Global cluster analysis and virtual staining. The k-means algorithm was then simultaneously applied to the entire dataset composed of 315 cells and 667,771 pre-processed spectra. A weighted k-means was employed giving each leukemia subtype the same importance. After screening different clustering levels, the optimal number of clusters (17) was chosen to correctly identify and distinguish the main biochemical components found in leukemia cells (see "Results and Discussion"). The clusters were grouped according to the presence of Raman features associated with specific cellular components: cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO) and hemoglobin (HB) (Fig. ). Falsecolor images were produced by assigning a color to each cellular component and a different color saturation level to clusters within the same cellular component group according to the peak intensities at group-specific Raman shifts (see asterisks in Fig. ).