@article {KNZ001320,
	title = {Multi-element fingerprinting and high throughput sequencing identify multiple elements that affect fungal communities in Quercus macrocarpa foliage},
	journal = {Plant Signaling and Behaviour},
	volume = {5},
	year = {2010},
	pages = {1157 -1161},
	abstract = {Diverse fungal mutualists, pathogens and saprobes colonize plant leaves. These fungi face a complex environment, in which stochastic dispersal interplays with abiotic and biotic filters. However, identification of the specific factors that drive the community assembly seems unattainable. We mined two broad data sets and identified chemical elements, to which dominant molecular operational taxonomic units (OTUs) in the foliage of a native tree respond most extremely. While many associations could be identified, potential complicating issues emerged. Those were related to unevenly distributed OTU frequency data, a large number of potentially explanatory variables, and the disproportionate effects of outlier observations.},
	keywords = {LTER-KNZ},
	doi = {10.4161/psb.5.9.12681},
	author = {A. Jumpponen and Keating, K. and Gadbury, G.L. and Jones, K.L. and Mattox, J.D.}
}
@inbook {KNZ001256,
	title = {Challenges and approaches to statistical design and inference in high dimensional investigations},
	booktitle = {Plant Systems Biology, Methods in Molecular Biology Series},
	year = {2009},
	pages = {181 -206},
	publisher = {The Humana Press Inc},
	organization = {The Humana Press Inc},
	address = {Totowa, NJ},
	abstract = {<p>Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other \&ldquo;omic\&rdquo; data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative.</p>},
	keywords = {LTER-KNZ, FDR, genomics, high-dimensional, microarray, multiple testing, statistics},
	doi = {10.1007/978-1-60327-563-7_9},
	url = {https://link.springer.com/protocol/10.1007\%2F978-1-60327-563-7_9},
	author = {Gadbury, G.L. and Garrett, K.A. and Allison, D.B.},
	editor = {Belostotsky, D.A.}
}
