The SPADE algorithm has been proposed as a new way to analysis high-dimensional cytometry data and to identified clusters of cells having similar phenotype. This algorithm performs a density-based down-sampling combined with an agglomerative hierarchical clustering. While SPADE offers unique opportunities for identifying cell populations, complementary approaches are needed to improve the characterization of identified cell populations. SPADEVizR is an R package designed to better visualize and analyze SPADE clustering results. This package extends the original SPADE outputs with techniques such as parallel coordinates, heatmaps, multidimensional scaling, volcano plots or streamgraph representations. Moreover, several statistical methods allow the identification of SPADE clusters with relevant biological behaviors. SPADEVizR can generate generalized linear models, Cox proportional hazards regression models and random forest models to predict biological outcomes, based on cluster abundances. SPADEVizR also has several features allowing to quantify and to visualize the quality of imported cell clustering results and can be used with results generated by algorithms different from SPADE.
|Author||Guillaume GAUTREAU and Nicolas TCHITCHEK|
|Bioconductor views||Clustering FlowCytometry MultidimensionalScaling Regression StatisticalMethod Visualization|
|Maintainer||Nicolas TCHITCHEK <firstname.lastname@example.org> and Guillaume GAUTREAU <email@example.com>|
|License||GPL-3 | file LICENSE|
|Package repository||View on GitHub|
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