Add new papers from 2024

papers/_posts/2024-09-24-boverhoff-profiling-the-fecal.md

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+---
+layout: paper
+title: "Profiling the fecal microbiome and its modulators across the lifespan in the Netherlands"
+nickname: 2024-09-24-boverhoff-profiling-the-fecal
+authors: "Boverhoff D, Kool J, Pijnacker R, Ducarmon QR, Zeller G, Shetty S, Sie S, Mulder AC, van der Klis F, Franz E, Mughini-Gras L, van Baarle D, Fuentes S"
+year: "2024"
+journal: "Cell Rep"
+volume: 43
+issue: 9
+pages: 114729
+is_published: true
+image: /assets/images/papers/cell-rep.png
+projects:
+tags: []
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid: 
+preprint:
+supplement:
+
+# Links
+doi: "10.1016/j.celrep.2024.114729"
+pmid: 39264809
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+---
+{% include JB/setup %}
+
+# Abstract
+
+Defining what constitutes a healthy microbiome throughout our lives remains an ongoing challenge. Understanding to what extent host and environmental factors can influence it has been the primary motivation for large population studies worldwide. Here, we describe the fecal microbiome of 3,746 individuals (0-87 years of age) in a nationwide study in the Netherlands, in association with extensive questionnaires. We validate previous findings, such as infant-adult trajectories, and explore the collective impact of our variables, which explain over 40% of the variation in microbiome composition. We identify associations with less explored factors, particularly those ethnic related, which show the largest impact on the adult microbiome composition, diversity, metabolic profiles, and CAZy (carbohydrate-active enzyme) repertoires. Understanding the sources of microbiome variability is crucial, given its potential as a modifiable target with therapeutic possibilities. With this work, we aim to serve as a foundational element for the design of health interventions and fundamental research.

papers/_posts/2024-09-25-wirbel-a-realistic-benchmark.md

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+---
+layout: paper
+title: "A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies"
+nickname: 2024-09-25-wirbel-a-realistic-benchmark
+authors: "Wirbel J, Essex M, Forslund SK, Zeller G"
+year: "2024"
+journal: "Genome Biol"
+volume: 25
+issue: 1
+pages: 247
+is_published: true
+image: /assets/images/papers/genome-biol.png
+projects: [stats]
+tags: []
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid: PMC11423519
+preprint: https://doi.org/10.1101/2022.05.09.491139
+supplement:
+
+# Links
+doi: "10.1186/s13059-024-03390-9"
+pmid: 39322959
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+---
+{% include JB/setup %}
+
+# Abstract
+
+BACKGROUND: In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations. RESULTS: Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications. CONCLUSIONS: Tight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.