Hierarchical Cluster Analysis (HCA) of Microorganisms: An Assessment of Algorithms for Resonance Raman Spectra

Abstract

Resonance Raman microspectroscopy in combination with hierarchical cluster analysis (HCA) is one of the most promising tools for the rapid examination of complex biological and medical samples. HCA is a ready, computerized tool for examining large sets of data for common characteristics, and a multitude of algorithms for this purpose have been developed over the years. However, resonance Raman spectra obtained from complex biological samples may originate from different chromophores as well as from a common chromophore found in different host environments, i.e., bacteria. Therefore, algorithms applied to resonance Raman spectra must handle data of high intrinsic similarity, i.e., spectra originating from a common chromophore, and data with highly dissimilar features, i.e., spectra from different chromophores, in the same unsupervised analysis. We examined the performance of eight widely used algorithms for hierarchical cluster analysis in clustering resonance Raman spectra of bacteria: Single-Linkage (Nearest-Neighbor), Complete-Linkage (Farthest-Neighbor), Average-Linkage, Weighted-Average-Linkage, Centroid, Median, and the Ward algorithm. Algorithm performance was evaluated by comparing the results of clustering a set of high-quality reference spectra with the results obtained when clustering a set of spectra recorded from single cells. References were formed by averaging 100 spectra of individual cells. While all algorithms returned highly similar results when clustering the reference spectra, their performance differed significantly when applied to single spectra. The best-performing algorithm, Weighted-Average-Linkage, correctly grouped single spectra with a reliability of above 95% while the spectral distances between the clusters deviated less than 10% from the results obtained with reference spectra. In contrast, the algorithm performing worst showed no similarity to the reference clustering at all. The widely used Ward algorithm deviated up to 30% from the reference in the spectral distances and returned a different spectral relation between bacteria expressing the same chromophore.