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Table 1 Methods compared in the study

From: A comparison framework and guideline of clustering methods for mass cytometry data

MethodsImplementation toolsDescriptionRef
UnsupervisedAccenseMATLABtSNE dimension reduction and 2D projection, kernel-based estimation of density, density-based peak-finding and partitioning[15]
PhenoGraphR (cytofkit package)Detection of k-nearest neighbors of each cell, Jaccard similarity coefficient as connectivity, community detection based on connection density[18]
XshiftVortexWeighted k-nearest neighbor density estimation, detection of density centroids, cells linked to centroid via density-ascending paths[21]
FlowSOMRSelf-organizing map (SOM) trained on scaled data, nodes of SOM connected by minimal spanning tree, consensus hierarchical meta-clustering of nodes[27]
flowMeansRK estimated by peak numbers of kernel density, kmeans clustering of estimated K, merging clusters by distance metrics[20]
DEPECHERTuning penalty by resampling dataset, penalized kmeans clustering[19]
kmeansMATLABStandard kmeans procedure 
Semi-supervisedACDCPythonMarker × cell matrix and cell type × marker table, detect landmark points by community detection, link cells to landmarks by random walkers[13]
LDAMATLABLinear discriminant analysis with training datasets[11]