Introduction

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.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.The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/ CONTACT: sunkim.bioinfo@snu.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.

Publications

  1. TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.
    Cite this
    Jung I, Jo K, Kang H, Ahn H, Yu Y, Kim S, 2017-01-01 - Bioinformatics (Oxford, England)

Credits

  1. Inuk Jung
    Developer

    Interdisciplinary Program in Bioinformatics, Seoul National University

  2. Kyuri Jo
    Developer

  3. Hyejin Kang
    Developer

    Department of Applied Biology and chemistry, Seoul National University

  4. Hongryul Ahn
    Developer

  5. Youngjae Yu
    Developer

  6. Sun Kim
    Investigator

    Bioinformatics Institute, Seoul National University

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT001251
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted BySun Kim