Visualization and statistical comparisons of microbial communities using R packages on Phylochip data.
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.
Statistics Department, Stanford University, Stanford, CA 94305, USA. susan@stat.stanford.edu
Susan
S
Holmes
Alexander
A
Alekseyenko
Alden
A
Timme
Tyrrell
T
Nelson
Pankaj Jay
PJ
Pasricha
Alfred
A
Spormann
D000818:Animals; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D006801:Humans; D043183:Irritable Bowel Syndrome / Q000382:microbiology; D054892:Metagenome / Q000235:genetics; D056186:Metagenomics / Q000706:statistics & numerical data*; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010802:Phylogeny; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D051381:Rats; D012984:Software
D012336:RNA, Ribosomal, 16S