Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process.
BACKGROUND
Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning these networks is challenging due to the low sample size and high dimensionality of genomic data.


RESULTS
We present a novel and highly efficient approach to estimate a VAR network. This proceeds in two steps: (i) improved estimation of VAR regression coefficients using an analytic shrinkage approach, and (ii) subsequent model selection by testing the associated partial correlations. In simulations this approach outperformed for small sample size all other considered approaches in terms of true discovery rate (number of correctly identified edges relative to the significant edges). Moreover, the analysis of expression time series data from Arabidopsis thaliana resulted in a biologically sensible network.


CONCLUSIONS
Statistical learning of large-scale VAR causal models can be done efficiently by the proposed procedure, even in the difficult data situations prevalent in genomics and proteomics.


BACKGROUND
The method is implemented in R code that is available from the authors on request.
Opgen-Rhein
Rainer
R
Department of Statistics, Ludwig-Maximilians-Universität München, München, Germany. opgen-rhein@stat.uni-muenchen.de
Strimmer
Korbinian
K
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000378:metabolism*; D012044:Regression Analysis; D015398:Signal Transduction / Q000502:physiology*; D049490:Systems Biology / Q000379:methods; D013997:Time Factors
D020543:Proteome