Cargando…
Normalization and microbial differential abundance strategies depend upon data characteristics
BACKGROUND: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative a...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5335496/ https://www.ncbi.nlm.nih.gov/pubmed/28253908 http://dx.doi.org/10.1186/s40168-017-0237-y |
_version_ | 1782512057351929856 |
---|---|
author | Weiss, Sophie Xu, Zhenjiang Zech Peddada, Shyamal Amir, Amnon Bittinger, Kyle Gonzalez, Antonio Lozupone, Catherine Zaneveld, Jesse R. Vázquez-Baeza, Yoshiki Birmingham, Amanda Hyde, Embriette R. Knight, Rob |
author_facet | Weiss, Sophie Xu, Zhenjiang Zech Peddada, Shyamal Amir, Amnon Bittinger, Kyle Gonzalez, Antonio Lozupone, Catherine Zaneveld, Jesse R. Vázquez-Baeza, Yoshiki Birmingham, Amanda Hyde, Embriette R. Knight, Rob |
author_sort | Weiss, Sophie |
collection | PubMed |
description | BACKGROUND: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. RESULTS: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. CONCLUSIONS: These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-017-0237-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5335496 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53354962017-03-06 Normalization and microbial differential abundance strategies depend upon data characteristics Weiss, Sophie Xu, Zhenjiang Zech Peddada, Shyamal Amir, Amnon Bittinger, Kyle Gonzalez, Antonio Lozupone, Catherine Zaneveld, Jesse R. Vázquez-Baeza, Yoshiki Birmingham, Amanda Hyde, Embriette R. Knight, Rob Microbiome Research BACKGROUND: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. RESULTS: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. CONCLUSIONS: These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-017-0237-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-03 /pmc/articles/PMC5335496/ /pubmed/28253908 http://dx.doi.org/10.1186/s40168-017-0237-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Weiss, Sophie Xu, Zhenjiang Zech Peddada, Shyamal Amir, Amnon Bittinger, Kyle Gonzalez, Antonio Lozupone, Catherine Zaneveld, Jesse R. Vázquez-Baeza, Yoshiki Birmingham, Amanda Hyde, Embriette R. Knight, Rob Normalization and microbial differential abundance strategies depend upon data characteristics |
title | Normalization and microbial differential abundance strategies depend upon data characteristics |
title_full | Normalization and microbial differential abundance strategies depend upon data characteristics |
title_fullStr | Normalization and microbial differential abundance strategies depend upon data characteristics |
title_full_unstemmed | Normalization and microbial differential abundance strategies depend upon data characteristics |
title_short | Normalization and microbial differential abundance strategies depend upon data characteristics |
title_sort | normalization and microbial differential abundance strategies depend upon data characteristics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5335496/ https://www.ncbi.nlm.nih.gov/pubmed/28253908 http://dx.doi.org/10.1186/s40168-017-0237-y |
work_keys_str_mv | AT weisssophie normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT xuzhenjiangzech normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT peddadashyamal normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT amiramnon normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT bittingerkyle normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT gonzalezantonio normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT lozuponecatherine normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT zaneveldjesser normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT vazquezbaezayoshiki normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT birminghamamanda normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT hydeembrietter normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics AT knightrob normalizationandmicrobialdifferentialabundancestrategiesdependupondatacharacteristics |