Determining transcription factor activity from microarray data using bayesian markov chain monte carlo sampling

Andrew V. Kossenkov, Aidan J. Peterson, Michael F. Ochs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Many biological processes rely on remodeling of the transcriptional response of cells through activation of transcription factors. Although determination of the activity level of transcription factors from microarray data can provide insight into developmental and disease processes, it requires careful analysis because of the multiple regulation of genes. We present a novel approach that handles both the assignment of genes to multiple patterns, as required by multiple regulation, and the linking of genes in prior probability distributions according to their known transcriptional regulators. We demonstrate the power of this approach in simulations and by application to yeast cell cycle and deletion mutant data. The results of simulations in the presence of increasing noise showed improved recovery of patterns in terms of χ2 fit. Analysis of the yeast data led to improved inference of biologically meaningful groups in comparison to other techniques, as demonstrated with ROC analysis. The new algorithm provides an approach for estimating the levels of transcription factor activity from microarray data, and therefore provides insights into biological response.

Original languageEnglish (US)
Title of host publicationStudies in Health Technology and Informatics
Subtitle of host publicationBuilding Sustainable Health Systems
PublisherIOS Press
Pages1250-1254
Number of pages5
Volume129
EditionPt 2
ISBN (Print)9781586037741
StatePublished - 2007
Event12th World Congress on Medical Informatics, MEDINFO 2007 - Brisbane, QLD, Australia
Duration: Aug 20 2007Aug 24 2007

Publication series

NameStudies in Health Technology and Informatics
Volume129
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other12th World Congress on Medical Informatics, MEDINFO 2007
CountryAustralia
CityBrisbane, QLD
Period8/20/078/24/07

Keywords

  • Bayesian analysis
  • Microarray analysis
  • transcription factors

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    Kossenkov, A. V., Peterson, A. J., & Ochs, M. F. (2007). Determining transcription factor activity from microarray data using bayesian markov chain monte carlo sampling. In Studies in Health Technology and Informatics: Building Sustainable Health Systems (Pt 2 ed., Vol. 129, pp. 1250-1254). (Studies in Health Technology and Informatics; Vol. 129). IOS Press.