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] |