Abstract
The human microbiome is associated with complex disorders such as diabetes, cancer, obesity and cardiovascular disorders. Recent technological developments have allowed researchers to fully quantify the composition of the microbiome using culture-independent approaches, resulting in a large amount of microbiome data, which provide invaluable opportunities to assess the important contributions of the microbiome to human health and disease. In this chapter, we discuss and evaluate multiple statistical approaches for processing, summarizing, and analyzing microbiome data. Specifically, we provide programming scripts for processing microbiome data using QIIME and calculating alpha and beta diversities, assessing the association between diversities and outcomes of interest using R programs, as well as interpretation of results. We illustrate the methods in the context of analyzing the foregut microbiome in esophageal adenocarcinoma.
The original version of this chapter was revised. A correction to this chapter can be found at https://doi.org/10.1007/978-1-4939-7274-6_32
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Change history
05 September 2018
The original version of this chapter was inadvertently published without including the dbGaP acknowledgment. The updated chapter now contains that information.
References
Ursell LK, Metcalf JL, Parfrey LW, Knight R (2012) Defining the human microbiome. Nutr Rev 70(Suppl 1):S38–S44
Li H (2015) Microbiome, metagenomics, and high-dimensional compositional data analysis. Annu Rev Stat Appl 2:73–94
Backhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI (2005) Host-bacterial mutualism in the human intestine. Science 307:1915–1920
Human Microbiome Project (2016) About HMP metagenomic sequencing & analysis. http://hmpdacc.org/micro_analysis/microbiome_ analyses.php
Claus SP, Guillou H, Ellero-Simatos S (2016) The gut microbiota: a major player in the toxicity of environmental pollutants? NPJ Biofilms Microbiomes 2:16003
National Institutes of Health (2016) NIH Human Microbiome Project defines normal bacterial makeup of the body. https://www.nih.gov/news-events/news-releases/nih-human-microbiome-project-defines-normal-bacterial-makeup-body
Hartstra AV, Bouter KEC, Backhed F, Nieuwdorp M (2015) Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care 38:159–165
Tang WHW, Hazen SL (2014) The contributory role of gut microbiota in cardiovascular disease. J Clin Invest 124:4204–4211
Dulal S, Keku TO (2014) Gut microbiome and colorectal adenomas. Cancer J 20:225–231
Illumina (2016) Introduction to human microbiome analysis, Survey the genomes of entire communities. http://www.illumina.com/areas-of-interest/microbiology/human-microbiome-analysis.html
Woo PCY, Lau SKP, Teng JLL, Tse H, Yuen KY (2008) Then and now: use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories. Clin Microbiol Infect 14:908–934
Clarridge JE (2004) Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev 17:840–862
Hamady M, Knight R (2009) Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res 19:1141–1152
Fiona Stewart EY (2014) Addressing challenges in microbiome DNA analysis, NEB UK Expressions
Brooks JP (2016) Challenges for case-control studies with microbiome data. Ann Epidemiol 26:336–341
Yang L, Chaudhary N, Baghdadi J, Pei Z (2014) Microbiome in reflux disorders and esophageal adenocarcinoma. Cancer J 20:207–210
Gilles A, Meglecz E, Pech N, Ferreira S, Malausa T, Martin JF (2011) Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing. BMC Genomics 12:245
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6:1621–1624
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336
Erica Plummer JT, Bulach DM, Garland SM, Tabrizi SN (2015) A comparison of three bioinformatics pipelines for the analysis of preterm gut microbiota using 16S rRNA gene sequencing data. J Proteomics Bioinform 8:283–291
Navas-Molina JA, Peralta-Sanchez JM, Gonzalez A, McMurdie PJ, Vazquez-Baeza Y, Xu ZJ, Ursell LK, Lauber C, Zhou HW, Song SJ, Huntley J, Ackermann GL, Berg-Lyons D, Holmes S, Caporaso JG, Knight R (2013) Advancing our understanding of the human microbiome using QIIME. Methods Enzymol 531:371–444
Mir K, Neuhaus K, Bossert M, Schober S (2013) Short barcodes for next generation sequencing. PLoS One 8:e82933
Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10:996–998
Scitable by nature education (2016) Primer. http://www.nature.com/scitable/definition/primer-305
De Beuf K, De Schrijver J, Thas O, Van Criekinge W, Irizarry RA, Clement L (2012) Improved base-calling and quality scores for 454 sequencing based on a Hurdle Poisson model. BMC Bioinformatics 13:303
Si XF, Baselga A, Leprieur F, Song X, Ding P (2016) Selective extinction drives taxonomic and functional alpha and beta diversities in island bird assemblages. J Anim Ecol 85:409–418
McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217
Hill MO (1973) Diversity and evenness: a unifying notation and its consequences. Ecology 54:427–432
Li K, Bihan M, Yooseph S, Methe BA (2012) Analyses of the microbial diversity across the human microbiome. PLoS One 7:e32118
Lande R (1996) Statistics and partitioning of species diversity, and similarity among multiple communities. Oikos 76:5–13
Basualdo CV (2011) Choosing the best non-parametric richness estimator for benthic macroinvertebrates databases. Rev Soc Entomol Argent 70(1–2):27–38
Sandra D, Williamson KB (2013) Species richness and diversity of a terrestrial insular environment: serpentine of the Barberton Greenstone Belt, South Africa. Int J Biodivers Conserv 5(5):296–310
Morris EK, Caruso T, Buscot F, Fischer M, Hancock C, Maier TS, Meiners T, Muller C, Obermaier E, Prati D, Socher SA, Sonnemann I, Waschke N, Wubet T, Wurst S, Rillig MC (2014) Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol Evol 4:3514–3524
Nagendra H (2002) Opposite trends in response for the Shannon and Simpson indices of landscape diversity. Appl Geogr 22:175–186
Saucedo-Garcia A, Anaya AL, Espinosa-Garcia FJ, Gonzalez MC (2014) Diversity and communities of foliar endophytic fungi from different agroecosystems of Coffea arabica L. in two regions of Veracruz, Mexico. PLoS One 9:e98454
Williams VL, Witkowski ETF, Balkwill K (2005) Application of diversity indices to appraise plant availability in the traditional medicinal markets of Johannesburg, South Africa. Biodivers Conserv 14:2971–3001
Colwell RK (2009) Biodiversity: concepts, patterns, and measurement. In: Levin SA (ed) The Princeton guide to ecology. Princeton University Press, Princeton, NJ, pp 257–263
Fisher RA, Corbet AS, Williams CB (1943) The relation between the number of species and the number of individuals in a random sample of an animal population. J Anim Ecol 12:42–58
Magurran AE (2004) Measuring biological diversity. Blackwell Publishing, Oxford, UK
Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJ (2001) Counting the uncountable: statistical approaches to estimating microbial diversity. Appl Environ Microbiol 67:4399–4406
Chao A, Ma MC, Yang MCK (1993) Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80:193–201
Gotelli NJ, Colwell RK (2010) Estimating species richness. In: Magurran AE, McGill BJ (eds) Frontiers in measuring biodiversity. Oxford University, New York, pp 39–54
Chao A, Chazdon RL, Colwell RK, Shen TJ (2005) A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol Lett 8:148–159
Soininen J (2010) Species turnover along abiotic and biotic gradients: patterns in space equal patterns in time? Bioscience 60:433–439
Koleff P, Gaston KJ, Lennon JJ (2003) Measuring beta diversity for presence-absence data. J Anim Ecol 72:367–382
Biology-forums (2016) Species turnover. http://biology-forums.com/definitions/index.php/Species_turnover
Shirkhorshidi AS, Aghabozorgi S, Wah TY (2015) A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS One 10:e0144059
Emran SM, Ye N (2002) Robustness of Chi-square and Canberra distance metrics for computer intrusion detection. Qual Reliab Eng Int 18:19–28
Giuseppe Jurman SR, Visintainer R, Furlanello C (2009) Canberra distance on ranked lists. Advances in ranking–NIPS 09 workshop, pp 22–27
Hennig C, Hausdorf B (2006) Design of dissimilarity measures: a new dissimilarity between species distribution areas. In: Batagelj V, Bock H-H, Ferligoj A, Žiberna A (eds) Stud class data anal. Springer, Berlin, Heidelberg, pp 29–37
Anderson MJ, Millar RB (2004) Spatial variation and effects of habitat on temperate reef fish assemblages in northeastern New Zealand. J Exp Mar Biol Ecol 305:191–221
