Correlation network analysis reveals relationships between diet-induced changes in human gut microbiota and metabolic health

Kelder, Thomas; Stroeve, Johanna H. M.; Bijlsma, Sabina; Radonjić, Marijana; Roeselers, Guus

doi:10.1038/nutd.2014.18

Cited by 78 publications

(75 citation statements)

References 46 publications

(51 reference statements)

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“…Low effect sizes are typical for microbiome datasets. Finally, an increasing number of studies report correlation networks of microbiome datasets (see for example: [41,42]), but as pointed out by others, the analysis of correlations in these datasets are problematic at best [24,23,37,43,44] and great care needs to be taken in their interpretation. As outlined above, and more completely in Lovell (2015), the essential problem is that whenever compositional data are analyzed the correlation matrix will be different if the OTUs making up the samples changes.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

It's all relative: analyzing microbiome data as compositions

Gloor

Pawlowsky‐Glahn

et al. 2016

Annals of Epidemiology

251

229

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Purpose: The ability to properly analyze and interpret large microbiome datasets has lagged behind our ability to acquire such datasets from environmental or clinical samples. Sequencing instruments impose a structure on these data: the natural sample space of a 16S rRNA gene sequencing dataset is a simplex, which is a part of real space that is restricted to non-negative values with a constant sum. Such data are compositional, and should be analyzed using compositionally appropriate tools and approaches. However, the majority of the tools for 16S rRNA gene sequencing analysis assume these data are unrestricted.Methods: We show that existing tools for compositional data (CoDa) analysis can be readily adapted to analyze high throughput sequencing datasets.Results: The Human Microbiome Project tongue vs. buccal mucosa dataset shows how the CoDa approach can address the major elements of microbiome analysis. Reanalysis of a publicly available autism microbiome dataset shows that the CoDa approach in concert with multiple hypothesis test corrections prevent false positive identifications.Conclusions: The CoDa approach is readily scalable to microbiome-sized analyses. We provide example code, and make recommendations to improve the analysis and reporting of microbiome datasets.

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

It's all relative: analyzing microbiome data as compositions

Gloor

Pawlowsky‐Glahn

et al. 2016

Annals of Epidemiology

251

229

View full text Add to dashboard Cite

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“…This is often used to compare diseased and healthy groups, demonstrating which bacteria/factors coexist and which do not. 57 …”

Section: Interesting Ways Of Representing Data-heat Maps Networkmentioning

confidence: 99%

Analysis and Interpretation of the Human Microbiome

Ashton

Beattie

Ennis

et al. 2016

Inflammatory Bowel Diseases

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Microbiome research has experienced an unprecedented level of growth over the last decade. This is largely due to revolutionary developments in, and accessibility to, DNA sequencing technologies that have enabled laboratories with even modest budgets to undertake projects. Study of the human microbiome in particular has seen a surge in interest, and although a lot of time and money has been focused on health and disease, the clinical interpretation of these data and the ability of clinicians to understand these studies in the context of disease are less straightforward. Conditions such as inflammatory bowel disease, asthma, and cancer have seen a huge increase in research focused on the role of microbiome in disease pathogenesis, but the ability of clinicians to appraise and use these data is largely lacking. The purpose of this article is to provide an introduction for clinicians and nonclinicians wishing to learn about and engage in microbiome research. It details the background of microbiome research and discusses the process of generating 16S rRNA sequencing data, the most commonly used method for microbiome analysis. We discuss the interpretation of results in a clinical context, commonly used metrics for analysis and discuss future impact and direction for microbiome research. The meteoric rise of genomic medicine to the brink of routine clinical use should be seen as a blueprint for the microbiome; the ability for physicians to understand and interpret these data is vital to this growth and aiding clinicians (and researchers) to participate in further microbiome research.(Inflamm Bowel Dis 2016;0:1-10)

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“…Within the gut, the microbiota plays different roles, including the fermentation of amino acids and saccharides, with the production of short-chain fatty acids (SCFAs), succinate, ethanol, H 2 , amines, lactate, phenols, thiols and indoles, disposal of hydrogen (as acetate H 2 S and methane) degradation of undigested proteins and carbohydrates, and the transformation of bile acids 8 . Actually, the production of SCFAs, including butyrate, propionate and acetate, plays an essential role in maintaining a healthy mucosa and the production of anti-inflammatory interleukins 5,11 . In brief, the SCFAs are derived from bacte- Moreover, butyric acid is the preferred source of energy for colonocytes.…”

Section: Microbiotamentioning

confidence: 99%

“…In fact, it controls the proliferation of pathogenic bacteria present in the intestinal tract (such as Clostridia or Colibacillacea) (ref. 5,10 ), stimulates the immune system, regulates the absorption of nutrients 11 , regulates the host metabolism and physiology 12 , intervenes in the production of vitamins and enzymes such as vitamin K and biotin 6 , and in the synthesis of compounds useful for the trophism of colonic mucosa and required for cell renewal 5,10 . Within the gut, the microbiota plays different roles, including the fermentation of amino acids and saccharides, with the production of short-chain fatty acids (SCFAs), succinate, ethanol, H 2 , amines, lactate, phenols, thiols and indoles, disposal of hydrogen (as acetate H 2 S and methane) degradation of undigested proteins and carbohydrates, and the transformation of bile acids 8 .…”

Section: Microbiotamentioning

confidence: 99%