Table 5.

Tools for processing single-cell multi-omics sequencing data with their functions and programming language

Tools	Methods	Functions	languages	References
MOFA	Matrix decomposition	Integration and data imputation, clustering.	R	[89]
LIGER	Matrix decomposition, SFN graph	Integration and batch effect, visualization, clustering, maker identification.	R	[17]
SpatialDE	VI, multivariate normal modeling	Clustering, identify spatially variable genes, spatial and/or temporal annotation, visualization.	Python	[90]
TotalVI	VAE	Integration, batch effect and data imputation,visualization,clustering.	Python	[91]
Seurat V3	CCA, MNN	Integration, batch effect and data imputation, clustering, cluster annotation.	R	[66]
Seurat V4	WNN graph	Integration, batch effect clustering, trajectory analysis, cluster annotation, response to vaccination.	R	[92]
Mimitou et al.	Seurat V3, harmony, LMM	Integration, data imputation, batch effect, clustering, trajectory analysis, multiplexed CRISPR perturbations in primary T cells.	R	[59]
MATCHER	Manifold learning	Integration, inferring pseudotime, trajectory analysis.	Python	[93]
Ma et al.	SNF, KNN	Integration and data imputation, clustering, visualization, pseudotime inference.	R and python	[49]