Temporal clustering of gene expression patterns using short-time segments

Nguyen Nguyen, Ying Ann Chiao, Yufei Huang, Shou Jiang Gao, Merry L Lindsey, Yidong Chen, Yufang Jin

Research output: Contribution to journalArticle

Abstract

Temporal clustering of time series data is a powerful tool to delaminate the dynamics of transcription and interactions among genes on a large scale. Different algorithms have been proposed to organise experimental data with meaningful biological clusters; however, these approaches often fail to generate well-defined temporal clusters, especially when genes exert their functions or response to stimuli coordinately only in a short period of time span. In this study, we proposed an algorithm using sliding windows to identify different temporal patterns based on fold changes of gene expressions. The algorithm was applied to simulated data and real experimental data. Furthermore, a comparison study has been carried out with the clusters obtained from commercial software packages. The identified clusters using our algorithm demonstrated better temporal matching and consistency.

Original languageEnglish (US)
Pages (from-to)32-46
Number of pages15
JournalInternational Journal of Functional Informatics and Personalised Medicine
Volume4
Issue number1
DOIs
StatePublished - Dec 1 2012

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Cluster Analysis
Gene Expression
Genes
Software

Keywords

  • Gene expression patterns
  • Short-time segments
  • Sliding window
  • Temporal clustering

ASJC Scopus subject areas

  • Clinical Neurology

Cite this

Temporal clustering of gene expression patterns using short-time segments. / Nguyen, Nguyen; Chiao, Ying Ann; Huang, Yufei; Gao, Shou Jiang; Lindsey, Merry L; Chen, Yidong; Jin, Yufang.

In: International Journal of Functional Informatics and Personalised Medicine, Vol. 4, No. 1, 01.12.2012, p. 32-46.

Research output: Contribution to journalArticle

Nguyen, Nguyen ; Chiao, Ying Ann ; Huang, Yufei ; Gao, Shou Jiang ; Lindsey, Merry L ; Chen, Yidong ; Jin, Yufang. / Temporal clustering of gene expression patterns using short-time segments. In: International Journal of Functional Informatics and Personalised Medicine. 2012 ; Vol. 4, No. 1. pp. 32-46.
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