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. 2011 Apr 25:12:118.
doi: 10.1186/1471-2105-12-118.

Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny

Qin Chang  1 , Yihui Luan, Fengzhu Sun
Affiliations

Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny

Qin Chang et al. BMC Bioinformatics. .

Abstract

Background: Beta diversity, which involves the assessment of differences between communities, is an important problem in ecological studies. Many statistical methods have been developed to quantify beta diversity, and among them, UniFrac and weighted-UniFrac (W-UniFrac) are widely used. The W-UniFrac is a weighted sum of branch lengths in a phylogenetic tree of the sequences from the communities. However, W-UniFrac does not consider the variation of the weights under random sampling resulting in less power detecting the differences between communities.

Results: We develop a new statistic termed variance adjusted weighted UniFrac (VAW-UniFrac) to compare two communities based on the phylogenetic relationships of the individuals. The VAW-UniFrac is used to test if the two communities are different. To test the power of VAW-UniFrac, we first ran a series of simulations which revealed that it always outperforms W-UniFrac, as well as UniFrac when the individuals are not uniformly distributed. Next, all three methods were applied to analyze three large 16S rRNA sequence collections, including human skin bacteria, mouse gut microbial communities, microbial communities from hypersaline soil and sediments, and a tropical forest census data. Both simulations and applications to real data show that VAW-UniFrac can satisfactorily measure differences between communities, considering not only the species composition but also abundance information.

Conclusions: VAW-UniFrac can recover biological insights that cannot be revealed by other beta diversity measures, and it provides a novel alternative for comparing communities.

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Figures

Figure 1
Figure 1
Hierarchical clustering of 80 samples where the microbiotas from foreheads were transplanted to forearms. UPGMA results using (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac.
Figure 2
Figure 2
Hierarchical clustering of 72 native microbiota samples from skin microbiota transplant experiments. UPGMA results using (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac.
Figure 3
Figure 3
PCoA plots of 19 microbial communities from mouse guts with three statistics. (a through c) PCoA plots of 19 microbial communities from mouse guts with UniFrac, W-UniFrac and VAW-UniFrac, respectively, where communities are marked with different symbols according to families. (d through f) PCoA plots of 19 microbial communities from mouse guts with UniFrac, W-UniFrac and VAW-UniFrac, respectively, where communities are marked with different symbols according to genotypes. The first two principal coordinate axes in PCoA and percentages of variation that they explain are shown.
Figure 4
Figure 4
Hierarchical clustering of 19 microbial communities from mouse guts with three statistics. UPGMA clustering diagrams of 19 microbial communities from mouse guts with (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac. The three mothers are found in sample M1, M2, and M3, and each offspring is named after its mother.
Figure 5
Figure 5
PCoA plots of 16 sequence collections in Application 3 with three statistics. PCoA plots of the 16 sequence collections with (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac. The collections derived by pyrosequencing and Sanger sequencing from the same sample are represented by the same symbol with red and blue, respectively.
Figure 6
Figure 6
Hierarchical clustering of 16 sequence collections in Application 3 with three statistics. UPGMA clustering diagrams of the 16 sequence collections from 8 samples: T3-0, T3-65, T3-130, T3-195 T3-260, T3-325, T3-390, and T3-455 with (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac. The Sanger sequencing data of T3-0 were named "T3-0", and the pyrosequencing data of T3-0 were named "pyro T3-0". Others were named in a similar manner.
Figure 7
Figure 7
Locations of the 19 1-ha tropical forest plots in Application 4. (a) Cocoli, (b) Sherman, and (c) BCI. The three plots were divided into 19 1-ha plots according to topography and geographic coordinates, so that the different distances between the subdivided plots would help us to test the performance of the statistics.
Figure 8
Figure 8
PCoA results of 19 1-ha tropical forest plots in Application 4 with three statistics. PCoA plots of 19 1-ha tropical forest plots of three sites using (a) UniFrac, (b) W-UniFrac, and (c) VAW-UniFrac. The first two principal coordinate axes in PCoA and percentages of variation that they explain are shown. Plots are denoted by different symbols according to sites.
Figure 9
Figure 9
Hierarchical clustering of 19 1-ha tropical forest plots in Application 4 with three statistics. UPGMA diagrams of the 19 1-ha tropical forest communities with (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac. The notations of the plots are the same as in Figure 7.
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