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Integrating spatial transcriptomics into your research can provide game-changing perspectives on tissue biology, enabling deeper insights into cell-to-cell interactions, cellular diversity, disease pathways, and more. However, analyzing spatial transcriptomics data can be a daunting task. At 10x Genomics, we provide data processing software and analysis guidance to help you navigate your analysis journey. In this article, we survey how the research community uses third-party computational methods to analyze spatial gene expression data from both the 10x Visium and Xenium In Situ platforms.
The literature examples in this article showcase powerful applications of our spatial technology. They serve as a guide to the types of data analysis tools used across a wide range of scientific topics. They are just a fraction of the analytical ingenuity found in research publications. You can visit the 10x publications page and other Analysis Guide articles for additional resources. You can also find a more comprehensive list of spatial analysis tools on this GitHub repository.
Xenium In Situ Gene Expression
- Phase 1: Data processing, decoding, and reanalysis:
- Xenium Analyzer and Xenium Onboard Analysis (10x Genomics): on-instrument data processing/decoding
- Xenium Explorer (10x Genomics): interactive visualization tool for Xenium data.
- Xenium Ranger (10x Genomics): a set of pipelines for flexible off-instrument reanalysis, including relabeling transcripts, resegmenting cells, importing customized segmentation data, or renaming datasets.
- Phase 2: Biological interpretation of spatial data:
Visium Spatial Gene Expression
- Phase 1: Processing raw sequencing data:
- Space Ranger (10x Genomics): a set of analysis pipelines for Visium data with brightfield or fluorescence microscope images, allowing users to map the whole transcriptome in a variety of tissues. Space Ranger v4.0 uses a custom StarDist model for cell segmentation based on nuclei detection and expansion.
- Phase 2: Biological interpretation of spatial data:
| Resource | 10x Platform | Summary | Analysis Framework | Additional Tools (description and links are provided in the Appendix of this article) |
|---|---|---|---|---|
| Cheng et al., 2025. Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data. BMC Bioinformatics. | Xenium In Situ | Compared performance of five reference-based methods (SingleR, Azimuth, RCTD, scPred and scmapCell) with the marker-gene-based manual annotation method on a Xenium human breast cancer dataset. | Seurat | Spatial: RCTD Single Cell: SingleR, Azimuth, scPred, scmapCell |
| Davar et al., 2024. Neoadjuvant vidutolimod and nivolumab in high-risk resectable melanoma: A prospective phase II trial. Cancer Cell. | Visium v1 | Combined vidutolimod and nivolumab treatments and found a broad anti-tumor immune response associated with a distinct baseline myeloid gene signature and gut microbiota. | Seurat | Spatial: g:Profiler/g:GOSt |
| Janesick et al., 2023. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Comm. | Xenium In Situ, Visium v2, Chromium | Combined 10x Genomics single cell and spatial technologies to explore tissue heterogeneity in human breast cancer sections and demonstrate how the integration of these methods leads to deeper insights. | Scanpy, Seurat | Spatial: spacexr |
| Kaliq et al., 2024. Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity. Nature Genetics. | Visium v2 | Applied spatial transcriptomics to matched primary and metastatic pancreatic ductal adenocarcinoma (PDAC) samples, providing critical insights into metastatic PDAC biology. | Seurat, stLearn | Spatial: RCTD, MISTy, ISCHIA, SpotClean |
| Kukanja et al., 2024. Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology. Cell. | Xenium In Situ | Investigated cellular dynamics of multiple through disease progression modeling in mouse experimental autoimmune encephalomyelitis (EAE) by performing single cell spatial expression profiling using in situ sequencing (ISS). | Scanpy/Squidpy, Seurat | Spatial: StarDist Single Cell: CellChat |
| Marco Salas et al., 2025. Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows. Nature Methods. | Xenium In Situ | Performed extensive testing of various third-party analysis tools on 25 Xenium datasets. Provides recommendations for data analysis best practices. | Scanpy/Squidpy, Seurat, Giotto | Spatial: Baysor, Mesmer, Watershed, Cellpose, ClusterMap, SOMDE, spatialDE, HotSPOT, SINFONIA, SpaGE, SpaOTsc, gimVI, Tangram, SpaGCN, Banksy, DeepST, STAGATE, SPACEL, SSAM, Points2Regions Single Cell: Liger, NovoSpaRc |
| Mo et al., 2024. Tumour evolution and microenvironment interactions in 2D and 3D space. Nature. | Visium v1, Chromium | Examined tumor structures and habitats in cancer tissue using spatial transcriptomics. | Seurat | Spatial: Morph, RCTD, COMMOT, BigWarp, CalicoST, InferCNV, GATK Single Cell: ROGUE |
| Oliviera et al., 2025. High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer. Nat Gen. | Visium HD, Xenium In Situ, Chromium | Used high-resolution spatial technologies to understand cellular interactions in the TME and pave the way for larger studies that may unravel mechanisms and biomarkers of CRC biology, improving diagnosis and disease management strategies. | Seurat, Python | Spatial: spacexr |
| Polanski et al., 2024. Bin2cell reconstructs cells from high resolution Visium HD data. Bioinformatics. | Visium HD | Used Bin2cell to reconstruct cells from the highest resolution Visium data (2-µm bins) by leveraging morphology image segmentation and gene expression information, demonstrating improvements in downstream analysis. | Scanpy | Spatial: bin2cell, CellTypist, StarDist |
| Wu et al., 2024. A developmental biliary lineage program cooperates with Wnt activation to promote cell proliferation in hepatoblastoma. Nat Comm. | Visium v1, Chromium | Used spatial transcriptomics to characterize hepatoblastoma and identify a non-genetic mechanism whereby developmental lineage programs influence tumor evolution. | Seurat | Spatial: Velocyto, scvelo Single Cell: monocle |
| Zheng et al., 2025. Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms. bioRxiv. | Xenium In Situ, Visium v2 | Introduced the Spatial Integration for Imputation and Deconvolution (SIID) algorithm to reconstruct a latent spatial gene expression matrix from a pair of observations from 10x Genomics spatial transcriptomics technologies. | Python | Spatial: SIID |
Below is a summary of the tools mentioned in the table above. The goal of this list is to provide quick links for your convenience.
