Visualization and statistical comparisons of microbial communities using R packages on phylochip data

Susan Holmes, Alexander Alekseyenko, Alden Timme, Tyrrell Nelson, Pankaj Jay Pasricha, Alfred Spormann

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

Abstract

This article explains the statistical and computational methodology used to analyze species abundances collected using the LNBL Phylochip in a study of Irritable Bowel Syndrome (IBS) in rats. Some tools already available for the analysis of ordinary microarray data are useful in this type of statistical analysis. For instance in correcting for multiple testing we use Family Wise Error rate control and step-down tests (available in the multtest package). Once the most significant species are chosen we use the hypergeometric tests familiar for testing GO categories to test specific phyla and families. We provide examples of normalization, multivariate projections, batch effect detection and integration of phylogenetic covariation, as well as tree equalization and robustification methods.

Original languageEnglish (US)
Title of host publicationPacific Symposium on Biocomputing 2011, PSB 2011
Pages142-153
Number of pages12
StatePublished - Dec 1 2011
Externally publishedYes
Event16th Pacific Symposium on Biocomputing, PSB 2011 - Kohala Coast, HI, United States
Duration: Jan 3 2011Jan 7 2011

Publication series

NamePacific Symposium on Biocomputing 2011, PSB 2011

Other

Other16th Pacific Symposium on Biocomputing, PSB 2011
CountryUnited States
CityKohala Coast, HI
Period1/3/111/7/11

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Keywords

  • Hypergeometric Test
  • PhyloChip
  • Phylogenetic Tree
  • Quality Control
  • R
  • projections

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Biomedical Engineering
  • Medicine(all)

Cite this

Holmes, S., Alekseyenko, A., Timme, A., Nelson, T., Pasricha, P. J., & Spormann, A. (2011). Visualization and statistical comparisons of microbial communities using R packages on phylochip data. In Pacific Symposium on Biocomputing 2011, PSB 2011 (pp. 142-153). (Pacific Symposium on Biocomputing 2011, PSB 2011).