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The unifrac significance test is sensitive to tree topology

Long et al. (BMC Bioinformatics 2014, 15(1):278) describe a “discrepancy” in using UniFrac to assess statistical significance of community differences. Specifically, they find that weighted UniFrac results differ between input trees where (a) replicate sequences each have their own tip, or (b) all r...

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Detalles Bibliográficos
Autores principales: Lozupone, Catherine A., Knight, Rob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492014/
https://www.ncbi.nlm.nih.gov/pubmed/26150095
http://dx.doi.org/10.1186/s12859-015-0640-y
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author Lozupone, Catherine A.
Knight, Rob
author_facet Lozupone, Catherine A.
Knight, Rob
author_sort Lozupone, Catherine A.
collection PubMed
description Long et al. (BMC Bioinformatics 2014, 15(1):278) describe a “discrepancy” in using UniFrac to assess statistical significance of community differences. Specifically, they find that weighted UniFrac results differ between input trees where (a) replicate sequences each have their own tip, or (b) all replicates are assigned to one tip with an associated count. We argue that these are two distinct cases that differ in the probability distribution on which the statistical test is based, because of the differences in tree topology. Further study is needed to understand which randomization procedure best detects different aspects of community dissimilarities.
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spelling pubmed-44920142015-07-07 The unifrac significance test is sensitive to tree topology Lozupone, Catherine A. Knight, Rob BMC Bioinformatics Research Long et al. (BMC Bioinformatics 2014, 15(1):278) describe a “discrepancy” in using UniFrac to assess statistical significance of community differences. Specifically, they find that weighted UniFrac results differ between input trees where (a) replicate sequences each have their own tip, or (b) all replicates are assigned to one tip with an associated count. We argue that these are two distinct cases that differ in the probability distribution on which the statistical test is based, because of the differences in tree topology. Further study is needed to understand which randomization procedure best detects different aspects of community dissimilarities. BioMed Central 2015-07-07 /pmc/articles/PMC4492014/ /pubmed/26150095 http://dx.doi.org/10.1186/s12859-015-0640-y Text en © Lozupone and Knight. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Lozupone, Catherine A.
Knight, Rob
The unifrac significance test is sensitive to tree topology
title The unifrac significance test is sensitive to tree topology
title_full The unifrac significance test is sensitive to tree topology
title_fullStr The unifrac significance test is sensitive to tree topology
title_full_unstemmed The unifrac significance test is sensitive to tree topology
title_short The unifrac significance test is sensitive to tree topology
title_sort unifrac significance test is sensitive to tree topology
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492014/
https://www.ncbi.nlm.nih.gov/pubmed/26150095
http://dx.doi.org/10.1186/s12859-015-0640-y
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