Global clustering analysis and virtual staining of leukemia cells. Patients diagnosed with one of the nine distinct leukemia subtypes, six AMLs (i.e., AML M0, M1, M2, M3, M5a, M6) and three ALLs (ALL B Ph-, ALL B Ph+, ALL T) (see Table ), were included in this study. Overall, 319 leukemia cells from bone marrow were mapped with high resolution by a homebuilt Raman confocal microscope using a pixel step size down to 156 nm and pixel dwell time of 100 ms. Aiming to provide final high-quality cell images reproducing those used by clinicians after specific clinical staining (i.e., May-Grünwald and Giemsa or hematoxylin and eosin protocols) we optimized a global k-means-based clustering analysis on the entire preprocessed final dataset (667,771 spectra) thus being able to recognize the same cellular components distributed over different leukemia subtypes. Considering that clustering approaches may easily recognize spectral features and spectral intensities not directly associated with biological/pathological portraits, due to technical variability, an accurate 9-step processing pipeline (Fig. ) has been automatically and simultaneously applied before global k-means-based clustering analysis. Fig. shows representative pseudo-stained Raman images of cells from nine different leukemia subtypes, accompanied by the spectra representing each cluster (centroids). Fig. shows the entire dataset including 315 cells (four cells were removed due to artifacts), pseudo-colored using the same approach. The images reported in the figure were obtained by performing a 17-level global k-means-based clustering. The selection of the best clustering level has been performed considering two main decisive criteria. The first criterion was to correctly detect the spectra of cellular components known to be present in leukemia cells: cytoplasm, nucleus, carotenoids, MPO, and HB. This straightforwardly follows from previously published data 20 and from the supervised identification of these components at single pixel level (see Fig. ). The second criterion was to represent the spatial distribution and degree of concentration of specific cellular features in the cells, thus resembling the morphological features visible in clinically stained samples (Fig. ). For example, the use of 5-or 10-level clustering analysis (Fig. and) resulted in the incorrect assignment of the carotenoid cluster(s) to cells (e.g., AML M0) in which carotenoids features are rarely detected by manual spectral analysis at single pixel level, and this was confirmed when the clustering level was increased. In addition, images resulting from a 5-or 10-level clustering analysis showed reduced intracellular complexity, missed separation between cellular compartments, or missed relative abundance of lineagespecific components.