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.
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The authors gratefully acknowledge funding from National Cancer Institute Integrative Cancer Biology Program (ICBP), grants U56CA112973 and U54CA149145 to S.K.P. A Damon Runyon Cancer Research Foundation Fellowship supports S.C.B. National Science Foundation Graduate Research Fellowship and Stanford DARE Fellowship support K.D.G. This work is also supported by US National Institutes of Health grants U19 AI057229, P01 CA034233, HHSN272200700038C, 1R01CA130826, 5U54 CA143907, RB2-01592, PN2EY018228, N01-HV-00242, HEALTH.2010.1.2-1 (European Commission), as well as the California Institute for Regenerative Medicine (DR1-01477) to G.P.N.
A patent for the SPADE algorithm has been applied for on behalf of Stanford University.
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Qiu, P., Simonds, E., Bendall, S. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29, 886–891 (2011). https://doi.org/10.1038/nbt.1991
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