Systematic Statistical Analysis of Microbial Data from Dilution Series

In microbial studies, samples are often treated under different experimental conditions and then tested for microbial survival. A technique, dating back to the 1880s, consists of diluting the samples several times and incubating each dilution to verify the existence of microbial colony-forming units or CFU’s, seen by the naked eye. The main problem in the dilution series data analysis is the uncertainty quantification of the simple point estimate of the original number of CFU’s in the sample (i.e., at dilution zero). Common approaches such as log-normal or Poisson models do not seem to handle well extreme cases with low or high counts, among other issues. We build a novel binomial model, based on the actual design of the experimental procedure including the dilution series. For repetitions, we construct a hierarchical model for experimental results from a single laboratory and in turn a higher hierarchy for inter-laboratory analyses. Results seem promising, with a systematic treatment of all data cases, including zeros, censored data, repetitions, intra- and inter-laboratory studies. Using a Bayesian approach, a robust and efficient MCMC method is used to analyze several real data sets.