Table 4. The methods of combining the multimodal datasets and spatial transcriptomics sequencing.

Authors	Sample	Integrating method	Main outcome
Combined with ST
Mika et al. [108]	Human thymus dataset	MultiMAP	Revealed transcription factor expression and binding site accessibility of T-cell differentiation.
Britta et al. [109]	Mouse gastrula	MEFISTO	By considering the spatio-temporal dependencies of samples, the method combined the continuous covariate among different samples, and continuously and dynamically detected the differentiation trajectory of organisms.
Vickovic et al. [111]	Mouse brain, spleen and colorectal cancer model	SM-Omics	An automated sequencing method mainly for spatial antibody-based multiplex protein detection.
Biancalani et al. [107]	Mouse brain	Tangram	A method combining all RNA datasets with anatomical atlases.
Combined with in situ spatial sequencing
Chee-Huat Linus Eng et al. [112]	Mouse brain	seqFISH+	Revealed the mRNA localizations of subcellular structures and the ligand-receptor pairs across neighboring cells.
Vu et al. [113]	Colorectal cancer and melanoma	MOSAICA	A multiomics method for mRNA and protein sequencing, which showed the ability of multiplexing scalability.
Park et al. [114]	Mouse brain	SSAM	A robust cell segmentation-free computational framework for identifying cell-types and tissue domains in two- and three-dimension.