Estimation of relationships between chemical substructures and antibiotic resistance-related gene expression in bacteria: Adapting a canonical correlation analysis for small sample data of gathered features using consensus clustering

Abstract

The emergence of antibiotic-resistant bacteria is a serious public health concern. Understanding the relationships between antibiotic compounds and phenotypic changes related to the acquisition of resistance is important to estimate the effective characteristics of drug seeds. It is important to analyze the relationships between phenotypic changes and compound structures; hence, we performed a canonical correlation analysis (CCA) for high dimensional phenotypic and compound structure datasets. For the CCA, the required sample number must be larger than the feature number; however, collecting a large amount of data can sometimes be difficult. Thus, we combined consensus clustering to gather and reduce features. The CCA was performed using the clustered features, and it revealed relationships between the features of chemical substructures and the expression level of genes related to several types of antibiotic resistance.