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Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection.

Authors
  • Wang, Zixing
  • San Lucas, F Anthony
  • Qiu, Peng
  • Liu, Yin1
  • 1 Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Houston, Texas, USA. [email protected]
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 20, 2014
Volume
15
Pages
153–153
Identifiers
DOI: 10.1186/1471-2105-15-153
PMID: 24885641
Source
Medline
License
Unknown

Abstract

By leveraging the structure of gene co-expression network, first we propose a variable selection method that selects individual genes with top connectivity. Both simulation studies and real data application have demonstrated that our method has better performance in terms of the reliability of the selected genes and sample clustering results. In addition, we propose a module recovery method that can help discover novel sample partitions that might be hidden when performing clustering analyses using all available genes. The source code of our program is available at http://nba.uth.tmc.edu/homepage/liu/netVar/.

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