Table 1.

Summary of approaches used to analyze antigen‐specific polyfunctional T cell responses

Software	Analysis strategy	Advantages	Disadvantages	Graphical display	Availability
FlowJo	Basic flow cytometry software package; manual, sequential gating	Popular, widely used software; output can be imported into other multidimensional software tools	Limited in scope for analyzing >2 dimensions; time consuming and subjective gating	Histograms and dot plots	Tree Star (P)
GemStone	PSM; template‐driven analysis	Accounts for population overlap and simple clustering routines; lack of gating eliminates subjectivity and operator variability	Templates require knowledge of some biology of the system; TriCOMs visually hard to interpret and compare ≥3 parameters	TriCOMs	Verity Software House (P)
SPADE	Unsupervised clustering extracts cellular hierarchy	No prior knowledge of hierarchical order needed; scalable; better for mutually exclusive markers and mixed lineage populations	Represents data as clusters rather than individual cells; does not appropriately analyze if data do not lend themselves to clustering	Spanning trees	Cytobank (F/P)
FlowSOM	R‐based clustering tool	Similar to SPADE; although R based, only requires minimal understanding of R to use effectively	Similar to SPADE; detailed data challenging to compare across multiple treatment groups	Minimal spanning trees or grids	Bioconductor (F)
viSNE	Nonlinear dimensionality reduction algorithm based on t‐SNE	Unsupervised and does not require in‐depth knowledge of experimental system; preserves cell separation and retains prior gating information	Low‐dimensional mapping cannot represent all of the information in a high‐dimensional space; a large number of cyt maps require visual overlay to make multidimensional comparisons	cyt maps	Cytobank (F/P)
FLOCK	Unsupervised rapid binning	Up‐front gating uses familiar FlowJo; delineates population‐based intensity of expression profiles; cross‐sample statistical analysis	Difficult to demonstrate differences among populations with very complex matrices	Color‐coded dot plots	ImmPort (F)
ACCENSE	Combines nonlinear dimensionality reduction with k‐means clustering	Automated cell classification while retaining single‐cell resolution; color codes identified populations; facilitates downstream statistical analysis with tabular data output	This type of analysis is more challenging to communicate to users; individual plot per sample makes comparison across multiple treatment groups more difficult	Color‐coded, multi‐dimensional cluster plots	ACCENSE (F)
SPICE	Quantitatively compares discrete phenotypic profiles in a mixture; uses FlowJo output with intermediary formatting tool Pestle	Ease of use and clear visualization of complex datasets; offers background subtraction and permutation statistical analysis; permits comparison across large numbers of treatment groups	Manual gating within FlowJo affords significant amount of subjectivity; software only works on Macintosh operating systems	Pie charts, bar graphs, cool plots	NIAID (F)