Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists,...

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Veröffentlicht in:PloS one 2021-11, Vol.16 (11), p.e0259973-e0259973
Hauptverfasser: Khomich, Maryia, Måge, Ingrid, Rud, Ida, Berget, Ingunn
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Måge, Ingrid
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description The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.
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A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. 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subjects Animals
Aquaculture
Biology and Life Sciences
Comparative analysis
Composition
Computer Simulation
Data analysis
Datasets as Topic
Design
Diet
Discriminant analysis
Fisheries
Food safety
Food science
Gastrointestinal Microbiome
Generalized linear models
Health aspects
High-Throughput Nucleotide Sequencing
Humans
Intestinal microflora
Medicine and Health Sciences
Methods
Microbiological research
Microbiomes
Microbiota (Symbiotic organisms)
Microorganisms
Multivariate Analysis
Next-generation sequencing
Phylogenetics
Physical Sciences
Physiological aspects
Raw materials
Research and Analysis Methods
Simulation
Software
Statistical methods
Statistics
Supervision
Variance analysis
title Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods
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