UniFrac: an effective distance metric for microbial community comparison
Open accessAbstract
UniFrac is a β-diversity measure that uses phylogenetic information to compare environmental samples. UniFrac, coupled with standard multivariate statistical techniques including principal coordinates analysis (PCoA), identifies factors explaining differences among microbial communities. A recent simulation study concluded that UniFrac is unsuitable as a distance metric and should not be used for multivariate analysis (Schloss, 2008). We counter this argument by reassessing the data that led to this conclusion and by providing a mathematical proof showing that UniFrac is a distance metric. However, we confirm with actual sequence data that UniFrac values can be influenced by the number of sequences/sample, and recommend sequence jackknifing (that is, determining how often the cluster results are recovered using random subsets of the data) to avoid this issue. UniFrac (Lozupone and Knight, 2005) has been applied in >150 research publications seeking to understand relationships among microbial communities in systems ranging from human disease to general ecology. UniFrac measures the difference between two collections of sequences (for example, 16S rRNA molecules sequenced from different microbial samples) as the amount of evolutionary history that is unique to either of the two, which is measured as the fraction of branch length in a phylogenetic tree that leads to descendents of one sample or the other but not both. There is also a weighted version that directly accounts for differences in relative abundances that can produce different but complementary results (Lozupone et al., 2007). UniFrac can be used to test if the phylogenetic lineages between samples are significantly different, or to cluster many samples using multivariate statistical techniques. UniFrac's widespread application is facilitated by its user-friendly web interface that handles large data sets from next-generation sequencing technologies (Hamady et al., 2010). UniFrac is also implemented in the QIIME (Caporaso et al., 2010) and mothur (Schloss et al., 2009) microbial community sequence analysis pipelines. Sequence-based studies of 10–1000 microbial samples are now common due to advances in sequencing technologies, and increase the value of interpreting UniFrac distances using multivariate statistics, such as PCoA and hierarchical clustering. Multivariate analyses with UniFrac frequently recover biologically meaningful patterns from complex data sets, such as differences in the fecal bacteria of dogs fed with different diets (Middelbos et al., 2010) or between wet and dry soils (Castro et al., 2010). Thus, UniFrac performs well on real data. Why might theoretical simulations disagree? In recent simulations, it was concluded that UniFrac could be used to determine whether communities were significantly different, but should not be used as a distance metric for multivariate statistical analyses. In these simulations, communities with ‘known’ diversity and overlap were estimated by drawing ellipses around random points in two-dimensional space, where larger ellipses represent more diverse communities and overlap between ellipses represents the shared community membership (Schloss, 2008). We argue that even these unrealistic two-dimensional simulations demonstrate UniFrac's effectiveness. The recommendation against using UniFrac as a distance measure is based on two assertions: (1) it does not exhibit a consistent linear correlation with the fraction of overlap between communities and (2) its values are sensitive to sampling. The first assertion is unsupported by the simulations, where community overlap correlates well with unweighted (r=0.97) and weighted (r=0.90) UniFrac measures (Figure 1; Schloss, 2008). The second assertion is based on resampling the data with 50–1000 individuals/community under three community overlap conditions: 0%, 80% and 100%. As the sequences/sample increased, mean UniFrac values stayed constant and standard deviations decreased. In contrast, mean-weighted UniFrac values decreased with sampling, particularly in the communities with 0% overlap (Schloss, 2008). Rarefaction of data from a study of obese twins (Turnbaugh et al., 2009). This study produced >1 million reads from the V2 region of ribosomal RNA using pyrosequencing. The samples with less than 3000 sequences were first excluded (leaving 112 samples). For five replicate trials, sequences from all 112 samples were subsampled so that each sample had a set number of sequences (between 50 and 2925 with a step size of 125). Pairwise UniFrac values were calculated with both the unweighted (a) and weighted (b) versions for all pairs of samples. To assess the effects of community divergence (the raw UniFrac value) on the sensitivity to sampling, the most similar and most different pairs of samples were identified from the most heavily subsampled data set (2925 sequences per sample) as those in the upper and lower quartile of UniFrac values respectively (calculated s
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- Authors
- Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J., & Knight, R.
- Affiliations
- University of Colorado Boulder (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Knight, Rob
- Venue
- The ISME Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-09-09
- Volume
- 5
- Issue
- 2
- Pages
- 169-172
- ISSN
- 1751-7362 (ISSN-L)
- 1751-7370
- PMC
- PMC3105689
- OpenAlex
- W2055615060
- Cited by
- 2,825
- References
- 13
- Citations by year
- 2026: 157 · 2025: 203 · 2024: 266 · 2023: 254 · 2022: 291 · 2021: 350 · 2020: 292 · 2019: 242 · 2018: 188 · 2017: 158 · 2016: 132 · 2015: 101 · 2014: 77 · 2013: 57 · 2012: 39
- FWCI
- 31.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The ISME Journal (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health
- University of Colorado Denver
- U.S. National Library of Medicine (T15LM009451)
- Topics
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- UniFrac, beta diversity, distance measures, phylogenetic diversity, microbial community comparison, 16S rRNA, sample size effects, principal coordinates analysis, multivariate analysis, QIIME, mothur, soil microbial communities, human gut microbiome, fecal bacteria
- MeSH terms
- Animals, Bacteria, Data Interpretation, Statistical, Ecology, Humans, Phylogeny, Software, Ecosystem
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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