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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

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.
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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.

Mentions: After applying the three statistics to each pair of the 16 samples, we used PCoA and UPGMA to analyze the results and investigated the performance of the three statistics (Figure 5 and Figure 6). The results from both PCoA and UPGMA analysis indicated that UniFrac could not detect the similarity between two collections derived from the same sample. Instead, it clustered the data from Sanger sequencing and pyrosequencing separately. On the other hand, W-UniFrac and VAW-UniFrac could cluster some libraries of the two sequencing methods together according to geographical transect at some level, for example, the most water-logged sites (T3-325, T3-390, and T3-455). VAW-UniFrac separated T3-0 (the driest) from the others. This is reasonable because T3-0 was the only site in a vegetated, upland area, and other sites were from exposed lakebed and water's edge [25].


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

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

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

License
Show All Figures
getmorefigures.php?uid=PMC3108311&req=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.
Mentions: After applying the three statistics to each pair of the 16 samples, we used PCoA and UPGMA to analyze the results and investigated the performance of the three statistics (Figure 5 and Figure 6). The results from both PCoA and UPGMA analysis indicated that UniFrac could not detect the similarity between two collections derived from the same sample. Instead, it clustered the data from Sanger sequencing and pyrosequencing separately. On the other hand, W-UniFrac and VAW-UniFrac could cluster some libraries of the two sequencing methods together according to geographical transect at some level, for example, the most water-logged sites (T3-325, T3-390, and T3-455). VAW-UniFrac separated T3-0 (the driest) from the others. This is reasonable because T3-0 was the only site in a vegetated, upland area, and other sites were from exposed lakebed and water's edge [25].

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