, Number 2 (2015), 950-968.
Received: September 2014
Revised: January 2015
First available in Project Euclid: 20 July 2015
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Hu, Jie; Zhao, Zhongying; Yalamanchili, Hari Krishna; Wang, Junwen; Ye, Kenny; Fan, Xiaodan. Bayesian detection of embryonic gene expression onset in C. elegans. Ann. Appl. Stat. 9 (2015), no. 2, 950--968. doi:10.1214/15-AOAS820. https://projecteuclid.org/euclid.aoas/1437397119
- Supplement A: Model checking. We provide the justification of our 3 model assumptions in Section 2.2.
- Supplement B: Hyperparameters of prior distributions. The settings and the sensitivity analysis of hyperparameters are shown in detail.
- Supplement C: Classification and stopping criterion based on SVR. We provide plots and tables to demonstrate the good performance of the SVR method in classifying expression and nonexpression branches.
- Supplement D: Convergence diagnosis and parameter estimation. Proofs of successful convergence and good parameter estimation are provided in additional figures and table.
- Supplement E: Detection of size-biased sampling. We supply some details in detection of the size-biased sampling problem.
- Supplement F: Detection results of real data files. All SVR reported expression branches, all exact expression onset time points and all expression segments in real data files are listed in a folder.