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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 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.
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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.

Mentions: As observed in [17], both PCoA and UPGMA with UniFrac revealed clear associations between populations of microbial communities and kinship (Figure 3a and Figure 4a). The siblings were clustered together, including two mothers who were sisters (M1 and M3). M2A-1 and M2A-2, which were represented by fewer than 200 sequences, were the only mice not clustered with their mothers (Figure 4a), potentially resulting from the relatively small number of sequences in their communities. These results indicate that the presence/absence of microbial species in mouse gut is mainly determined by kinship. However, we were more interested in the performance of W-UniFrac and VAW-UniFrac. When W-UniFrac was used to cluster the mice, Lozupone et al. [17] indicated that there was a greater correlation with the obesity genotype than with kinship. Figure 3 shows that analysis using VAW-UniFrac reveals not only a correlation with obesity genotype, but also a clear correlation with kinship. Although PCoA analysis using W-UniFrac does not separate the kinship well, PCoA analysis using VAW-UniFrac clearly separates the offspring of M2 from the offspring of M1 and M3, indicating that kinship plays the most important role in gut microbial community (Figures 3b and 3c). In fact, the ob/ob individuals tend to cluster together within a given sibship using VAW-UniFrac, but this is not so clear by W-UniFrac. For instance, M1-1(ob/ob) and M1-2(ob/ob) clustered tightly by VAW-UniFrac (Figure 4c), but diverged by W-UniFrac (Figure 4b). Moreover, M3-1(+/+), M3-2(ob/+) and M3-3(+,+) were clustered together by VAW-UniFrac, but were dispersed by W-UniFrac (Figure 4b).


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

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

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.
Mentions: As observed in [17], both PCoA and UPGMA with UniFrac revealed clear associations between populations of microbial communities and kinship (Figure 3a and Figure 4a). The siblings were clustered together, including two mothers who were sisters (M1 and M3). M2A-1 and M2A-2, which were represented by fewer than 200 sequences, were the only mice not clustered with their mothers (Figure 4a), potentially resulting from the relatively small number of sequences in their communities. These results indicate that the presence/absence of microbial species in mouse gut is mainly determined by kinship. However, we were more interested in the performance of W-UniFrac and VAW-UniFrac. When W-UniFrac was used to cluster the mice, Lozupone et al. [17] indicated that there was a greater correlation with the obesity genotype than with kinship. Figure 3 shows that analysis using VAW-UniFrac reveals not only a correlation with obesity genotype, but also a clear correlation with kinship. Although PCoA analysis using W-UniFrac does not separate the kinship well, PCoA analysis using VAW-UniFrac clearly separates the offspring of M2 from the offspring of M1 and M3, indicating that kinship plays the most important role in gut microbial community (Figures 3b and 3c). In fact, the ob/ob individuals tend to cluster together within a given sibship using VAW-UniFrac, but this is not so clear by W-UniFrac. For instance, M1-1(ob/ob) and M1-2(ob/ob) clustered tightly by VAW-UniFrac (Figure 4c), but diverged by W-UniFrac (Figure 4b). Moreover, M3-1(+/+), M3-2(ob/+) and M3-3(+,+) were clustered together by VAW-UniFrac, but were dispersed by W-UniFrac (Figure 4b).

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