Bayesian detection of embryonic gene expression onset in C. elegans

TL;DR

This paper presents a Bayesian change point detection method for analyzing noisy 4D confocal microscopy data to accurately determine the onset of gene expression during C. elegans embryonic development.

Contribution

It introduces a probabilistic change point model on cell lineage trees combined with Bayesian inference, enabling precise estimation of gene expression onset times from noisy imaging data.

Findings

High accuracy demonstrated in simulations

Successfully identified known and new gene expression onset times in real data

Provides a principled statistical framework for noisy biological data analysis

Abstract

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.