Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in...

Although flow cytometry produces reliable results, the data processing from gating to fingerprinting is prone to subjective bias. Here, we integrated autogating with Automated Machine Learning in flow cytometry to enhance the classification of bacterial phenotypes. We analyzed six bacterial strains prevalent in the soil and groundwater- Bacillus subtilis , Burkholderia thailandensis , Corynebacterium glutamicum , Escherichia coli , Pseudomonas putida , and Pseudomonas stutzeri . Using the H2O-AutoML framework, we applied gradient-boosting machine (GBM) models to classify bacteria across different metabolic phases. Our results demonstrated an overall classification accuracy of 82.34% for GBM. Notably, accuracy varied across metabolic phases, with the highest observed during the late log (88.06%), lag (88.43%), and early log phases (89.37%), whereas the stationary phase showed a slightly lower accuracy of 80.73%. P. stutzeri exhibited consistently high sensitivity and specificity across all the phases, which indicated that it was the most distinctly identifiable strain. In contrast, E. coli showed low sensitivity, particularly in the stationary phase, which indicated challenges in its classification. Overall, this study with incorporating autogating and the AutoML framework, substantially reduces subjective biases and enhances the reproducibility and accuracy of microbial classification. Our methodology offers a robust framework for microbial classification in flow cytometric analysis, paving the way for more precise and comprehensive analyses of microbial ecology.