Horn HS (1966) Measurement of overlap in comparative ecological studies. Am Nat 100:419
Anderson MJ, Ellingsen KE, McArdle BH (2006) Multivariate dispersion as a measure of beta diversity. Ecol Lett 9:683–693
Cao Y, Williams WP, Bark AW (1997) Similarity measure bias in river benthic Aufwuchs community analysis. Water Environ Res 69:95–106
Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71:8228–8235
Clarke KR, Somerfield PJ, Chapman MG (2006) On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray-Curtis coefficient for denuded assemblages. J Exp Mar Biol Ecol 330:55–80
Fukuyama J, McMurdie PJ, Dethlefsen L, Relman DA, Holmes S (2012) Comparisons of distance methods for combining covariates and abundances in microbiome studies. Pac Symp Biocomput:213–224
Lozupone CA, Knight R (2007) Global patterns in bacterial diversity. Proc Natl Acad Sci U S A 104:11436–11440
Schloss PD (2008) Evaluating different approaches that test whether microbial communities have the same structure. ISME J 2:265–275
Ives AR, Helmus MR (2010) Phylogenetic metrics of community similarity. Am Nat 176:E128–E142
McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82:290–297
Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, Zhou JJ, Ringel Y, Li HZ, Wu MC (2015) Testing in microbiome-profiling studies with MiRKAT, the Microbiome regression-based kernel association test. Am J Hum Genet 96:797–807
Zhu X, Wang J, Peng B, Shete S (2016) Empirical estimation of sequencing error rates using smoothing splines. BMC Bioinformatics 17:177
Scealy JL, Welsh AH (2011) Regression for compositional data by using distributions defined on the hypersphere. J R Stat Soc B 73:351–375
Kent JT (1982) The Fisher-Bingham distribution on the sphere. J R Stat Soc B 44:71–80
Aitchison J (1982) The statistical-analysis of compositional data. J R Stat Soc B 44:139–177
Shi PX, Zhang AR, Li HZ (2016) Regression analysis for microbiome compositional data. Ann Appl Stat 10:1019–1040
Fisher CK, Mehta P (2014) Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS One 9:e102451
Chen EZ, Li HZ (2016) A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics 32:2611–2617
Gevers D, Knight R, Petrosino JF, Huang K, McGuire AL, Birren BW, Nelson KE, White O, Methe BA, Huttenhower C (2012) The Human Microbiome Project: a community resource for the healthy human microbiome. PLoS Biol 10:e1001377
Edgar RC (2016) UNCROSS: filtering of high-frequency cross-talk in 16S amplicon reads. doi:10.1101/088666
McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P (2012) An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072
R Core Team (2016) R: a language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org/
van den Boogaart KG, Tolosana R, Bren M (2014) compositions: Compositional Data Analysis. R Package Version 1:40–1. http://CRAN.R-project.org/package=compositions
Oksanen J, Guillaume Blanchet F, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Henry M, Stevens H (2016) vegan: Community Ecology Package. R package version 2.3-5. http://CRAN.R-project.org/package=vegan
Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290
Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG (2013) Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 10:57–59
Walters WA, Caporaso JG, Lauber CL, Berg-Lyons D, Fierer N, Knight R (2011) PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics 27:1159–1161
Acknowledgments
Funding support for the Study of Foregut Microbiome in Development of Esophageal Adenocarcinoma was provided by the National Cancer Institute (UH3CA140233) through the Human Microbiome Project of the NIH Roadmap Initiative. Data for the Foregut Microbiome study were provided by Zhiheng Pei, MD, PhD, on behalf of his collaborators at New York University School of Medicine, the J. Craig Venter Institute, and Lawrence Berkeley National Laboratory.
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Zhu, X., Wang, J., Reyes-Gibby, C., Shete, S. (2017). Processing and Analyzing Human Microbiome Data. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_31
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DOI: https://doi.org/10.1007/978-1-4939-7274-6_31
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