Profiling time course expression of virus genes---an illustration of Bayesian inference under shape restrictions

TL;DR

This paper presents a hierarchical Bayesian shape restricted regression method for analyzing virus gene expression over time, providing precise estimates of key features and enabling quantitative biological insights from microarray data.

Contribution

It introduces a novel Bayesian inference approach that accurately estimates gene expression features under shape restrictions, improving analysis of temporal viral gene data.

Findings

Effective estimation of gene expression features like onset and maximum time.

Application to baculovirus data yields new biological insights.

Method improves over crude statistical approaches.

Abstract

There have been several studies of the genome-wide temporal transcriptional program of viruses, based on microarray experiments, which are generally useful in the construction of gene regulation network. It seems that biological interpretations in these studies are directly based on the normalized data and some crude statistics, which provide rough estimates of limited features of the profile and may incur biases. This paper introduces a hierarchical Bayesian shape restricted regression method for making inference on the time course expression of virus genes. Estimates of many salient features of the expression profile like onset time, inflection point, maximum value, time to maximum value, area under curve, etc. can be obtained immediately by this method. Applying this method to a baculovirus microarray time course expression data set, we indicate that many biological questions can be formulated quantitatively and we are able to offer insights into the baculovirus biology.