title: Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana.
abstract: BACKGROUND
  Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here.
  
  
  RESULTS
  Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor-target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings.
  
  
  BACKGROUND
  Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.
authors.affiliation: Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Kiddle
authors.forename: Oliver P F
authors.initials: OP
authors.lastname: Windram
authors.forename: Stuart
authors.initials: S
authors.lastname: McHattie
authors.forename: Andrew
authors.initials: A
authors.lastname: Mead
authors.forename: Jim
authors.initials: J
authors.lastname: Beynon
authors.forename: Vicky
authors.initials: V
authors.lastname: Buchanan-Wollaston
authors.forename: Katherine J
authors.initials: KJ
authors.lastname: Denby
authors.forename: Sach
authors.initials: S
authors.lastname: Mukherjee
mesh_terms: D017360:Arabidopsis / Q000235:genetics*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D014157:Transcription Factors / Q000235:genetics*; D014158:Transcription, Genetic
chemical_list: D014157:Transcription Factors