In this study, starting from Raman imaging data, an optimized single-step global clustering approach applied to a dataset of 315 cells from patients affected by nine different leukemia subtypes facilitated the automatic generation of high-quality false-color images and offered a direct depiction of the intricate biochemical and morphological features of cells. Using a semi-automatic approach, we virtually stained each cluster to replicate images commonly examined by pathologists. Beyond highlighting the cytoplasm (pink) and nucleus (violet) contrast, provided by MGG staining methods, we introduced specific color representations for carotenoids, MPO, and HB, typically undetected by standard staining procedures, potentially serving as valuable subtype-specific hallmarks.