Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging

A detailed comparison of six multivariate algorithms is presented to analyze and generate Raman microscopic images that consist of a large number of individual spectra. This includes the segmentation algorithms for hierarchical cluster analysis, fuzzy C-means cluster analysis, and k-means cluster analysis and the spectral unmixing techniques for principal component analysis and vertex component analysis (VCA). All algorithms are reviewed and compared. Furthermore, comparisons are made to the new approach N-FINDR. In contrast to the related VCA approach, the used implementation of N-FINDR searches for the original input spectrum from the non-dimension reduced input matrix and sets it as the endmember signature. The algorithms were applied to hyperspectral data from a Raman image of a single cell. This data set was acquired by collecting individual spectra in a raster pattern using a 0.5-mu m step size via a commercial Raman microspectrometer. The results were also compared with a fluorescence staining of the cell including its mitochondrial distribution. The ability of each algorithm to extract chemical and spatial information of subcellular components in the cell is discussed together with advantages and disadvantages.