Abstract
BackgroundIn computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.ResultsIn this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better.ConclusionWhen the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better.
Highlights
In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data
To illustrate and compare the differences between the dynamic Bayesian network inference and the conditional Granger causality, a simple multivariate model with fixed coefficients, which has been discussed in many papers to test the Granger causality, is tested first
A fair comparison In our results presented here, one of the key issues which is the cause of the critical point of the sampling size between the dynamic Bayesian approach and the Granger causality lies in the fact that a batch fitting approach is used in the Granger causality approach
Summary
One often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. Based upon high throughput data, to reliably and accurately explore the network structure of elements (genes, proteins, metabolites, neurons etc.) is one of the most important issues in computational biology [1,2,3,4,5,6]. One is the Granger causality approach [10,11], and the other is the Bayesian network inference approach [12,13] The latter is often applied to static data. The Granger causality has the advantage of having a corresponding frequency domain decomposition so that one can clearly find at which frequencies two elements interact with each other
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
