Data analysis and R demonstration for application of BIOLOG microarrays technique in microbial ecology study

In the study of microbial ecology, there have been increasing concerns about community patterns and their relationship with environmental factors. Selecting appropriate statistical methods not only provides a better understanding of the microbial community structure and pattern but also clarifies the effects of environmental factors on the microbial community. Based on practical examples, this paper summarized the statistical methods for BIOLOG data in microbial ecology study, including data input, indicator indices calculation, unconstrained ordination, constrained ordination, clustering analysis, environmental factor fitting, Mantel test, and so on. The statistical results were discussed based on study purposes and ecological theories, and the applicability of those methods was also evaluated. All analysis was conducted with R programming. The results show that BIOLOG microarrays techniques effectively reveal the physiological profiles of the microbial community. Moreover, this technique can be appropriately used to analyze the relationship between microbial profiles and their environmental factors. Here, we should recognize that the pre-inspections are required, as BIOLOG data maybe not in a normal distribution. For ordination analysis, some other applications of distance-based ordination methods are safer than Principal Component Analysis(PCA), which should be given a second thought. The priority should be given to the ordination analysis based on the distance matrix rather than PCA. Furthermore, R programming facilitates the BIOLOG data manipulation. The study can help researchers choose proper analysis in microbial ecology study and increase the efficiency of data manipulation.