Statistical recipe for quantifying microbial functional diversity from EcoPlate metabolic profiling

Takeshi Miki, Taichi Yokokawa, Po Ju Ke, I. Fang Hsieh, Chih hao Hsieh, Tomonori Kume, Kinuyo Yoneya, Kazuaki Matsui

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


EcoPlate quantifies the ability of a microbial community to utilize 31 distinct carbon substrates, by monitoring color development of microplate wells during incubation. Well color patterns represent metabolic profiles. Previous studies typically used color patterns representing average values of three technical replicates on the final day of the incubation and did not consider substrate chemical diversity. However, color fluctuates during incubation and color varies between replicates, undermining statistical power to distinguish differences among samples in microbial functional composition and diversity. Therefore, we developed a protocol to improve statistical power with two approaches. First, we optimized data treatment for color development during incubation and technical replicates. Second, we incorporated chemical structural information for the 31 carbon substrates into the computation. Our framework implemented as the protocol in the R environment is able to compare the statistical power among different calculation methods. When we applied it to data from aquatic microcosm and forest soil systems, we observed substantial improvement in statistical power when we incorporated temporal patterns during incubation instead of using only endpoint data. Using maximum or minimum values of technical replicates also sometimes gave better results than averages. Incorporating chemical structural information based on fuzzy set theory could improve statistical power but only when relative color density information was considered; it was not seen when the pattern was first binarized into the presence or absence of metabolic activity. Finally, we discuss research directions to improve these approaches and offer some practical considerations for applying our methods to other datasets.

Original languageEnglish
Pages (from-to)249-260
Number of pages12
JournalEcological Research
Issue number1
Publication statusPublished - Jan 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics


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