No. | Methods | Modeling counts | Modeling zero inflation | Non-linear projection | Computation efficiency | Implementation language | Year of publication | Reference |
---|

1 | PCA | No | No | No | Yes | R | 1901 | [42] |

2 | ICA | No | No | No | No | R | 1994 | [43] |

3 | FA | No | No | No | Yes | R | 1952 | [44] |

4 | NMF | No | No | No | Yes | R | 1999 | [45] |

5 | Poisson NMF | Yes | No | No | Yes | R | 1999 | [45] |

6 | Diffusion Map | No | No | Yes | Yes | R | 2005 | [46] |

7 | ZIFA | No | Yes | No | No | Python | 2016 | [30] |

8 | ZINB-WaVE | Yes | Yes | No | No | R | 2018 | [32] |

9 | GLMPCA | Yes | No | No | No | R | 2019 | [47] |

10 | pCMF | Yes | Yes | No | No | R | 2019 | [31] |

11 | scScope | No | Yes | Yes | Yes | Python | 2019 | [37] |

12 | DCA | Yes | Yes | Yes | Yes | Python | 2018 | [40] |

13 | tSNE | No | No | Yes | No | R | 2008 | [48] |

14 | MDS | No | No | No | Yes | R | 1958 | [49] |

15 | LLE | No | No | Yes | Yes | R | 2000 | [50] |

16 | LTSA | No | No | Yes | No | R | 2004 | [51] |

17 | Isomap | No | No | Yes | Yes | R | 2000 | [11] |

18 | UMAP | No | No | Yes | Yes | Python | 2019 | [52] |

- These properties include whether it models count data (3rd column), whether it accounts for zero inflation (4th column), whether it is a linear dimensionality reduction method (5th column), its computation efficiency (6th column), implementation language (7th column), year of publication (8th column), and reference (9th column).
*FA* factor analysis, *PCA* principal component analysis, *ICA* independent component analysis, *NMF* nonnegative matrix factorization, *Poisson NMF* Kullback-Leibler divergence-based NMF, *ZIFA* zero-inflated factor analysis, *ZINB-WaVE* zero-inflated negative binomial-based wanted variation extraction, *pCMF* probabilistic count matrix factorization, *DCA* deep count autoencoder network, *scScope* scalable deep-learning-based approach, *GLMPCA* generalized linear model principal component analysis, Diffusion Map, *MDS* multidimensional scaling, *LLE* locally linear embedding, *LTSA* local tangent space alignment, Isomap; *UMAP* uniform manifold approximation and projection, *tSNE t*-distributed stochastic neighbor embedding