Associating microbiome composition with environmental covariates using generalized UniFrac distances

By Jun Chen, Kyle Bittinger, Emily S. Charlson, Christian Hoffmann, James D. Lewis, Gary D. Wu, Ronald G. Collman, Frederic D. Bushman, Hongzhe Li. Bioinform., 2012

916 citations recorded by the source corpus.

Motivation: The human microbiome plays an important role in human disease and health. Identification of factors that affect the microbiome composition can provide insights into disease mechanism as well as suggest ways to modulate the microbiome composition for therapeutical purposes. Distance-based statistical tests have been applied to test the association of microbiome composition with environmental or biological covariates. The unweighted and weighted UniFrac distances are the most widely used distance measures. However, these two measures assign too much weight either to rare lineages or to most abundant lineages, which can lead to loss of power when the important composition change occurs in moderately abundant lineages. Results: We develop generalized UniFrac distances that extend the weighted and unweighted UniFrac distances for detecting a much wider range of biologically relevant changes. We evaluate the use of generalized UniFrac distances in associating microbiome composition with environmental covariates using extensive Monte Carlo simulations. Our results show that tests using the unweighted and weighted UniFrac distances are less powerful in detecting abundance change in moderately abundant lineages. In contrast, the generalized UniFrac distance is most powerful in detecting such changes, yet it retains nearly all its power for detecting rare and highly abundant lineages. The generalized UniFrac distance also has an overall better power than the joint use of unweighted/weighted UniFrac distances. Application to two real microbiome datasets has demonstrated gains in power in testing the associations between human microbiome and diet intakes and habitual smoking. Availability: http://cran.r-project.org/web/packages/GUniFrac Contact: hongzhe@upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/6ab0de2168211f3ad6f3af79493e860424c581a1

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Citations in the corpus

microViz: an R package for microbiome data visualization and statistics (2021, background): “The former provides a balanced intermediate between unweighted and weighted UniFrac (Chen et al., 2012), and the latter is a distance measure designed for use with compositional data, such as sequencing read counts (Gloor et al., 2017).”
Intermittent Fasting Promotes White Adipose Browning and Decreases Obesity by Shaping the Gut Microbiota (2017, neutral): “A phylogenic tree and Operation Taxonomic Unit (OTU) table were obtained from the Mothur Bayesian classifier (Kozich et al., 2013) and uploaded to R studio for GUnifrac analysis (Chen et al., 2012).”
An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis (2016, background): “4 for unweighted and weighted UniFrac, respectively), suggesting that the major microbiota difference was in the presence and abundance of rare and less abundant taxa [25].”
A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish (2020, methodology): “To obtain an equal sequencing depth for all primers, the sequence reads of each uniquely tagged PCR were rarefied to 48,000 (the minimum number of reads per tag was 48,308) using the r package gUniFrac (Chen et al., 2012).”
What drives study‐dependent differences in distance–decay relationships of microbial communities? (2021, background): “We suggest, therefore, that weighted phylogenetic metrics might underestimate microbial biogeographical patterns, unless appropriate weight is given to rare and abundant taxa (Chen et al., 2012).”
LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, neutral): “However, for community-wide analysis such as distance-based analysis [38, 39], addressing the compositionality may not be necessary in order to control the type I error.”
Metformin Is Associated With Higher Relative Abundance of Mucin-Degrading Akkermansia muciniphila and Several Short-Chain Fatty Acid–Producing Microbiota in the Gut (2016, neutral): “latedwith theGUniFrac package of R (23).”
A Novel Sparse Compositional Technique Reveals Microbial Perturbations (2019, methodology): “Weighted UniFrac distances were calculated using generalized UniFrac with an alpha of one (52).”
Computational meta'omics for microbial community studies (2013, background): “It remains an area of active research, with current options including categorical nonparametric biomarker discovery (White et al , 2009; Parks and Beiko, 2010; Segata et al , 2011) and appropriately transformed regression models (Chen et al , 2012).”