  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.
