Journal: Methods in enzymology

Article Title: Characterization of the tumor immune infiltrate by multiparametric flow cytometry and unbiased high-dimensional data analysis

doi: 10.1016/bs.mie.2019.11.012

Figure Lengend Snippet: Visualization methods.

Article Snippet: Some of the algorithms used for this purpose are briefly described below (see also ). table ft1 table-wrap mode="anchored" t5 caption a7 Method Environment Description Limitations PhenoGraph R (included in Cytofkit), Phyton, MATLAB ™ , FlowJo Exchange plug-in Based on nearest-neighbor graph, followed by definition of communities as interconnected events Number of clusters not customizable Not so fast ClusterX R (defined in Cytofkit) Based on densities in the tSNE projection map Fast Limited to tSNE ability to define clusters Number of clusters not customizable FlowSOM R package (included in Cytofkit), FlowJo Exchange plug-in Based in Self-Organization Maps (SOM) follow by hierarchical clustering Very fast Number of clusters customizable For exploratory analysis, hard to determine the number of clusters Open in a separate window Clustering methods.

Techniques:

Journal: Methods in enzymology

Article Title: Characterization of the tumor immune infiltrate by multiparametric flow cytometry and unbiased high-dimensional data analysis

doi: 10.1016/bs.mie.2019.11.012

Figure Lengend Snippet: Clustering methods.

Article Snippet: Some of the algorithms used for this purpose are briefly described below (see also ). table ft1 table-wrap mode="anchored" t5 caption a7 Method Environment Description Limitations PhenoGraph R (included in Cytofkit), Phyton, MATLAB ™ , FlowJo Exchange plug-in Based on nearest-neighbor graph, followed by definition of communities as interconnected events Number of clusters not customizable Not so fast ClusterX R (defined in Cytofkit) Based on densities in the tSNE projection map Fast Limited to tSNE ability to define clusters Number of clusters not customizable FlowSOM R package (included in Cytofkit), FlowJo Exchange plug-in Based in Self-Organization Maps (SOM) follow by hierarchical clustering Very fast Number of clusters customizable For exploratory analysis, hard to determine the number of clusters Open in a separate window Clustering methods.

Techniques: