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Bol Soc Mat Mex. 2013 Oct 1;19(2):255-266.

TWO APPLICATIONS OF PERMUTATION TESTS IN BIOSTASTICS.

Author information

1
Department of Mathematics, University of Louisiana at Lafayette , Lafayette, LA, USA luis@louisina.edu.
2
Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida. Gainesville, FL, USA kkemppainen@ufl.edu.
3
Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida. Gainesville, FL, USA lexi88@ufl.edu.
4
Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida. Gainesville, FL, USA adavisr@ufl.edu.
5
Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida. Gainesville, FL, USA jenn1eruth@ufl.edu.
6
Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida. Gainesville, FL, USA ganokelsey@gmail.com.
7
Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida. Gainesville, FL, USA ewt@ufl.edu.

Abstract

We show two examples of how we answer biological questions by converting them into statistical hypothesis testing problems. We consider gene abundance data, and apply permutation tests. Though these tests are simple, they allow us to test biologically relevant hypotheses. Here we present the analysis of data rising from two studies on Type 1 Diabetes. In the first study [3] are interested in comparing the gut bacterial biodiversity in children at risk and not at risk of developing diabetes. In the second study, [4] compare the gut bacterial biodiversity of children in six different sites in USA and Europe. The statistical analyses presented here are parts of the "statistical methods" in two papers mentioned above. Here we offer a detailed explanation of the "Statistical Methods" addressed to readers with a statistics background.

KEYWORDS:

Shannon diversity index; analysis of covariance; permutation test; type 1 diabetes; unifrac distance

PMID:
25221355
PMCID:
PMC4159102

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