title: Microbial community resemblance methods differ in their ability to detect biologically relevant patterns.
abstract: High-throughput sequencing methods enable characterization of microbial communities in a wide range of environments on an unprecedented scale. However, insight into microbial community composition is limited by our ability to detect patterns in this flood of sequences. Here we compare the performance of 51 analysis techniques using real and simulated bacterial 16S rRNA pyrosequencing datasets containing either clustered samples or samples arrayed across environmental gradients. We found that many diversity patterns were evident with severely undersampled communities and that methods varied widely in their ability to detect gradients and clusters. Chi-squared distances and Pearson correlation distances performed especially well for detecting gradients, whereas Gower and Canberra distances performed especially well for detecting clusters. These results also provide a basis for understanding tradeoffs between number of samples and depth of coverage, tradeoffs that are important to consider when designing studies to characterize microbial communities.
authors.affiliation: Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Colorado, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Kuczynski
authors.forename: Zongzhi
authors.initials: Z
authors.lastname: Liu
authors.forename: Catherine
authors.initials: C
authors.lastname: Lozupone
authors.forename: Daniel
authors.initials: D
authors.lastname: McDonald
authors.forename: Noah
authors.initials: N
authors.lastname: Fierer
authors.forename: Rob
authors.initials: R
authors.lastname: Knight
mesh_terms: D001419:Bacteria / Q000145:classification* / Q000235:genetics; D016000:Cluster Analysis; D003198:Computer Simulation; D017753:Ecosystem; D008828:Microbiological Techniques / Q000379:methods* / Q000706:statistics & numerical data; D025341:Principal Component Analysis; D012988:Soil Microbiology
references.citation: Genome Biol. 2010;11(5):210
references.pmid: 20441597
references.citation: Nat Rev Microbiol. 2008 Oct;6(10):776-88
references.pmid: 18794915
references.citation: Proc Natl Acad Sci U S A. 2010 Apr 6;107(14):6477-81
references.pmid: 20231444
references.citation: ISME J. 2010 Jan;4(1):17-27
references.pmid: 19710709
references.citation: Nat Methods. 2008 Mar;5(3):235-7
references.pmid: 18264105
references.citation: Nature. 2009 Jan 22;457(7228):480-4
references.pmid: 19043404
references.citation: FEMS Microbiol Ecol. 2007 Nov;62(2):142-60
references.pmid: 17892477
references.citation: Annu Rev Microbiol. 1977;31:107-33
references.pmid: 334036
references.citation: Nature. 2005 Sep 15;437(7057):376-80
references.pmid: 16056220
references.citation: Appl Environ Microbiol. 2009 Aug;75(15):5111-20
references.pmid: 19502440
references.citation: Nature. 2007 Oct 18;449(7164):804-10
references.pmid: 17943116
references.citation: Proc Natl Acad Sci U S A. 2007 Aug 21;104(34):13780-5
references.pmid: 17699621
references.citation: Proc Natl Acad Sci U S A. 2008 Nov 18;105(46):17994-9
references.pmid: 19004758
references.citation: Genome Biol. 2007;8(8):R171
references.pmid: 17708774
references.citation: Science. 2008 Jun 20;320(5883):1647-51
references.pmid: 18497261
references.citation: Genome Res. 2009 Jul;19(7):1141-52
references.pmid: 19383763
references.citation: Oecologia. 2001 Oct;129(2):271-280
references.pmid: 28547606
references.citation: Science. 2009 Dec 18;326(5960):1694-7
references.pmid: 19892944
references.citation: Proc Natl Acad Sci U S A. 2009 Jul 7;106(27):11276-81
references.pmid: 19549860
references.citation: Science. 2005 Jun 10;308(5728):1635-8
references.pmid: 15831718
references.citation: ISME J. 2010 Oct;4(10):1340-51
references.pmid: 20445636
references.citation: Annu Rev Microbiol. 2003;57:369-94
references.pmid: 14527284
references.citation: FEMS Microbiol Rev. 2008 Jul;32(4):557-78
references.pmid: 18435746