Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible

Abstract

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 75

1. 1.

   Shendure J, Lieberman Aiden E (2012 ) The expanding scope of DNA sequencing. Nature Biotechnology 30: 1084–1094.

   Not in our library10.1038/nbt.2421

2. 2.

   Shendure J, Ji H (2008 ) Next-generation DNA sequencing. Nature Biotechnology 26: 1135–1145.

   Not in our library10.1038/nbt1486

3. 3.

   Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008 ) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5: 621–628.

   Not in our library10.1038/nmeth.1226

4. 4.

   Pace NR (1997 ) A molecular view of microbial diversity and the biosphere. Science 276: 734–740.

   Not in our library10.1126/science.276.5313.734

5. 5.

   Wilson KH, Wilson WJ, Radosevich JL, DeSantis TZ, Viswanathan VS, et al (2002 ) High-Density Microarray of Small-Subunit Ribosomal DNA Probes. Appl Environ Microbiol 68: 2535–2541.

   Not in our library10.1128/aem.68.5.2535-2541.2002

6. 6.
   Exploring Microbial Diversity and Taxonomy Using SSU rRNA Hypervariable Tag Sequencing · 2008

   Corrected
7. 7.

   Riesenfeld CS, Schloss PD, Handelsman J (2004 ) Metagenomics: genomic analysis of microbial communities. Annual Review of Genetics 38: 525–552.

   Not in our library10.1146/annurev.genet.38.072902.091216

8. 8.

   Allison DB, Cui X, Page GP, Sabripour M (2006 ) Microarray Data Analysis: from Disarray to Consolidation and Consensus. Nature Reviews Genetics 7: 55–65.

   Not in our library10.1038/nrg1749

9. 9.

   Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008 ) RNA-Seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research 18: 1509–1517.

   Not in our library10.1101/gr.079558.108

10. 10.

    Lu J, Tomfohr JK, Kepler TB (2005 ) Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach. BMC Bioinformatics 6: 165.

    Not in our library10.1186/1471-2105-6-165

11. 11.

    Robinson MD, Smyth GK (2007 ) Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics (Oxford, England) 9: 321–332.

    Not in our library10.1093/biostatistics/kxm030

12. 12.

    Cameron AC, Trivedi P (2013) Regression analysis of count data, volume 53. Cambridge University Press.

    Not in our library

13. 13.
    Differential expression analysis for sequence count data · 2010

    No notice
14. 14.

    Yu D, Huber W, Vitek O (2013 ) Shrinkage estimation of dispersion in Negative Binomial models for RNASeq experiments with small sample size. Bioinformatics (Oxford, England) 29: 1275–1282.

    Not in our library10.1093/bioinformatics/btt143

15. 15.

    Di Bella JM, Bao Y, Gloor GB, Burton JP, Reid G (2013 ) High throughput sequencing methods and analysis for microbiome research. Journal of Microbiological Methods 95: 401–414.

    Not in our library10.1016/j.mimet.2013.08.011

16. 16.
    Computational meta'omics for microbial community studies · 2013

    No notice
17. 17.

    Navas-Molina JA, Peralta-Sánchez JM, González A, McMurdie PJ, Vázquez-Baeza Y, et al (2013 ) Advancing Our Understanding of the Human Microbiome Using QIIME. Methods in Enzymology 531: 371–444.

    Not in our library10.1016/b978-0-12-407863-5.00019-8

18. 18.

    Hughes JB, Hellmann JJ (2005 ) The application of rarefaction techniques to molecular inventories of microbial diversity. Methods in Enzymology 397: 292–308.

    Not in our library10.1016/s0076-6879(05)97017-1

19. 19.
    A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets · 2013

    No notice
20. 20.

    Sanders HL (1968 ) Marine benthic diversity: A comparative study. The American Naturalist 102: 243–282.

    Not in our library

21. 21.

    Gotelli NJ, Colwell RK (2001 ) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters 4: 379–391.

    Not in our library

22. 22.

    Mao CX, Colwell RK (2005 ) Estimation of Species Richness: Mixture Models, the Role of Rare Species, and Inferential Challenges. Ecology 86: 1143–1153.

    Not in our library

23. 23.

    Lozupone C, Knight R (2005 ) UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 71: 8228–8235.

    Not in our library10.1128/aem.71.12.8228-8235.2005

24. 24.

    Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011 ) UniFrac: an effective distance metric for microbial community comparison. The ISME Journal 5: 169–172.

    Not in our library10.1038/ismej.2010.133

25. 25.

