title: Toward deterministic and semiautomated SPADE analysis.
abstract: SPADE stands for spanning-tree progression analysis for density-normalized events. It combines downsampling, clustering and a minimum-spanning tree to provide an intuitive visualization of high-dimensional single-cell data, which assists with the interpretation of the cellular heterogeneity underlying the data. SPADE has been widely used for analysis of high-content flow cytometry data and CyTOF data. The downsampling and clustering components of SPADE are both stochastic, which lead to stochasticity in the tree visualization it generates. Running SPADE twice on the same data may generate two different tree structures. Although they typically lead to the same biological interpretation of subpopulations present in the data, robustness of the algorithm can be improved. Another avenue of improvement is the interpretation of the SPADE tree, which involves visual inspection of multiple colored versions of the tree based on expression of measured markers. This is essentially manual gating on the SPADE tree and can benefit from automated algorithms. This article presents improvements of SPADE in both aspects above, leading to a deterministic SPADE algorithm and a software implementation for semiautomated interpretation. © 2017 International Society for Advancement of Cytometry.
authors.affiliation: Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia.
authors.forename: Peng
authors.initials: P
authors.lastname: Qiu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D005434:Flow Cytometry / Q000379:methods* / Q000706:statistics & numerical data; D018740:Genetic Heterogeneity; D006801:Humans; D012984:Software
keywords: SPADE; deterministic