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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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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. |
format | Online Article Text |
id | pubmed-4492014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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