Data from: Novel R pipeline for analyzing Biolog phenotypic microarray data

Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells’ respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.

由Biolog表型微阵列（Biolog Phenotype MicroArrays）产生的数据，是对细胞在不同底物上的呼吸活性进行的纵向测量。本研究提出一套三步式分析流程，用于表型微阵列数据的分析，其包含分组、归一化与效应识别三类全新方法。分组与归一化是表型微阵列分析中的两类经典问题：前者指将细菌的响应划分为活性与非活性两类，后者则指从实验数据中移除系统误差。本研究通过引入一项重要假设拓展了现有解决方案：活性与非活性细菌的代谢模式完全不同，因此应分开进行处理。而效应识别模块则为检测不同实验条件（例如菌株与温度的不同组合）下的呼吸活性差异提供了全新视角，其不仅可评估主效应，还可分析各效应间的交互作用。在效应识别环节，多级数据可通过贝叶斯框架下的层级模型得到高效处理。本分析流程已通过针对12块表型微孔板的数据集完成测试，该数据集使用的菌株为小肠结肠炎耶尔森菌（Yersinia enterocolitica）。本流程基于R语言开发，依托opm R包实现，且可免费用于科研用途。