title: TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.
abstract: BACKGROUND
  Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions.
  
  
  RESULTS
  We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully.
  
  
  METHODS
  The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/.
  
  
  BACKGROUND
  sunkim.bioinfo@snu.ac.kr.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea.
authors.forename: Inuk
authors.initials: I
authors.lastname: Jung
authors.affiliation: Department of Computer Science and Engineering.
authors.forename: Kyuri
authors.initials: K
authors.lastname: Jo
authors.affiliation: Department of Applied Biology and chemistry, Seoul National University, Gwanak-Gu, Seoul, 151-744, Republic of Korea.
authors.forename: Hyejin
authors.initials: H
authors.lastname: Kang
authors.affiliation: Department of Computer Science and Engineering.
authors.forename: Hongryul
authors.initials: H
authors.lastname: Ahn
authors.affiliation: Department of Computer Science and Engineering.
authors.forename: Youngjae
authors.initials: Y
authors.lastname: Yu
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea.|Department of Computer Science and Engineering.|Bioinformatics Institute, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea.
authors.forename: Sun
authors.initials: S
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors; D059467:Transcriptome