Towards high-accuracy bacterial taxonomy identification using phenotypic single-cell Raman spectroscopy data.

ABSTRACT: Single-cell Raman Spectroscopy (SCRS) emerges as a promising tool for single-cell phenotyping in environmental ecological studies, offering non-intrusive, high-resolution, and high-throughput capabilities. In this study, we obtained a large and the first comprehensive SCRS dataset that captured phenotypic variations with cell growth status for 36 microbial strains, and we compared and optimized analysis techniques and classifiers for SCRS-based taxonomy identification. First, we benchmarked five dimensionality reduction (DR) methods, 10 classifiers, and the impact of cell growth variances using a SCRS dataset with both taxonomy and cellular growth stage labels. Unsupervised DR methods and non-neural network classifiers are recommended for at a balance between accuracy and time efficiency,

SUBMITTER: Li G 

PROVIDER: S-EPMC11910137 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature