Microbial community resemblance methods differ in their ability to detect biologically relevant patterns.
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.
Kuczynski
Justin
J
Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Colorado, USA.
Liu
Zongzhi
Z
Lozupone
Catherine
C
McDonald
Daniel
D
Fierer
Noah
N
Knight
Rob
R
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
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