Compositional analysis: A valid approach to analyze microbiome high-throughput sequencing data

Canadian Journal of Microbiology

Volumn 62, Issue 8, 2016, Pages 692-703

  Gloor, Gregory B ^a,b   Reid, Gregor ^a,b  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b LAWSON HEALTH RESEARCH INSTITUTE   (Canada)

Author keywords

Compositional data; Correlation; Microbiome; Multiple test correction

Indexed keywords

BIOINFORMATICS; CORRELATION; DATA ASSIMILATION; DATA SET; DNA; DRUG; MICROBIOLOGY; PROBIOTICS;

ARTICLE; ATOPOBIUM; BIOINFORMATICS; DATA ANALYSIS; DIALISTER; DNA SEQUENCE; HIGH THROUGHPUT SEQUENCING; HUMAN; LACTOBACILLUS; LACTOBACILLUS CRISPATUS; LACTOBACILLUS INERS; MEGASPHAERA; MICROBIOME; PREVOTELLA; PRIORITY JOURNAL; STREPTOCOCCUS; CANADA; CLUSTER ANALYSIS; MICROFLORA;

CANADA;

PROBIOTIC AGENT;

CANADA; CLUSTER ANALYSIS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MICROBIOTA; PROBIOTICS; SEQUENCE ANALYSIS, DNA;

EID: 84982953236     PISSN: 00084166     EISSN: 14803275     Source Type: Journal    
DOI: 10.1139/cjm-2015-0821     Document Type: Article

References (40)

