TABLE 2.
Comparisons between flow cytometry and mass cytometry methods
| Methods | Classification | Description | Ref. |
|---|---|---|---|
| PCA | Dimensionality reduction | Linear, principle component analysis, orthogonal transformation. | 51 |
| Isomap | Nonlinear, spectral clustering, geodesic distance. | 53 | |
| Diffusion Map | Nonlinear, spectral clustering, diffusion distance. | 54 | |
| t‐SNE/viSNE | Nonlinear, t‐distributed stochastic neighborhood embedding, attraction/repulsion balance. | 55 | |
| UMAP | Nonlinear, uniform manifold approximation and projection, identify user‐specified number of neighbors to build high‐dimensional manifolds. | 56 | |
| ACCENSE | Unsupervised clustering | t‐SNE, kernel‐based density estimation, peak‐finding, and partitioning. | 57 |
| Phenograph | k‐nearest neighbors (k‐NN) detection, community detection, and Jaccard similarity coefficient. | 58 | |
| Xshift | Weighted k‐NN density estimation and density‐ascending path‐based clustering. | 59 | |
| FlowSOM | Self‐organizing map, minimal spanning tree‐based nodes connection, and consensus of hierarchical meta‐clustering. | 60 | |
| DEPECHE | Penalized k‐means clustering. | 61 | |
| SPADE | Density‐normalization, spanning tree progression analysis, and hierarchical/agglomerative clustering. | 62 | |
| ACDC | Semi‐supervised clustering | Community detection of landmark points. Cells and random walker‐based clustering. | 63 |
| LDA | Linear discriminant analysis. | 64 | |
| Citrus | Clustering with statistics | Hierarchically clustering, regularized supervised learning algorithms, nearest shrunken centroid methods, and lasso regularized logistic regression. | 67 |
| Wanderlust | Differentiation trajectory determination | Ensemble of k I‐nearest neighbor graphs, shortest path distance‐based trajectory construction, and waypoints‐based iteratively trajectory refinement. | 69 |