A strategy for differential abundance analysis of sparse microbiome data with group-wise structured zeros.

Autor: Abegaz F; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands. f.a.yazew@uva.nl.; Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands. f.a.yazew@uva.nl., Abedini D; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands., White F; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands., Guerrieri A; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands., Zancarini A; IGEPP, INRAE, Institut Agro, Univ Rennes, 35653, Le Rheu, France., Dong L; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands., Westerhuis JA; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands., van Eeuwijk F; Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands., Bouwmeester H; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands., Smilde AK; Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 30; Vol. 14 (1), pp. 12433. Date of Electronic Publication: 2024 May 30.
DOI: 10.1038/s41598-024-62437-w
Abstrakt: Comparing the abundance of microbial communities between different groups or obtained under different experimental conditions using count sequence data is a challenging task due to various issues such as inflated zero counts, overdispersion, and non-normality. Several methods and procedures based on counts, their transformation and compositionality have been proposed in the literature to detect differentially abundant species in datasets containing hundreds to thousands of microbial species. Despite efforts to address the large numbers of zeros present in microbiome datasets, even after careful data preprocessing, the performance of existing methods is impaired by the presence of inflated zero counts and group-wise structured zeros (i.e. all zero counts in a group). We propose and validate using extensive simulations an approach combining two differential abundance testing methods, namely DESeq2-ZINBWaVE and DESeq2, to address the issues of zero-inflation and group-wise structured zeros, respectively. This combined approach was subsequently successfully applied to two plant microbiome datasets that revealed a number of taxa as interesting candidates for further experimental validation.
(© 2024. The Author(s).)
Databáze: MEDLINE
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