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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 72 native microbiota samples from skin microbiota transplant experiments. UPGMA results using (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac.
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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.

Mentions: We wondered why the 8 native samples from individual F3 were clustered together and separated from other native samples of the same habitats according to VAW-UniFrac, while the clustering of native samples from F3 was not detected at all by UniFrac and W-UniFrac. To investigate this phenomenon in more depth, we then applied the three statistics to all 72 native microbiotas in that study. The UPGMA results are shown in Figure 2. The results based on W-UniFrac and VAW-UniFrac are similar. According to both methods, the tongue samples are clustered separately from the skin samples. The forehead samples from individuals other than F3 are clustered together, while the forehead samples from F3 are separated from them. In the result derived by UniFrac, there is no such obvious clustering pattern, but the samples from forehead and forearm of F3 are always mixed. These results further support the observation that the forehead and forearm samples from F3 are significantly different from those of other individuals.


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 72 native microbiota samples from skin microbiota transplant experiments. UPGMA results using (a) UniFrac, (b) W-UniFrac and (c) VAW-UniFrac.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

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.
Mentions: We wondered why the 8 native samples from individual F3 were clustered together and separated from other native samples of the same habitats according to VAW-UniFrac, while the clustering of native samples from F3 was not detected at all by UniFrac and W-UniFrac. To investigate this phenomenon in more depth, we then applied the three statistics to all 72 native microbiotas in that study. The UPGMA results are shown in Figure 2. The results based on W-UniFrac and VAW-UniFrac are similar. According to both methods, the tongue samples are clustered separately from the skin samples. The forehead samples from individuals other than F3 are clustered together, while the forehead samples from F3 are separated from them. In the result derived by UniFrac, there is no such obvious clustering pattern, but the samples from forehead and forearm of F3 are always mixed. These results further support the observation that the forehead and forearm samples from F3 are significantly different from those of other individuals.

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