1. Aitchison, J. 1986. The statistical analysis of compositional data. Chapman and Hall, London, UK.
2. Aitchison, J., and Greenacre, M. 2002. Biplots of compositional data. J. R. Stat. Soc. Ser. C Appl. Stat. 51: 375-92. doi:10.1111/1467-9876.00275.
3. Benjamini, Y., and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57: 289-300. Available from http://www.jstor.org/stable/2346101.
4. David, L.A., Maurice, C.F., Carmody, R.N., Gootenberg, D.B., Button, J.E., Wolfe, B.E., et al. 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484): 559-563. doi:10.1038/nature12820.PMID:24336217.
5. Di Bella, J.M., Bao, Y., Gloor, G.B., Burton, J.P., and Reid, G. 2013. High throughput sequencing methods and analysis for microbiome research. J. Microbiol. Methods, 95(3): 401-414. doi:10.1016/j.mimet.2013.08.011.PMID:24029734.
6. Fernandes, A.D., Macklaim, J.M., Linn, T.G., Reid, G., and Gloor, G.B. 2013. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One, 8(7): e67019. doi:10.1371/journal.pone.0067019.PMID:23843979.
7. 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 RNAseq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome, 2: 15. doi:10.1186/2049-2618-2-15.PMID:24910773.
8. Filzmoser, P., Hron, K., and Reimann, C. 2009. Univariate statistical analysis of environmental (compositional) data: problems and possibilities. Sci. Total Environ. 407: 6100-6108. doi:10.1016/j.scitotenv.2009.08.008.PMID:19740525.
9. Frémont, M., Coomans, D., Massart, S., and De Meirleir, K. 2013. High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in myalgic encephalomyelitis/ chronic fatigue syndrome patients. Anaerobe, 22: 50-56. doi:10.1016/j.anaerobe.2013.06.002.PMID:23791918.
10. Friedman, J., and Alm, E.J. 2012. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8(9): e1002687. doi:10.1371/journal.pcbi.1002687.PMID:23028285.
11. Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit,S.,etal.2004.Bioconductor:opensoftwaredevelopmentforcomputationalbiologyandbioinformatics.GenomeBiol.5(10):R80.doi:10.1186/gb-2004-5-10-r80.PMID:15461798.
12. Gloor, G.B. 2016. Compositional data analysis for high throughput sequencing: an example from 16S rRNA gene sequencing. Available from https://github.com/ggloor/CJM_Supplement. doi:10.5281/zenodo.49579.
13. Gloor, G.B., Macklaim, J.M., and Fernandes, A.F. 2016a. Displaying variation in large datasets: a visual summary of effect sizes. J. Comput. Graph. Stat. In press. doi:10.1080/10618600.2015.1131161.
14. Gloor, G.B., Macklaim, J.M., Vu, M., and Fernandes, A.F. 2016b. Compositional uncertainty should not be ignored in highthroughput sequencing data analysis. Austrian Journal of Statistics. In press.
15. Hsiao, E.Y., McBride, S.W., Hsien, S., Sharon, G., Hyde, E.R., McCue, T., et al. 2013. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell, 155(7): 1451-1463. doi:10.1016/j.cell.2013.11.024.PMID:24315484.
16. Hubert, L., and Wainer, H. 2012. A statistical guide for the ethically perplexed. CRC Press, London, UK.
17. Kuczynski, J., Stombaugh, J., Walters, W.A., González, A., Caporaso, J.G., and Knight, R. 2012. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Chapter 1, Unit 1E-5. Curr. Protoc. Microbiol. doi:10.1002/9780471729259.mc01e05s27.PMID:23184592.
18. 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.PMID:25950956.
19. Kwak, D.S., Jun, D.W., Seo, J.G., Chung, W.S., Park, S.E., Lee, K.N., et al. 2014. Short-term probiotic therapy alleviates small intestinal bacterial overgrowth, but does not improve intestinal permeability in chronic liver disease. Eur. J. Gastroenterol. Hepatol. 26(12): 1353-1359. doi:10.1097/MEG.0000000000000214.PMID:25244414.
20. Lourenço, T.G., Heller, D., Silva-Boghossian, C.M., Cotton, S.L., Paster, B.J., and Colombo, A.P. 2014. Microbial signature profiles of periodontally healthy and diseased patients. J. Clin. Periodontol. 41(11): 1027-1036. doi:10.1111/jcpe.12302.PMID:25139407.
21. 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.PMID:25775355.
22. Lozopone, C., and Knight, R. 2005. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71: 8228-8235. doi:10.1128/AEM.71.12.8228-8235.2005.PMID:16332807.
23. Macklaim, J.M., Clemente, J.C., Knight, R., Gloor, G.B., and Reid, G. 2015. Changes in vaginal microbiota following antimicrobial and probiotic therapy. Microb. Ecol. Health Dis. 26: 27799. doi:10.3402/mehd.v26.27799.PMID:26282697.
24. 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.PMID:26028277.
25. Martín-Fernández, J.A., Barceló-Vidal, C., and Pawlowsky-Glahn, V. 1998. Measures of difference for compositional data and hierarchical clustering methods. In Proceedings of the IAMG’98, 1998 Annual Conference of the International Association for Mathematical Geology. Edited by A. Buccianti, G. Nardi, and R. Potenza. pp. 526-531.
26. Palarea-Albaladejo, J., and Martín-Fernández, J.A. 2015. Compositions — R package for multivariate imputation of left-censored data under a compositional approach. Ch-emometr. Intell. Lab. Syst. 143: 85-96. doi:10.1016/j.chemolab.2015.02.019.
27. Pawlowsky-Glahn, V., Egozcue, J.J., and Tolosana-Delgado, R. 2015. Modeling and analysis of compositional data. John Wiley & Sons, Springer, London, UK.
28. Pearson, K. 1896. 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. 60: 489-498. doi:10.1098/rspl.1896.0076.
29. R Core Team. 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from https://www.R-project.org/.
30. Rajca, S., Grondin, V., Louis, E., Vernier-Massouille, G., Grimaud, J.C., Bouhnik, Y., et al. 2014. Alterations in the intestinal microbiome (dysbiosis) as a predictor of relapse after infliximab withdrawal in Crohn’s disease. Inflamm. Bowel Dis. 20(6): 978-986. doi:10.1097/MIB.0000000000000036.PMID:24788220.
31. Reardon, S. 2013, Bacterium can reverse autism-like behaviour in mice. Nature. doi:10.1038/nature.2013.14308.
32. Schellenberg, J., Links, M.G., Hill, J.E., Dumonceaux, T.J., Peters, G.A., Tyler, S., et al. 2009. Pyrosequencing of the chaperonin-60 universal target as a tool for determining microbial community composition. Appl. Environ. Microbiol. 75: 2889-2898. doi:10.1128/AEM.01640-08.PMID:19270139.
33. Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., et al. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75: 7537-7541. doi:10.1128/AEM.01541-09.PMID:19801464.
34. Seekatz, A.M., Aas, J., Gessert, C.E., Rubin, T.A., Saman, D.M., Bakken, J.S., and Young, V.B. 2014. Recovery of the gut microbiome following fecal microbiota transplantation. MBio, 5(3): e00893-14. doi:10.1128/mBio.00893-14.PMID:24939885.
35. Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., and Huttenhower, C. 2011. Metagenomic biomarker discovery and explanation. Genome Biol. 12: R60. doi:10.1186/gb-2011-12-6-r60.PMID:21702898.
36. Urbaniak, C., Cummins, J., Brackstone, M., Macklaim, J.M., Gloor, G.B., Baban, C.K., et al. 2014. Microbiota of human breast tissue. Appl. Environ. Microbiol. 80(10): 3007-3014. doi:10.1128/AEM.00242-14.PMID:24610844.
37. Van den Boogaart, K.G., and Tolosana-Delgado, R. 2013. Analyzing compositional data with R. Springer, Berlin Heidelberg, Germany.
38. Walker, A.W., Martin, J.C., Scott, P., Parkhill, J., Flint, H.J., and Scott, K.P. 2015. 16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome, 3: 26. doi:10.1186/s40168-015-0087-4.PMID:26120470.
39. Warton, D.I., Wright, S.T., and Wang, Y. 2012. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3: 89-101. doi:10.1111/j.2041-210X.2011.00127.x.
40. White, J.R., Nagarajan, N., and Pop, M. 2009. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5: e1000352. doi:10.1371/journal.pcbi.1000352.PMID:19360128.