Table 2.

Tools for scMulti-omics analysis and application examples.

Toolsa	Implemented inb	Datac	Number of Cells	Main biological insights	Ref
MATCHER	Py	RE+DM+CA	5,151	Establish a continuum that ranges from pluripotency to a differentiation primed state; while the shared master time was highly correlated in the matched dataset which was useful for determining the overall reprogramming progress of each cell	[26]
MIMOSCA	Py	RE+GP	200,000	Predict the regulation of anti-parasitic response genes Gbp2,2b,3,4,5 and 7 by inducing CRISPR/Cas9 knockout, targeting 24 transcriptome factors; suggest Stat2’s impact on Gpb genes may be mediated through Irf8.	[52]
MOFA	R/Py	RE+DM	87	Reveal the cooperation between the transcriptome and methylation sites during the transition from naive to primed pluripotent states. Factor-specific markers were identified, such as Rex1/Zpf42, Tbx3, and Fbxo15.	[36]
		RE+PE	200,000	Identified 44 genes whose activation induces a zygotic genome activation-like transcriptional response, including 40 novel maternal proteins.	[45]
Clonealign	R	RE+DC	1,152	Build single-cell phylogeny with four distinct clades and eight sub-clades. The intra-clonal clustering identified cell cycle corresponding clusters.	[28]
Trendsceek	R	RE+SI	~10,000	Identify 35 significant genes with expression primarily in nongranular cells including Ptn, Nr2f2, and Fabp7 in mouse olfactory bulb tissue.	[32]
SpatialDE	Py	RE+SI	~10,000	Identify 67 spatial variable genes with spatial dependencies of the gene expression variance and showed clear spatial substructure, consistent with matched tissues in mouse olfactory bulb.	[33]
Seurat3	R	RE+CA	14,249+100,000	Reveal cell-type-specific regulatory loci whose accessibility profiles were consistent with expected patterns.	[24]
			35,882	Identify 91,601 putative peak-to-gene linkages and inferred the potential oncogene RUNX1.	[91]
			7,846	Reveal cell-state transcriptional regulators and lineage relationships in mammary gland cells.	[115]
		RE+PE	33,454	Matched RNA expression and 25 cell-surface protein expressions, leveraging connections between protein abundance and gene expression.	[24]
		RE+SI	14,249	Predict spatial gene expression patterns and spatial subpopulations.	[24]
LIGER	R	RE+DM	55,803+3378	Resulted in 37 neuron clusters and identified methylation regions that were anticorrelated with Arx expression, including a validated Arx enhancer.	[49]
MUSIC	R	RE+GP	32,777	Identify a novel knockout effect on cell migration impacted by the perturbation of Cebpb on immune cell activation, and gene-gene perturbation associations between Cebpb and other gene perturbations.	[38]
Giotto	R	RE+SI	913	Identify six global and distinctive clusters including excitatory neurons (Icam), GABAergic neurons (Slc32a1), and four smaller groups; Visualize both single-cell resolution heterogeneity in both expression and spatial space representations.	[53]

^aTools that are equipped with multiple functions; developed methods with only scripts are not considered in this table; tools are sorted by publishing year from old to new.

^bThe platform of each tool, either as an R or Python (Py) package.

^cData type combination examples using the corresponding tool. RE=RNA expression, DM=DNA methylation, CA=chromatin accessibility, PE=protein expression, SI=Spatial information, DC=DNA copy, GP=Gene perturbation, HM=HiChiP.