Limits...
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.

Show MeSH

Related in: MedlinePlus

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

License
getmorefigures.php?uid=PMC3108311&req=5

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.

Mentions: Figure 1 shows the UPGMA results of the 80 samples based on UniFrac, W-UniFrac and VAW-UniFrac, respectively. VAW-UniFrac clustered the 80 samples into three main clusters (Figure 1c). Cluster 1 contains 8 samples consisting of only native microbiotas from forehead and forearm of individual F3. Cluster 2 contains 21 samples, and 20 of them are native microbiotas from foreheads or forearms of other individuals in the experiment. The microbiotas from the foreheads form a tight subcluster of this cluster. Only one non-native microbiota (F322A2) belongs to this cluster and only four native microbiotas (M420A1, M420A2, F210A1, F210A2) are outside this cluster. In cluster 2, the samples from the same individual, same day and same site (forehead or forearm), but different plots, are clustered together almost perfectly. Cluster 3 contains 51 samples, and 47 of them are the microbiotas after inoculating microbiotas from the foreheads to the forearms. In this cluster, the samples collected at 2, 4 and 8 hours after inoculating at the same plot are always clustered with each other, indicating that the variation across time within the same plot is small compared to variation across different plots. In fact, the two plots "A1" and "A2" on forearm of one individual were always inoculated with microbiotas from foreheads of different individuals. The clustering is in accordance with the main conclusion of the original article: that the variation of skin bacterial communities is primarily explained by habitat, then by individual, and, finally, by time. Neither UniFrac nor W-UniFrac could obtain results as clear as VAW-UniFrac.


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 80 samples where the microbiotas from foreheads were transplanted to forearms. 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 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.
Mentions: Figure 1 shows the UPGMA results of the 80 samples based on UniFrac, W-UniFrac and VAW-UniFrac, respectively. VAW-UniFrac clustered the 80 samples into three main clusters (Figure 1c). Cluster 1 contains 8 samples consisting of only native microbiotas from forehead and forearm of individual F3. Cluster 2 contains 21 samples, and 20 of them are native microbiotas from foreheads or forearms of other individuals in the experiment. The microbiotas from the foreheads form a tight subcluster of this cluster. Only one non-native microbiota (F322A2) belongs to this cluster and only four native microbiotas (M420A1, M420A2, F210A1, F210A2) are outside this cluster. In cluster 2, the samples from the same individual, same day and same site (forehead or forearm), but different plots, are clustered together almost perfectly. Cluster 3 contains 51 samples, and 47 of them are the microbiotas after inoculating microbiotas from the foreheads to the forearms. In this cluster, the samples collected at 2, 4 and 8 hours after inoculating at the same plot are always clustered with each other, indicating that the variation across time within the same plot is small compared to variation across different plots. In fact, the two plots "A1" and "A2" on forearm of one individual were always inoculated with microbiotas from foreheads of different individuals. The clustering is in accordance with the main conclusion of the original article: that the variation of skin bacterial communities is primarily explained by habitat, then by individual, and, finally, by time. Neither UniFrac nor W-UniFrac could obtain results as clear as VAW-UniFrac.

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