A Bayesian method for detecting pairwise associations in compositional data

PLoS Computational Biology

Volumn 13, Issue 11, 2017, Pages

  Schwager, Emma ^a,b   Mallick, Himel ^a,b   Ventz, Steffen ^c,d   Huttenhower, Curtis ^a,b  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^c UNIVERSITY OF RHODE ISLAND   (United States)

^d DANA FARBER CANCER INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COVARIANCE MATRIX;

BAYESIAN ANALYSIS; BAYESIAN FRAMEWORKS; BAYESIAN METHODS; COMPOSITIONAL DATA; IN-FIELD; INFORMATION LOSS; MCMC SAMPLING; NORMALISATION; PRECISION MATRIX; SIMULATED DATASETS;

ECOLOGY;

BAYES THEOREM; COMPETITION; COVARIANCE; ECOLOGY; HABITAT; HUMAN; MICROBIOME; NONHUMAN; PROTEOBACTERIA; SAMPLING; SIMULATION; UNCERTAINTY; ALGORITHM; BIOLOGICAL MODEL; BIOLOGY; COMPUTER SIMULATION; MARKOV CHAIN; MICROFLORA; PROCEDURES;

ALGORITHMS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; ECOLOGY; HUMANS; MARKOV CHAINS; MICROBIOTA; MODELS, BIOLOGICAL; PROTEOBACTERIA;

EID: 85036464214     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005852     Document Type: Article

References (35)

1. Pearson K, Mathematical contributions to the theory of evolution. III. regression, heredity, and panmixia. Philos Trans A Math Phys Eng Sci. 1896;187: 253–318.
2. Spearman C, The proof and measurement of association between two things. The American Journal of Psychology. 1904;15: 72–101.
3. Pearson K, Mathematical contributions to the theory of evolution.–On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proc R Soc Lond. 1897;60: 489–498.
4. Chayes FA, On correlation between variables of constant sum. J Geophys Res. 1960;65: 4185–4193.
5. Chayes FA, Kruskal W, Approximate statistical test for correlations between proportions. The Journal of Geology. 1966;74: 692–702.
6. Aitchison J, A new approach to null correlations of proportions. Math Geol. 1981;13: 175–189.
7. Faust K., Sathirapongsasuti F., Izard J, Segata N, Gevers D, Raes J, Huttenhower C, Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;8: e1002606. doi: 10.1371/journal.pcbi.1002606 22807668
8. Friedman J, Alm EJ, Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8: e1002687. doi: 10.1371/journal.pcbi.1002687 23028285
9. Fang H, Huang C, Zhao H, Deng M, CCLasso: Correlation inference for compositional data through lasso. Bioinformatics. 2015;31: 3172–3180. doi: 10.1093/bioinformatics/btv349 26048598
10. Ban Y, An L, Jiang H, Investigating microbial co-occurrence patterns based on metagenomic compositional data. Bioinformatics. 2015;31: 3322–3329. doi: 10.1093/bioinformatics/btv364 26079350
11. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA, Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11: 1–25.
12. Shah RD, Samworth RJ, Variable selection with error control: Another look at stability selection. J R Stat Soc Series B Stat Methodol. 2013;75: 55–80.
13. Wang H, Bayesian graphical lasso models and efficient posterior computation. Bayesian Anal. 2012;7: 867–886.
14. Preston FW, The commonness, and rarity, of species. Ecology. 1948;29: 254–283.
15. Magurran AE, Henderson PA, Explaining the excess of rare species in natural species abundance distributions. Nature. 2003;422: 714–716. doi: 10.1038/nature01547 12700760
16. Koren O, Knights D, Gonzalez A, Waldron L, Segata N, Knight R, et al. A guide to enterotypes across the human body: Meta-analysis of microbial community structures in human microbiome datasets. PLoS Comput Biol. 2013;9: 1–16.
17. Paulson JN, Stine OC, Bravo HC, Pop M, Differential abundance analysis for microbial marker-gene surveys. Nat Meth. 2013;10: 1200–1202.
18. Mitchell TJ, Beauchamp JJ, Bayesian variable selection in linear regression. J Am Stat Assoc. 1988;83: 1023–1032.
19. Mallick H, Yi N, Bayesian methods for high dimensional linear models. J Biom Biostat. 2013;1: 005. doi: 10.4172/2155-6180.S1-005 24511433
20. Stan Development Team. RStan: the R interface to Stan, version 2.6.0 2014. Available: http://mc-stan.org/rstan.html
21. Ren B, Schwager E, Tickle TL, Huttenhower C. SparseDOSSA: Sparse data observations for simulating synthetic abundance. 2016. Available: https://huttenhower.sph.harvard.edu/sparsedossa
22. The Human Microbiome ConsortiumStructure, function and diversity of the healthy human microbiome. Nature. 2012;486: 207–214. doi: 10.1038/nature11234 22699609
23. Aitchison J. A concise guide to compositional data analysis. 2nd compositional data analysis workshop. 2003.
24. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Meth. 2015;12: 902–903.
25. Rajilić-Stojanović M, Vos WM de, The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol Rev. Oxford, UK; 2014;38: 996–1047. doi: 10.1111/1574-6976.12075 24861948
26. Barberán A, Bates ST, Casamayor EO, Fierer N, Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6: 343–351. doi: 10.1038/ismej.2011.119 21900968
27. Kolenbrander PE, Palmer RJ, JrPeriasamy S, Jakubovics NS, Oral multispecies biofilm development and the key role of cell–cell distance. Nat Rev Micro. 2010;8: 471–480.
28. Gajer P, Brotman RM, Bai G, Sakamoto J, Schütte UME, Zhong X, et al. Temporal dynamics of the human vaginal microbiota. Sci Transl Med. 2012;4: 132ra52–132ra52. doi: 10.1126/scitranslmed.3003605 22553250
29. Rasko DA, Rosovitz MJ, Myers GSA, Mongodin EF, Fricke WF, Gajer P, et al. The pangenome structure of escherichia coli: Comparative genomic analysis of e. coli commensal and pathogenic isolates. J Bacteriol. 2008;190: 6881–6893. doi: 10.1128/JB.00619-08 18676672
30. Hogg JS, Hu FZ, Janto B, Boissy R, Hayes J, Keefe R, et al. Characterization and modeling of the haemophilus influenzae core and supragenomes based on the complete genomic sequences of Rd and 12 clinical nontypeable strains. Genome Biol. 2007;8: R103. doi: 10.1186/gb-2007-8-6-r103 17550610
31. Arthur JC, Perez-Chanona E, Mühlbauer M, Tomkovich S, Uronis JM, Fan T-J, et al. Intestinal inflammation targets cancer-inducing activity of the microbiota. Science. 2012;338: 120–123. doi: 10.1126/science.1224820 22903521
32. Li Q, Lin N, The bayesian elastic net. Bayesian Anal. 2010;5: 151–170.
33. Carvalho CM, Polson NG, Scott JG, The horseshoe estimator for sparse signals. Biometrika. 2010;97: 465–480.
34. Dawid AP, Lauritzen SL, Hyper markov laws in the statistical analysis of decomposable graphical models. The Annals of Statistics. 1993;21: 1272–1317.
35. Wong F, Carter CK, Kohn R, Efficient estimation of covariance selection models. Biometrika. 2003;90: 809–830.