Table 2.

Visualization methods.

Method	Type of method	Environment	Description	Limitations
PCA	Visualization, relatedness or trajectory	Many different options, R (included in Cytofkit), Phyton, etc.	It establishes distance and relatedness between populations in the linear space Preserve global distances Fast	Non-linear relatedness not considered Overcrowding of data points
tSNE	Visualization	R (included in Cytofkit), Phyton, FlowJo, etc.	Non-linear dimensionality reduction based on the k-neighbor algorithm Not overcrowding of data points Preserve local distances in detriment of global structure	Distance among clusters has no meaning Limited number of events per run Not so fast
UMAP	Visualization, some degree of relatedness	R (included in Cytofkit2 but requires Python) Python, and FlowJo Exchange plug-in	Non-linear dimensionality reduction. Preserve local distances, with some global structure Fast	Loss of resolution among populations with little variation Crowding of similar populations