title: Discovering graphical Granger causality using the truncating lasso penalty.
abstract: BACKGROUND
  Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an important problem in systems biology. Whole-genome expression data over time provides an opportunity to determine how the expression levels of genes are affected by changes in transcription levels of other genes, and can therefore be used to discover regulatory interactions among genes.
  
  
  RESULTS
  In this article, we propose a novel penalization method, called truncating lasso, for estimation of causal relationships from time-course gene expression data. The proposed penalty can correctly determine the order of the underlying time series, and improves the performance of the lasso-type estimators. Moreover, the resulting estimate provides information on the time lag between activation of transcription factors and their effects on regulated genes. We provide an efficient algorithm for estimation of model parameters, and show that the proposed method can consistently discover causal relationships in the large p, small n setting. The performance of the proposed model is evaluated favorably in simulated, as well as real, data examples.
  
  
  BACKGROUND
  The proposed truncating lasso method is implemented in the R-package 'grangerTlasso' and is freely available at http://www.stat.lsa.umich.edu/~shojaie/.
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA. shojaie@umich.edu
authors.forename: Ali
authors.initials: A
authors.lastname: Shojaie
authors.forename: George
authors.initials: G
authors.lastname: Michailidis
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006367:HeLa Cells; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D012044:Regression Analysis
references.citation: Stat Appl Genet Mol Biol. 2010;9:Article22
references.pmid: 20597848
references.citation: J Stat Softw. 2010;33(1):1-22
references.pmid: 20808728
references.citation: BMC Syst Biol. 2007 Aug 30;1:39
references.pmid: 17761000
references.citation: Biometrika. 2010 Sep;97(3):519-538
references.pmid: 22434937
references.citation: Bioinformatics. 2003 Oct;19 Suppl 2:ii138-48
references.pmid: 14534183
references.citation: Bioinformatics. 2007 Feb 15;23(4):442-9
references.pmid: 17158516
references.citation: Proc Natl Acad Sci U S A. 2004 Jan 13;101(2):641-6
references.pmid: 14694202
references.citation: Bioinformatics. 2009 Jun 15;25(12):i110-8
references.pmid: 19477976
references.citation: BMC Bioinformatics. 2009 Apr 24;10:122
references.pmid: 19393071
references.citation: BMC Bioinformatics. 2007 May 03;8 Suppl 2:S3
references.pmid: 17493252
references.citation: Bioinformatics. 2002;18 Suppl 1:S241-8
references.pmid: 12169553
references.citation: J Comput Biol. 2009 Mar;16(3):407-26
references.pmid: 19254181
references.citation: Mol Biol Cell. 2002 Jun;13(6):1977-2000
references.pmid: 12058064