Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

Top Cited Papers
Open Access
Publisher Website
Google Scholar
Abstract
New instruments can measure the presence of >30 molecular markers for massive numbers of single cells, but data analysis algorithms have lagged behind. Qiu et al. describe an approach called SPADE for recovering cellular hierarchies from mass or flow cytometry data. The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.