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Fréchet analysis and the microbiome
Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada .
Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada .
Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada .ORCID iD: 0000-0003-2437-1300
2014 (English)In: Journal of Statistical Planning and Inference, ISSN 0378-3758, E-ISSN 1873-1171, Vol. 145, p. 37-41Article in journal (Refereed) Published
Abstract [en]

The paper under discussion provides a detailed survey of the important developments in Fréchet analysis on manifolds or on stratified sample spaces. As it appears that data is now being realized over non-Euclidean spaces, such a paper is timely as such methods are called for in modern data analysis. In this discussion we explore this in the context of computational biology in general, and in particular for microbiome data which is gaining in popularity both in the scholarly and the popular presses. We will discuss the microbiome and metagenomics as well as outline how data is collected and strategies for data analysis. Finally we tie in how the microbiome data can be analyzed within the context of Fréchet analysis.

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 145, p. 37-41
Keywords [en]
16S rRNA, Metagenomics, Microbiology, OTU, Persistent homology, Phylogenetic tree space, Phylotypes, Pyrosequencing, Unifrac metric, Wasserstein metric, Fréchet means
National Category
Probability Theory and Statistics Biological Sciences
Identifiers
URN: urn:nbn:se:oru:diva-65208DOI: 10.1016/j.jspi.2013.08.005ISI: 000327828200005Scopus ID: 2-s2.0-84887607050OAI: oai:DiVA.org:oru-65208DiVA, id: diva2:1185421
Note

Funding Agencies:

NSERC 

CIHR-NSERC 

Available from: 2018-02-24 Created: 2018-02-24 Last updated: 2018-03-13Bibliographically approved

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Rush, Stephen

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