论文标题
稀释系列微生物数据的系统统计分析
Systematic statistical analysis of microbial data from dilution series
论文作者
论文摘要
在微生物研究中，通常在不同的实验条件下处理样品，然后测试微生物存活率。一种可以追溯到1880年代的技术包括多次稀释样品并孵化每种稀释，以验证肉眼看到的微生物菌落形成单元或CFU的存在。稀释系列数据分析中的主要问题是样品中CFU原始数量的简单点估计值的不确定性量化（即稀释为零）。诸如对数正态或泊松模型之类的常见方法似乎无法处理低计数或高计数的良好极端案例。我们基于包括稀释系列的实验过程的实际设计构建了一种新型的二项式模型。对于重复，我们构建了一个层次模型，以从单个实验室的实验结果，进而为LILAB间分析提供更高的层次结构。结果似乎很有希望，对所有数据案例进行了系统的处理，包括零，审查数据，重复，内部和实验室间研究。使用贝叶斯方法，使用强大而有效的MCMC方法来分析几个实际数据集。
In microbial studies, samples are often treated under different experimental conditions and then tested for microbial survival. A technique, dating back to the 1880's, 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 lab and in turn a higher hierarchy for inter-lab 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.
