Microbiome datasets are compositional: And this is not optional

Frontiers in Microbiology

Volumn 8, Issue NOV, 2017, Pages

  Gloor, Gregory B ^a   Macklaim, Jean M ^a   Pawlowsky Glahn, Vera ^b   Egozcue, Juan J ^c  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b UNIVERSITY OF GIRONA   (Spain)

^c UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

Bayesian estimation; Compositional data; Correlation; Count normalization; High throughput sequencing; Microbiota; Relative abundance

Indexed keywords

RNA 16S;

16S RRNA GENE; ANTIBIOTIC SENSITIVITY; BIBLIOGRAPHIC DATABASE; CORRELATION ANALYSIS; ECOSYSTEM; FALSE POSITIVE RATE; GENE; GENE AMPLIFICATION; GENE SEQUENCE; HIGH THROUGHPUT SEQUENCING; MATHEMATICAL MODEL; MICROBIAL SENSITIVITY TEST; MICROBIOME; NONHUMAN; RNA SEQUENCE; SHORT SURVEY;

EID: 85034034202     PISSN: None     EISSN: 1664302X     Source Type: Journal    
DOI: 10.3389/fmicb.2017.02224     Document Type: Short Survey

References (43)

1. Aitchison, J. (1983). Principal component analysis of compositional data. Biometrika 70, 57-65. doi: 10.1093/biomet/70.1.57
2. Aitchison, J. (1986). The Statistical Analysis of Compositional Data. London: Chapman and Hall
3. Aitchison, J., Barceló-Vidal, C., Martín-Fernández, J. A., and Pawlowsky-Glahn, V. (2000). Logratio analysis and compositional distance. Math. Geol. 32, 271-275. doi: 10.1023/A:1007529726302
4. Aitchison, J., and Greenacre, M. (2002). Biplots of compositional data. J. Roy. Stat. Soc. Ser. C 51, 375-392. doi: 10.1111/1467-9876.00275
5. Anders, S., and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol. 11:R106. doi: 10.1186/gb-2010-11-10-r106
6. Bian, G., Gloor, G. B., Gong, A., Jia, C., Zhang, W., Hu, J., et al. (2017). The gut microbiota of healthy aged chinese is similar to that of the healthy young. mSphere 2:e00327-17. doi: 10.1128/mSphere.00327-17
7. Erb, I., and Notredame, C. (2016). How should we measure proportionality on relative gene expression data? Theory Biosci. 135, 21-36. doi: 10.1007/s12064-015-0220-8
8. Fernandes, A. D., Macklaim, J. M., Linn, T., Reid, G., and Gloor, G. B. (2013). ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-seq. PLoS ONE 8:e67019
9. Fernandes, A. D., Reid, J. N., Macklaim, J. M., McMurrough, T. A., Edgell, D. R., and Gloor, G. B. (2014). Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15.1-15.13. doi: 10.1186/2049-2618-2-15
10. Friedman, J., and Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8:e1002687. doi: 10.1371/journal.pcbi.1002687
11. Gloor, G. B., Macklaim, J. M., Vu, M., and Fernandes, A. D. (2016a). Compositional uncertainty should not be ignored in high-throughput sequencing data analysis. Aust. J. Stat. 45, 73-87. doi: 10.17713/ajs.v45i4.122
12. Gloor, G. B., and Reid, G. (2016). Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692-703. doi: 10.1139/cjm-2015-0821
13. Gloor, G. B., Wu, J. R., Pawlowsky-Glahn, V., and Egozcue, J. J. (2016b). It's all relative: analyzing microbiome data as compositions. Ann. Epidemiol. 26, 322-329. doi: 10.1016/j.annepidem.2016.03.003
14. Gorvitovskaia, A., Holmes, S. P., and Huse, S. M. (2016). Interpreting prevotella and bacteroides as biomarkers of diet and lifestyle. Microbiome 4:15. doi: 10.1186/s40168-016-0160-7
15. Hawinkel, S., Mattiello, F., Bijnens, L., and Thas, O. (2017). A broken promise: microbiome differential abundance methods do not control the false discovery rate. Brief. Bioinf. bbx104. doi: 10.1093/bib/bbx104
16. Jaynes, E. T., and Bretthorst, G. L. (2003). Probability Theory: The Logic of Science. Cambridge, UK: Cambridge University Press
17. Kurtz, Z. D., Müller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., and Bonneau, R. A. (2015). Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11:e1004226. doi: 10.1371/journal.pcbi.1004226
18. Lovell, D., Müller, W., Taylor, J., Zwart, A., and Helliwell, C. (2011). 'Proportions, percentages, ppm: do the molecular biosciences treat compositional data right,' in Compositional Data Analysis: Theory and Applications, eds V. Pawlowsky-Glahn and A. Buccianti (London: John Wiley & Sons, Ltd.), 193-207
19. Lovell, D., Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S., and Bähler, J. (2015). Proportionality: a valid alternative to correlation for relative data. PLoS Comput. Biol. 11:e1004075. doi: 10.1371/journal.pcbi.1004075
20. Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J., and Knight, R. (2011). Unifrac: an effective distance metric for microbial community comparison. ISME J. 5, 169-172. doi: 10.1038/ismej.2010.133
21. Macklaim, M. J., Fernandes, D. A., Di Bella, M. J., Hammond, J.-A., Reid, G., and Gloor, G. B. (2013). Comparative meta-RNA-seq of the vaginal microbiota and differential expression by Lactobacillus iners in health and dysbiosis. Microbiome 1:15. doi: 10.1186/2049-2618-1-12
22. Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., and Peddada, S. D. (2015). Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26:27663. doi: 10.3402/mehd.v26.27663
23. Martín-Fernández, J., Barceló-Vidal, C., Pawlowsky-Glahn, V., Buccianti, A., Nardi, G., and Potenza, R. (1998). Measures of difference for compositional data and hierarchical clustering methods. Proc. IAMG. 98, 526-531
24. McMillan, A., Rulisa, S., Sumarah, M., Macklaim, J. M., Renaud, J., Bisanz, J. E., et al. (2015). A multi-platform metabolomics approach identifies highly specific biomarkers of bacterial diversity in the vagina of pregnant and non-pregnant women. Sci. Rep. 5, 14174. doi: 10.1038/srep14174
25. McMurdie, P. J., and Holmes, S. (2013). phyloseq: an r package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8:e61217. doi: 10.1371/journal.pone.0061217
26. McMurdie, P. J., and Holmes, S. (2014). Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10:e1003531. doi: 10.1371/journal.pcbi.1003531
27. McMurrough, T. A., Dickson, R. J., Thibert, S. M. F., Gloor, G. B., and Edgell, D. R. (2014). Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues. Proc. Natl. Acad. Sci. U.S.A. 111, E2376-E2383. doi: 10.1073/pnas.1322352111
28. Morton, J. T., Toran, L., Edlund, A., Metcalf, J. L., Lauber, C., and Knight, R. (2017). Uncovering the horseshoe effect in microbial analyses. mSystems 2:e00166-16. doi: 10.1128/mSystems.00166-16
29. Ortego, M. I., and Egozcue, J. J. (2013). 'Spurious copulas,' in Proceedings of the 5th Workshop on Compositional Data Analysis, CoDaWork 2013 (Vorau)
30. Palarea-Albaladejo, J., and Martín-Fernández, J. A. (2015). zCompositions-R package for multivariate imputation of left-censored data under a compositional approach. Chemometr. Intel. Lab. Syst. 143, 85-96. doi: 10.1016/j.chemolab.2015.02.019
31. Pawlowsky-Glahn, V., Egozcue, J. J., and Tolosana-Delgado, R. (2015). Modeling and Analysis of Compositional Data. London: John Wiley & Sons
32. Pearson, K. (1897). 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. Roy. Soc. Lond. 60, 489-498
33. Quinn, T., Richardson, M. F., Lovell, D., and Crowley, T. (2017). propr: An R-package for identifying proportionally abundant features using compositional data analysis. bioRxiv. doi: 10.1101/104935
34. Robinson, C. K., Brotman, R. M., and Ravel, J. (2016). Intricacies of assessing the human microbiome in epidemiologic studies. Ann. Epidemiol. 26, 311-321. doi: 10.1016/j.annepidem.2016.04.005
35. Robinson, M. D., and Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25.1-R25.9. doi: 10.1186/gb-2010-11-3-r25
36. Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience 31, 131-134
37. Silverman, J. D., Washburne, A. D., Mukherjee, S., and David, L. A. (2017). A phylogenetic transform enhances analysis of compositional microbiota data. Elife 6:21887. doi: 10.7554/eLife.21887
38. Thorsen, J., Brejnrod, A., Mortensen, M., Rasmussen, M. A., Stokholm, J., Al-Soud, W. A., et al. (2016). Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies. Microbiome 4, 62. doi: 10.1186/s40168-016-0208-8
39. Tsilimigras, M. C. B., and Fodor, A. A. (2016). Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann. Epidemiol. 26, 330-335. doi: 10.1016/j.annepidem.2016.03.002
40. Van den Boogaart, K. G., and Tolosana-Delgado, R. (2013). Analyzing Compositional Data with R, London, UK: Springer
41. Weiss, S., Van Treuren, W., Lozupone, C., Faust, K., Friedman, J., Deng, Y., et al. (2016). Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10, 1669-1681. doi: 10.1038/ismej.2015.235
42. Weiss, S., Xu, Z. Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., et al. (2017). Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27. doi: 10.1186/s40168-017-0237-y
43. Wong, R. G., Wu, J. R., and Gloor, G. B. (2016). Expanding the UniFrac toolbox. PLoS ONE 11:e0161196. doi: 10.1371/journal.pone.0161196