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Impact of Experimental Bias on Compositional Analysis of Microbiome Data
Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530728/ https://www.ncbi.nlm.nih.gov/pubmed/37761917 http://dx.doi.org/10.3390/genes14091777 |
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author | Hu, Yingtian Satten, Glen A. Hu, Yi-Juan |
author_facet | Hu, Yingtian Satten, Glen A. Hu, Yi-Juan |
author_sort | Hu, Yingtian |
collection | PubMed |
description | Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon–taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon–taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here. |
format | Online Article Text |
id | pubmed-10530728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105307282023-09-28 Impact of Experimental Bias on Compositional Analysis of Microbiome Data Hu, Yingtian Satten, Glen A. Hu, Yi-Juan Genes (Basel) Brief Report Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon–taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon–taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here. MDPI 2023-09-08 /pmc/articles/PMC10530728/ /pubmed/37761917 http://dx.doi.org/10.3390/genes14091777 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Brief Report Hu, Yingtian Satten, Glen A. Hu, Yi-Juan Impact of Experimental Bias on Compositional Analysis of Microbiome Data |
title | Impact of Experimental Bias on Compositional Analysis of Microbiome Data |
title_full | Impact of Experimental Bias on Compositional Analysis of Microbiome Data |
title_fullStr | Impact of Experimental Bias on Compositional Analysis of Microbiome Data |
title_full_unstemmed | Impact of Experimental Bias on Compositional Analysis of Microbiome Data |
title_short | Impact of Experimental Bias on Compositional Analysis of Microbiome Data |
title_sort | impact of experimental bias on compositional analysis of microbiome data |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530728/ https://www.ncbi.nlm.nih.gov/pubmed/37761917 http://dx.doi.org/10.3390/genes14091777 |
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