Analysis of time-series gene expression data: Methods, challenges, and opportunities

I. P. Androulakis, E. Yang, R. R. Almon

Research output: Contribution to journalReview articlepeer-review

82 Scopus citations

Abstract

Monitoring the change in expression patterns over time provides the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Gene arrays measuring the level of mRNA expression of thousands of genes simultaneously provide a method of high-throughput data collection necessary for obtaining the scope of data required for understanding the complexities of living organisms. Unraveling the coherent complex structures of transcriptional dynamics is the goal of a large family of computational methods aiming at upgrading the information content of time-course gene expression data. In this review, we summarize the qualitative characteristics of these approaches, discuss the main challenges that this type of complex data present, and, finally, explore the opportunities in the context of developing mechanistic models of cellular response.

Original languageEnglish (US)
Pages (from-to)205-228
Number of pages24
JournalAnnual Review of Biomedical Engineering
Volume9
DOIs
StatePublished - 2007

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Biomedical Engineering

Keywords

  • Bioinformatics
  • Clustering
  • Microarrays
  • Pharmacogenomics
  • Regulation

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