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) |