Characterizing genes by marginal expression distribution

Edward Wijaya, Hajime Harada, Paul Horton

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

Abstract

We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal distribution of the gene. The base distributions used were normal, lognormal and gamma. The expectation-maximization algorithm was used to learn the model parameters. Experiments with articifial data were performed to ascertain the robustness of learning. Applying the procedure to data from two publicly available microarray datasets, we conclude that lognormal performed the best function for modeling the marginal distributions of gene expression. Our results should provide guidances in the development of informed priors or gene specific normalization for use with gene network inference algorithms.

Original languageEnglish
Title of host publicationAdvances in Computational Science and Engineering
Subtitle of host publicationSecond International Conference, FGCN 2008, Workshops and Symposia, Sanya, Hainan Island, China, December 13-15, 2008. Revised Selected Papers
EditorsTai-hoon Kim, Laurence T. Yang, Jong Hyuk Park, Alan Chin-Chen Chang, Thanos Vasilakos, Yan Zhang, Damien Sauveron, Xingang Wang, Young-Sik Jeong
Pages164-175
Number of pages12
DOIs
Publication statusPublished - 2009 Dec 1

Publication series

NameCommunications in Computer and Information Science
Volume28
ISSN (Print)1865-0929

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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