CART variance stabilization and regularization for high-throughput genomic data

Ariadni Papana, Hemant Ishwaran

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Motivation: mRNA expression data obtained from high-throughput DNA microarrays exhibit strong departures from homogeneity of variances. Often a complex relationship between mean expression value and variance is seen. Variance stabilization of such data is crucial for many types of statistical analyses, while regularization of variances (pooling of information) can greatly improve overall accuracy of test statistics. Results: A Classification and Regression Tree (CART) produce is introduced for variance stabilization as well as regularization. The CART procedure adaptively clusters genes by variances. Using both local and cluster wide information leads to improved estimation of population variances which improves test statistics. Whereas making use of cluster wide information allows for variance stabilization of data.

Original languageEnglish (US)
Pages (from-to)2254-2261
Number of pages8
JournalBioinformatics
Volume22
Issue number18
DOIs
StatePublished - Sep 15 2006
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'CART variance stabilization and regularization for high-throughput genomic data'. Together they form a unique fingerprint.

  • Cite this