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Variable Selection in High‐Dimensional Multivariate Binary Data with Application to the Analysis of Microbial Community DNA Fingerprints
Author(s) -
Wilbur J. D.,
Ghosh J. K.,
Nakatsu C. H.,
Brouder S. M.,
Doerge R. W.
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00378.x
Subject(s) - multivariate statistics , context (archaeology) , selection (genetic algorithm) , microbial population biology , identification (biology) , dna profiling , binary number , feature selection , biology , computational biology , computer science , biological system , mathematics , dna , genetics , artificial intelligence , statistics , ecology , bacteria , paleontology , arithmetic
Summary. In order to understand the relevance of microbial communities on crop productivity, the identification and characterization of the rhieosphere soil microbial community is necessary. Characteristic profiles of the microbial communities are obtained by denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR) amplified 16s rDNA from soil extracted DNA. These characteristic profiles, commonly called community DNA fingerprints, can be represented in the form of high‐dimensional binary vectors. We address the problem of modeling and variable selection in high‐dimensional multivariate binary data and present an application of our methodology in the context of a controlled agricultural experiment.