Comparisons of distance methods for combining covariates and abundances in microbiome studies - PubMed (original) (raw)
Comparative Study
Comparisons of distance methods for combining covariates and abundances in microbiome studies
Julia Fukuyama et al. Pac Symp Biocomput. 2012.
Abstract
This article compares different methods for combining abundance data, phylogenetic trees and clinical covariates in a nonparametric setting. In particular we study the output from the principal coordinates analysis on UNIFRAC and WEIGHTED UNIFRAC distances and the output from a double principal coordinate analyses DPCOA using distances computed on the phylogenetic tree. We also present power comparisons for some of the standard tests of phylogenetic signal between different types of samples. These methods are compared both on simulated and real data sets. Our study shows that DPCoA is less robust to outliers, and more robust to small noisy fluctuations around zero.
Figures
Fig. 1
(a) shows ordination of community points by PCoA with the UniFrac metric; (b) by DPCoA. (c) shows ordination of species points by DPCoA: blue indicates species whose levels change in subjects A/B versus C/D, red indicates species whose levels change along a gradient related to location, and green indicates all other species.
Fig. 2
Comparisons of DPCoA (row 1) and PCoA/MDS with unweighted UniFrac (row 2) for different noise levels. Columns correspond to data sets with noise levels .01, .2, and .39.
Fig. 3
Comparing the UniFrac variants. From left to right: PCoA/MDS with unweighted UniFrac, with weighted UniFrac, and with weighted UniFrac performed on presence/absence data extracted from the abundance data used in the other two plots.
Fig. 4
(a) PCoA/MDS of the
otus
based on the patristic distance, (b) community and (c) species points for DPCoA after removing two outlying species.
Fig. 5
Community points as represented by DPCoA (left) and PCoA/MDS with unweighted UniFrac (right). The labels represent subject plus antibiotic condition. The ordinations are the same as in figures 4 and 3.
Fig. 6
The left shows the _p_-values of the Mantel (circles), RV (pluses), and adonis (diamonds) tests for a location effect at different magnitudes of the effect. The right shows the _p_-values of the Abouheif (x's), adonis (diamonds), and UniFrac (triangles) tests at different magnitudes of the group effect.
Similar articles
- Emphasis on the deep or shallow parts of the tree provides a new characterization of phylogenetic distances.
Fukuyama J. Fukuyama J. Genome Biol. 2019 Jun 28;20(1):131. doi: 10.1186/s13059-019-1735-y. Genome Biol. 2019. PMID: 31253178 Free PMC article. - Associating microbiome composition with environmental covariates using generalized UniFrac distances.
Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H. Chen J, et al. Bioinformatics. 2012 Aug 15;28(16):2106-13. doi: 10.1093/bioinformatics/bts342. Epub 2012 Jun 17. Bioinformatics. 2012. PMID: 22711789 Free PMC article. - Fiber supplementation influences phylogenetic structure and functional capacity of the human intestinal microbiome: follow-up of a randomized controlled trial.
Holscher HD, Caporaso JG, Hooda S, Brulc JM, Fahey GC Jr, Swanson KS. Holscher HD, et al. Am J Clin Nutr. 2015 Jan;101(1):55-64. doi: 10.3945/ajcn.114.092064. Epub 2014 Nov 12. Am J Clin Nutr. 2015. PMID: 25527750 Clinical Trial. - Ortholog identification in the presence of domain architecture rearrangement.
Sjölander K, Datta RS, Shen Y, Shoffner GM. Sjölander K, et al. Brief Bioinform. 2011 Sep;12(5):413-22. doi: 10.1093/bib/bbr036. Epub 2011 Jun 28. Brief Bioinform. 2011. PMID: 21712343 Free PMC article. Review. - Correlation and association analyses in microbiome study integrating multiomics in health and disease.
Xia Y. Xia Y. Prog Mol Biol Transl Sci. 2020;171:309-491. doi: 10.1016/bs.pmbts.2020.04.003. Epub 2020 May 23. Prog Mol Biol Transl Sci. 2020. PMID: 32475527 Review.
Cited by
- Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold.
Chiarello M, McCauley M, Villéger S, Jackson CR. Chiarello M, et al. PLoS One. 2022 Feb 24;17(2):e0264443. doi: 10.1371/journal.pone.0264443. eCollection 2022. PLoS One. 2022. PMID: 35202411 Free PMC article. - Selective Probiotic Treatment Positively Modulates the Microbiota-Gut-Brain Axis in the BTBR Mouse Model of Autism.
Pochakom A, Mu C, Rho JM, Tompkins TA, Mayengbam S, Shearer J. Pochakom A, et al. Brain Sci. 2022 Jun 14;12(6):781. doi: 10.3390/brainsci12060781. Brain Sci. 2022. PMID: 35741667 Free PMC article. - Emphasis on the deep or shallow parts of the tree provides a new characterization of phylogenetic distances.
Fukuyama J. Fukuyama J. Genome Biol. 2019 Jun 28;20(1):131. doi: 10.1186/s13059-019-1735-y. Genome Biol. 2019. PMID: 31253178 Free PMC article. - Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment.
Fukuyama J, Rumker L, Sankaran K, Jeganathan P, Dethlefsen L, Relman DA, Holmes SP. Fukuyama J, et al. PLoS Comput Biol. 2017 Aug 18;13(8):e1005706. doi: 10.1371/journal.pcbi.1005706. eCollection 2017 Aug. PLoS Comput Biol. 2017. PMID: 28821012 Free PMC article. - Associating microbiome composition with environmental covariates using generalized UniFrac distances.
Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H. Chen J, et al. Bioinformatics. 2012 Aug 15;28(16):2106-13. doi: 10.1093/bioinformatics/bts342. Epub 2012 Jun 17. Bioinformatics. 2012. PMID: 22711789 Free PMC article.
References
- Ihaka R, Gentleman R. Journal of Computational and Graphical Statistics. 1996;5:299.
- Paradis E, Claude J, Strimmer K. Bioinformatics. 2004;20:289. - PubMed