Table 1.

DL algorithms reviewed in the paper

App	Algorithm	Models	Evaluation	Environment	Codes	Refs
Imputation
	DCA	AE	DREMI	Keras, Tensorflow, scanpy	https://github.com/theislab/dca	[18]
	SAVER-X	AE + TL	t-SNE, ARI	R/sctransfer	https://github.com/jingshuw/SAVERX	[58]
	DeepImpute	DNN	MSE, Pearson’s correlation	Keras/Tensorflow	https://github.com/lanagarmire/DeepImpute	[20]
	LATE	AE	MSE	Tensorflow	https://github.com/audreyqyfu/LATE	[59]
	scGAMI	AE	NMI, ARI, HS and CS	Tensorflow	https://github.com/QUST-AIBBDRC/scGMAI/	[60]
	scIGANs	GAN	ARI, ACC, AUC and F-score	PyTorch	https://github.com/xuyungang/scIGANs	[19]
Batch correction
	BERMUDA	AE + TL	KNN batch-effect test (kBET), the entropy of Mixing, SI	PyTorch	https://github.com/txWang/BERMUDA	[63]
	DESC	AE	ARI, KL	Tensorflow	https://github.com/eleozzr/desc	[67]
	iMAP	AE + GAN	kBET, Local Inverse Simpson’s Index (LISI)	PyTorch	https://github.com/Svvord/iMAP	[70]
Clustering, latent representation, dimension reduction and data augmentation
	Dhaka	VAE	ARI, Spearman Correlation	Keras/Tensorflow	https://github.com/MicrosoftGenomics/Dhaka	[72]
	scvis	VAE	KNN preservation, log-likelihood	Tensorflow	https://bitbucket.org/jerry00/scvis-dev/src/master/	[75]
	scVAE	VAE	ARI	Tensorflow	https://github.com/scvae/scvae	[76]
	VASC	VAE	NMI, ARI, HS and CS	H5py, keras	https://github.com/wang-research/VASC	[77]
	scDeepCluster	AE	ARI, NMI, clustering accuracy	Keras, Scanpy	https://github.com/ttgump/scDeepCluster	[79]
	cscGAN	GAN	t-SNE, marker genes, MMD, AUC	Scipy, Tensorflow	https://github.com/imsb-uke/scGAN	[82]
Multi-functional models (IM: imputation, BC: batch correction, CL: clustering)
	scVI	VAE	IM: L1 distance; CL: ARI, NMI, SI; BC: Entropy of Mixing	PyTorch, Anndata	https://github.com/YosefLab/scvi-tools	[17]
	LDVAE	VAE	Reconstruction errors	Part of scVI	https://github.com/YosefLab/scvi-tools	[86]
	SAUCIE	AE	IM: R2 statistics; CL: SI; BC: modified kBET; Visualization: Precision/Recall	Tensorflow	https://github.com/KrishnaswamyLab/SAUCIE/	[15]
	scScope	AE	IM:Reconstruction errors; BC: Entropy of mixing; CL: ARI	Tensorflow, Scikit-learn	https://github.com/AltschulerWu-Lab/scScope	[92]
Cell-type Identification
	DigitalDLSorter	DNN	Pearson correlation	R/Python/Keras	https://github.com/cartof/digitalDLSorter	[51]
	scCapsNet	CapsNet	Cell-type Prediction accuracy	Keras, Tensorflow	https://github.com/wanglf19/scCaps	[52]
	netAE	VAE	Cell-type Prediction accuracy, t-SNE for visualization	pyTorch	https://github.com/LeoZDong/netAE	[101]
	scDGN	DANN	Prediciton accuracy	pyTorch	https://github.com/SongweiGe/scDGN	[53]
Function analysis
	CNNC	CNN	AUROC, AUPRC and accuracy	Keras, Tensorflow	https://github.com/xiaoyeye/CNNC	[54]
	scGen	VAE	Correlation, visualization	Tensorflow	https://github.com/theislab/scgen	[114]

DL Model keywords: AE + TL: autoencoder with transfer learning, DANN: domain adversarial neural network, CapsNet: capsule neural network