In this study, we conducted a Raman imaging study on leukemia cells derived from 19 patients , each presenting one of nine distinct leukemia subtypes. We collected high spatial resolution (up to 355nm) maps at the single-cell level, enabling the detection and recognition of specific molecules or subcellular structures that could potentially identify specific subtypes. The final dataset consisted of 319 maps and over 1.3 million spectra necessitating the development and optimization of suitable approaches to generate high-quality images and identify distinct morphological and biochemical features across different subtypes. To achieve this, we optimized a single-step global clustering approach that involved simultaneous cluster analysis of all cells in the entire dataset. After a semi-automated pre-processing pipeline, the entire dataset was thoroughly investigated at the single-pixel level by a single global cluster analysis step, thus automatically providing "virtually stained" cell images and, in parallel, the distribution of relevant biomolecules over the different cells and different subtypes. Furthermore, data emerging from the global cluster analysis were then processed by multivariate analysis with classification aims. Overall, our results underscore the potential of employing a global clustering approach for semi-automated analysis of an extensive dataset of Raman maps. Concurrently, we present the most extensive characterization of leukemia subtypes to date using Raman microscopy or Raman imaging methods.