A: model-based imputation | ||

bayNorm | Binomial model, empirical Bayes prior | [47] |

BISCUIT | Gaussian model of log counts, cell- and cluster-specific parameters | [48] |

CIDR | Decreasing logistic model (DO), non-linear least-squares regression (imp) | [49] |

SAVER | NB model, Poisson LASSO regression prior | [50] |

ScImpute | Mixture model (DO), non-negative least squares regression (imp) | [51] |

scRecover | ZINB model (DO identification only) | [52] |

VIPER | Sparse non-negative regression model | [53] |

B: data smoothing | ||

DrImpute | k-means clustering of PCs of correlation matrix | [54] |

knn-smooth | k-nearest neighbor smoothing | [55] |

LSImpute | Locality sensitive imputation | [56] |

MAGIC | Diffusion across nearest neighbor graph | [57] |

netSmooth | Diffusion across PPI network | [58] |

C: data reconstruction, matrix factorization | ||

ALRA | SVD with adaptive thresholding | [59] |

ENHANCE | Denoising PCA with aggregation step | [60] |

scRMD | Robust matrix decomposition | [61] |

consensus NMF | Meta-analysis approach to NMF | [62] |

f-scLVM | Sparse Bayesian latent variable model | [63] |

GPLVM | Gaussian process latent variable model | [64] |

pCMF | Probab. count matrix factorization with Poisson model | [65] |

scCoGAPS | Extension of NMF | [66] |

SDA | Sparse decomposition of arrays (Bayesian) | [67] |

ZIFA | ZI factor analysis | [68] |

ZINB-WaVE | ZINB factor model | [69] |

C: data reconstruction, machine learning | ||

AutoImpute | AE, no error back-propagation for zero counts | [70] |

BERMUDA | AE for cluster batch correction (MMD and MSE loss function) | [71] |

DeepImpute | AE, parallelized on gene subsets | [72] |

DCA | Deep count AE (ZINB / NB model) | [73] |

DUSC / DAWN | Denoising AE (PCA determines hidden layer size) | [74] |

EnImpute | Ensemble learning consensus of other tools | [75] |

Expression Saliency | AE (Poisson negative log-likelihood loss function) | [76] |

LATE | Non-zero value AE (MSE loss function) | [77] |

Lin_DAE | Denoising AE (imputation across k-nearest neighbor genes) | [78] |

SAUCIE | AE (MMD loss function) | [79] |

scScope | Iterative AE | [80] |

scVAE | Gaussian-mixture VAE (NB / ZINB / ZIP model) | [81] |

scVI | VAE (ZINB model) | [82] |

scvis | VAE (objective function based on latent variable model and t-SNE) | [83] |

VASC | VAE (denoising layer; ZI layer, double-exponential and Gumbel distribution) | [84] |

Zhang_VAE | VAE (MMD loss function) | [85] |

T: using external information | ||

ADImpute | Gene regulatory network information | [86] |

netSmooth | PPI network information | [58] |

SAVER-X | Transfer learning with atlas-type resources | [87] |

SCRABBLE | Matched bulk RNA-seq data | [88] |

TRANSLATE | Transfer learning with atlas-type resources | [77] |

URSM | Matched bulk RNA-seq data | [89] |