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

Chang Q, Luan Y, Sun F - BMC Bioinformatics (2011)

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

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Mathematics, Shandong University, Jinan, Shandong, PR China.

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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Related in: MedlinePlus

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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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.

Mentions: We divided these plots into 19 1-ha plots and compared their compositions using the three statistics. In order to obtain overall insight into the distribution of plants, trees from all census data were included in this study. Details about geographical division are shown in Figure 7. Both PCoA and UPGMA were used to analyze the results. PCoA plots showed that all three methods could perfectly cluster the communities by sites (Figure 8). This revealed that the tropical forests in the three sites differed substantially in species compositions, which had been verified by previous studies [1]. From UPGMA (Figure 9) clustering, we found that VAW-UniFrac and W-UniFrac provided almost identical clustering results.


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

Chang Q, Luan Y, Sun F - BMC Bioinformatics (2011)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108311&req=5

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.
Mentions: We divided these plots into 19 1-ha plots and compared their compositions using the three statistics. In order to obtain overall insight into the distribution of plants, trees from all census data were included in this study. Details about geographical division are shown in Figure 7. Both PCoA and UPGMA were used to analyze the results. PCoA plots showed that all three methods could perfectly cluster the communities by sites (Figure 8). This revealed that the tropical forests in the three sites differed substantially in species compositions, which had been verified by previous studies [1]. From UPGMA (Figure 9) clustering, we found that VAW-UniFrac and W-UniFrac provided almost identical clustering results.

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

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Mathematics, Shandong University, Jinan, Shandong, PR China.

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.

Show MeSH
Related in: MedlinePlus