References

Core SPADE publications

1. Qiu, P., Simonds, E.F., Bendall, S.C., Gibbs, K.D. Jr., Bruggner, R.V., Linderman, M.D., Sachs, K., Nolan, G.P., & Plevritis, S.K. (2011). “Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE.” Nature Biotechnology, 29(10), 886–891. doi:10.1038/nbt.1991

   The original SPADE paper. Introduces density-dependent downsampling + agglomerative clustering + MST for CyTOF data.

2. Qiu, P. (2017). “Toward deterministic and semiautomated SPADE analysis.” Cytometry Part A, 91(7), 714–727. doi:10.1002/cyto.a.23068

   Addresses stochasticity and proposes deterministic variants of SPADE.

Key applications

3. Bendall, S.C., Simonds, E.F., Qiu, P., et al. (2011). “Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.” Science, 332(6030), 687–696. doi:10.1126/science.1198704

   First high-profile CyTOF + SPADE application — mapping human hematopoiesis.

4. Levine, J.H., Simonds, E.F., Bendall, S.C., et al. (2015). “Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.” Cell, 162(1), 184–197. doi:10.1016/j.cell.2015.05.047

   Introduces PhenoGraph; includes SPADE comparisons. Source of the Levine_32dim and Levine_13dim benchmark datasets.

Benchmark studies

5. Samusik, N., Good, Z., Spitzer, M.H., Davis, K.L., & Nolan, G.P. (2016). “Automated mapping of phenotype space with single-cell data.” Nature Methods, 13(6), 493–496. doi:10.1038/nmeth.3863

   Systematic comparison of SPADE, FlowSOM, PhenoGraph, and other methods. Source of the Samusik_01 benchmark dataset.

6. Weber, L.M. & Robinson, M.D. (2016). “Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.” Cytometry Part A, 89(12), 1084–1096. doi:10.1002/cyto.a.23030

   Comprehensive benchmark of 18 clustering methods including SPADE, with standardized evaluation metrics.

Related methods

7. Van Gassen, S., Callebaut, B., Van Helden, M.J., et al. (2015). “FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.” Cytometry Part A, 87(7), 636–645. doi:10.1002/cyto.a.22625

   FlowSOM — often compared to SPADE. Faster, also produces a tree, but uses SOMs instead of density-dependent downsampling.

8. Levine, J.H., et al. (2015). See reference 4 above.

   PhenoGraph — graph-based clustering using k-NN + Louvain community detection. Produces flat clusters (no tree), but often better cluster purity.

Datasets used in densitree benchmarks

9. Levine_32dim: 81,747 cells, 32 markers, 14 manually gated populations. From reference 4. Available via FlowRepository FR-FCM-ZZPH and Weber & Robinson’s HDCytoData.

10. Samusik_01: 86,864 cells, 39 markers, 24 manually gated populations. From reference 5. Available via FlowRepository FR-FCM-ZZYA and HDCytoData.