Abstract
Often protein (or gene) time-course data are collected for multiple replicates. Each replicate generally has sparse data with the number of time points being less than the number of proteins. Usually each replicate is modeled separately. However, here all the information in each of the replicates is used to make a composite inference about signal networks. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the networks. In particular, when the edge's partial correlation between two proteins is at least moderate, then the composite's posterior probability is large.
Highlights
Often the laboratory collection of protein phosphorylation time-course data results not in a single set of timecourse data, but in multiple sets of time-course data
Edge probabilities are computed for an undirected graph where the nodes represent individual proteins and where an edge between two nodes represents a relationship between the two corresponding proteins. These edge probabilities are based on an algorithmic search through the space of all models (DAGs) guided by the posterior probability of the directed acyclic graph (DAG)
This DAG posterior probability takes into account all of the data sets
Summary
Often the laboratory collection of protein phosphorylation time-course data results not in a single set of timecourse data, but in multiple sets of time-course data. The data are sparse: the number t of time points is significantly less than the number k of proteins. Even though there are differences between these data sets, the underlying biochemical interactions (signal) are reflected in each of these data sets. Many times these individual sets of time course protein data are modeled individually. The discussion in this paper focuses on protein measurements. It applies to sparse time course measurements obtained through gene microarrays
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