SPADE – Data Analysis and Visualization Software for FACS and other Multidimensional Data

Stanford researchers have developed a versatile computational approach for easily visualizing and analyzing multidimensional molecular data, such as flow cytometry data. This software, called SPADE (spanning-tree progression analysis of density-normalized events), graphically connects clusters of multidimensional data in a progressive structure. It is complementary to existing approaches for analyzing cytometry data by enabling analysis of differentiation and branching even if the user has no prior knowledge of the underlying cell population. For FACS data, the algorithm can perform gating analysis, discover lineages of cellular progression and differentiation, and identify uncharacterized cellular features that are relevant to progression of cells. The invention can also be generalized for applications to other multidimensional datasets, including microarray data and in-vivo medical images.

Final SPADE output of a colored tree showing cellular heterogeneity. This tree was produced from a simulated two-parameter flow cytometry data set, with one rare population and three abundant populations. The nodes are colored by the median intensities of protein markers of cells in each node, allowing visualization of the behaviors of the two markers across the entire heterogeneous cell population.

Stage of Research
The software is implemented in MATLAB and the inventors have demonstrated the utility of SPADE with cytometry data from mouse and human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes. They have also used SPADE to analyze results from simultaneous monitoring of 18 molecular markers to provide system-wide views of immune signaling in human hematopoiesis.