Recognition of differential culture conditions with dimensional reduction approach

Abstract

Microorganisms constantly modify their gene expression and metabolic profiles in response to alterations in their surrounding environment. Monitoring these changes is crucial for regulating microbial production of substances. However, it remains challenging to identify differential culture conditions through the extraction of differentially expressed genes and clustering of gene expression profiles. In this study, we employed a dimensionality reduction technique for yeast gene expression data obtained under multiple culture conditions to visualize discrepancies among culture conditions. Our findings indicate that this approach is effective in identifying multiple culture conditions.