| 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 |