    Hamady M, Walker JJ, Harris JK, Gold NJ, Knight R (2008 ) Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nature Methods 5: 235–237.

    Not in our library10.1038/nmeth.1184

26. 26.

    Liu Z, DeSantis TZ, Andersen GL, Knight R (2008 ) Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers. Nucleic Acids Research 36: e120.

    Not in our library10.1093/nar/gkn491

27. 27.

    Hamady M, Lozupone C, Knight R (2010 ) Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. The ISME Journal 4: 17–27.

    Not in our library10.1038/ismej.2009.97

28. 28.

    Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, et al (2012 ) Human gut microbiome viewed across age and geography. Nature 486: 222–227.

    Not in our library10.1038/nature11053

29. 29.

    Caporaso J, Kuczynski J, Stombaugh J, Bittinger K, Bushman F, et al (2010 ) QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7: 335–336.

    Not in our library10.1038/nmeth.f.303

30. 30.

    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, et al (2009 ) Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Applied and Environmental Microbiology 75: 7537–7541.

    Not in our library10.1128/aem.01541-09

31. 31.

    Gilbert JA, Field D, Swift P, Newbold L, Oliver A, et al (2009 ) The seasonal structure of microbial communities in the Western English Channel. Environmental Microbiology 11: 3132–3139.

    Not in our library10.1111/j.1462-2920.2009.02017.x

32. 32.
    phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data · 2013

    No notice
33. 33.
    Disordered Microbial Communities in the Upper Respiratory Tract of Cigarette Smokers · 2010

    No notice
34. 34.
    The Effects of Circumcision on the Penis Microbiome · 2010

    No notice
35. 35.

    Kembel SW, Jones E, Kline J, Northcutt D, Stenson J, et al (2012 ) Architectural design influences the diversity and structure of the built environment microbiome. The ISME Journal 6: 1469–1479.

    Not in our library10.1038/ismej.2011.211

36. 36.

    Flores GE, Bates ST, Caporaso JG, Lauber CL, Leff JW, et al (2013 ) Diversity, distribution and sources of bacteria in residential kitchens. Environmental Microbiology 15: 588–596.

    Not in our library10.1111/1462-2920.12036

37. 37.
    Reduced Incidence of Prevotella and Other Fermenters in Intestinal Microflora of Autistic Children · 2013

    No notice
38. 38.
    Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples · 2012

    No notice
39. 39.
    Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples · 2009

    No notice
40. 40.

    Paulson JN, Stine OC, Bravo HC, Pop M (2013 ) Differential abundance analysis for microbial marker-gene surveys. Nature Methods 10: 1200–1202.

    Not in our library10.1038/nmeth.2658

Cited by papers we hold 111

• Networks as tools for defining emergent properties of microbiomes and their stability · 2024
  No notice
• Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses · 2024
  No notice
• Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region · 2022
  Not checked
• Tree Size Drives Diversity and Community Structure of Microbial Communities on the Bark of Beech (Fagus sylvatica) · 2022
  Not checked
• Grapevine treatment with bagasse vermicompost changes the microbiome of Albariño must and wine and improves wine quality · 2022
  Not checked
• Investigating differential abundance methods in microbiome data: A benchmark study. · 2022
  Not checked
• A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions · 2022
  No notice
• Microbiome differential abundance methods produce different results across 38 datasets · 2022
  Corrected
• Association of Broiler Litter Microbiome Composition and Campylobacter Isolation · 2021
  Not checked
• Clinical health issues, reproductive hormones, and metabolic hormones associated with gut microbiome structure in African and Asian elephants · 2021
  Not checked
• Maternal consumption of artificially sweetened beverages during pregnancy is associated with infant gut microbiota and metabolic modifications and increased infant body mass index · 2021
  Not checked
• You are more than what you eat: potentially adaptive enrichment of microbiome functions across bat dietary niches · 2021
  Not checked
• Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities · 2021
  No notice
• Multivariable association discovery in population-scale meta-omics studies · 2021
  No notice
• Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas · 2021
  No notice
• Conserved and reproducible bacterial communities associate with extraradical hyphae of arbuscular mycorrhizal fungi · 2021
  No notice
• Open challenges for microbial network construction and analysis · 2021
  No notice
• A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities · 2020
  Corrected
• Gut Microbiome Profiles Are Associated With Type 2 Diabetes in Urban Africans · 2020
  No notice
• Quorum Sensing Signals Alter in vitro Soil Virus Abundance and Bacterial Community Composition · 2020
  Not checked

and 91 more

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.