Reference and Github

Method	Reference	Github
Spotlight	SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes	https://github.com/MarcElosua/SPOTlight
cell2location	Cell2location maps fine-grained cell types in spatial transcriptomics	https://github.com/BayraktarLab/cell2location/
RCTD	Robust decomposition of cell type mixtures in spatial transcriptomics	https://github.com/dmcable/spacexr
Tangram	Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram	https://github.com/broadinstitute/Tangram
SCimilarity	A cell atlas foundation model for scalable search of similar human cells	https://github.com/Genentech/scimilarity
GraphST	Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST	https://github.com/JinmiaoChenLab/GraphST
inferCNV	Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma	https://github.com/broadinstitute/infercnv
Hotspot	Hotspot identifies informative gene modules across modalities of single-cell genomics	https://github.com/YosefLab/Hotspot
NeST	NeST: nested hierarchical structure identification in spatial transcriptomic data	https://github.com/bwalker1/NeST
hdWGCNA	hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data	https://github.com/smorabit/hdWGCNA
scanpy（t-test、Wilcoxon）	SCANPY: large-scale single-cell gene expression data analysis	https://github.com/scverse/scanpy
DESeq2	Dictionary learning for integrative, multimodal and scalable single-cell analysis	https://github.com/satijalab/seurat
edgeR	edgeR: a Bioconductor package for differential expression analysis of digital gene expression data	https://bioconductor.org/packages/devel/bioc/html/edgeR.html
MAST	MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data	https://github.com/RGLab/MAST
gsea/enrichr/prerank/gsva	GSEApy: a comprehensive package for performing gene set enrichment analysis in Python	https://github.com/zqfang/GSEApy
cellchat v2	CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics	https://github.com/jinworks/CellChat
cellphoneDB	CellPhoneDB v5: inferring cell–cell communication from single-cell multiomics data	https://github.com/ventolab/CellphoneDB
monocle3	The single-cell transcriptional landscape of mammalian organogenesis	https://github.com/cole-trapnell-lab/monocle3
pySCENIC	A scalable SCENIC workflow for single-cell gene regulatory network analysis	https://github.com/aertslab/pySCENIC
STRINGdb	The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest	-
Squidpy	Squidpy: a scalable framework for spatial omics analysis	https://github.com/scverse/squidpy
CRAWDAD	Characterizing cell-type spatial relationships across length scales in spatially resolved omics data	https://github.com/JEFworks-Lab/CRAWDAD
Cellular Neighborhood Analysis: Single Cell Type Mode	Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics	-
Cellular Neighborhood Analysis: Cell Type Score Mode	Spatial multi-omic map of human myocardial infarction	-
​immuneScore​/geneSetScore/survivalKM	Frontiers | IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures	https://github.com/IOBR/IOBR