Classification of genes and putative biomarker identification using distribution metrics on expression profiles

Hung Chung Huang, Daniel Jupiter, Vincent VanBuren

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Background: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. Methodology/Principal Findings:In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. Conclusions/Significance: The methodology employed here may be used to facilitate disease-specific biomarker discovery.

Original languageEnglish (US)
Article numbere9056
JournalPloS one
Volume5
Issue number2
DOIs
StatePublished - Feb 4 2010

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of 'Classification of genes and putative biomarker identification using distribution metrics on expression profiles'. Together they form a unique fingerprint.

  • Cite this