A stationary wavelet entropy-based clustering approach accurately predicts gene expression

Nha Nguyen, An Vo, Inchan Choi, Kyoung Jae Won

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation.

Original languageEnglish (US)
Pages (from-to)236-249
Number of pages14
JournalJournal of Computational Biology
Volume22
Issue number3
DOIs
StatePublished - Mar 1 2015
Externally publishedYes

Keywords

  • algorithms
  • gene expression
  • genetic analysis
  • genome analysis
  • next-generation sequencing

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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