SPADE is a novel computational method for analyzing high-dimensional cytometry data to reveal cellular hierarchies. It was developed to overcome limitations of traditional flow cytometry analysis, which is often subjective and labor-intensive. SPADE organizes cells into a branched tree structure without requiring prior knowledge of cellular order, enabling the identification of cell types and functional markers. The method was tested on mouse and human bone marrow data, where it successfully identified known hematopoietic hierarchies and revealed functionally distinct cell populations, such as natural killer (NK) cells, without using NK-specific parameters. SPADE also mapped intracellular signal activation across hematopoietic development and provided a visualization of cellular heterogeneity. The algorithm consists of four steps: density-dependent downsampling, agglomerative clustering, minimum spanning tree construction, and upsampling. It was validated against traditional gating analysis, showing consistency in identifying biologically relevant populations. SPADE's ability to handle high-dimensional data and its scalability make it a powerful tool for analyzing complex biological systems. The method was implemented in MATLAB and is available on the Nature Biotechnology website. The study highlights SPADE's potential to advance our understanding of cellular heterogeneity and functional markers in response to perturbations.SPADE is a novel computational method for analyzing high-dimensional cytometry data to reveal cellular hierarchies. It was developed to overcome limitations of traditional flow cytometry analysis, which is often subjective and labor-intensive. SPADE organizes cells into a branched tree structure without requiring prior knowledge of cellular order, enabling the identification of cell types and functional markers. The method was tested on mouse and human bone marrow data, where it successfully identified known hematopoietic hierarchies and revealed functionally distinct cell populations, such as natural killer (NK) cells, without using NK-specific parameters. SPADE also mapped intracellular signal activation across hematopoietic development and provided a visualization of cellular heterogeneity. The algorithm consists of four steps: density-dependent downsampling, agglomerative clustering, minimum spanning tree construction, and upsampling. It was validated against traditional gating analysis, showing consistency in identifying biologically relevant populations. SPADE's ability to handle high-dimensional data and its scalability make it a powerful tool for analyzing complex biological systems. The method was implemented in MATLAB and is available on the Nature Biotechnology website. The study highlights SPADE's potential to advance our understanding of cellular heterogeneity and functional markers in response to perturbations.