AMULET: (Python, single cell) A count-based method for detecting doublets from single nucleus ATAC-seq (snATAC-seq) data.
Azimuth: (R/web, single cell and spatial) Series of computational tools for reference-mapping of single cell data.
Banksy: (Seurat, spatial) A method for clustering spatial omics data by augmenting the features of each cell with both an average of the features of its spatial neighbors along with neighborhood feature gradients.
Baysor: (Julia, Xenium In Situ) Bayesian segmentation of imaging-based spatial transcriptomics data.
BBKNN: (Python, single cell and spatial) A fast and intuitive batch effect removal tool that can be directly used in the scanpy workflow.
BigWarp: (ImageJ/FIJI, spatial) A tool for manual pointwise deformable registration using bigdataviewer.
Bin2cell: (Python, Visium HD) Bin2cell proposes 2µm bin-to-cell groupings based on segmentation, which can be performed using the morphology image and/or a visualization of the gene expression.
CalicoST: (Python, poly-A based Visium) A probabilistic model that infers allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics.
Cell2location: (Python, Visium) Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics.
CellChat: (R, single cell and spatial) R toolkit for inference, visualization and analysis of cell-cell communication from single cell data.
CellOracle: (Python, single cell) Python library for in silico gene perturbation analysis using single cell omics data and gene regulatory network models.
- GitHub
- https://morris-lab.github.io/CellOracle.documentation/
- https://doi.org/10.1038/s41586-022-05688-9
CellPhoneDB: (Python, single cell and spatial) A suite to study cell-cell communication using single cell transcriptomics data.
Cellpose: (Python, spatial) An anatomical segmentation algorithm written in Python 3.
Celltypist: (Python/Web/CLI, single cell and spatial) An open-source tool for automated cell type annotations as well as a working group in charge of curating models and ontologies.
cisTopic: (R, single cell) An R package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data.
ClusterMap: (Python, Xenium In Situ) Multi-scale clustering analysis of spatial gene expression data.
COMMOT: (Python, spatial) Infers cell-cell communication in spatial transcriptomics, accounting for the competition between different ligand and receptor species as well as spatial distances between cells.
- GitHub
- https://commot.readthedocs.io/en/latest/index.html
- https://doi.org/10.1038/s41592-022-01728-4
CytoTRACE: (Web, single cell) A computational method that predicts the differentiation state of cells from single cell RNA-sequencing data.
DeepST: (Python, spatial) A deep learning framework to identify spatial domains.
Drug2Cell: (Python, single cell and spatial) Pipeline for predicting drug targets as well as drug side effects.
g:Profiler/g:GOSt: (Web, single cell and spatial) Functional enrichment analysis tool that supports various evidence types, identifier types, and organisms.
GATK: (Java/Python, single cell and poly-A based Visium) Genome Analysis Toolkit - Variant discovery in high-throughput sequencing data.
gimVI: (Scanpy, spatial) Method for imputing missing genes in spatial data from sequencing data.
GRNBoost: (Python, single cell) A scalable strategy for gene regulatory network (GRN) inference.
HotSPOT: (Web) A computational tool to design targeted sequencing panels.
InferCNV: (R, single cell and spatial) InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for somatic large-scale chromosomal copy number alterations, such as gains or deletions of entire chromosomes or large segments of chromosomes.
ISCHIA: (R, spatial) A framework for analysis of cell-types and Ligand-Receptor co-occurrences in spatial transcriptomics/proteomics data.
LIGER: (R, single cell) A package for integrating and analyzing multiple single cell datasets.
Mesmer: (ImageJ/FIJI and Python, spatial) A method for whole-cell segmentation of multiplexed tissue imaging data.
Milo: (R and Python, single cell and spatial) A method for differential abundance analysis on KNN graph from single cell datasets.
MISTy: (R, spatial) Multiview Intercellular SpaTial modeling framework (MISTy) is an explainable machine learning framework for knowledge extraction and analysis of single cell, highly multiplexed, spatially resolved data.
Monocle: (R, single cell) An analysis toolkit for single cell RNA-seq.
Morph: (Python, spatial) An automated tool for inferring tumor boundaries from spatial transcriptomic datasets.
Muon: (Python, single cell and spatial) An analysis framework for multiomic datasets (e.g., RNA, ATAC, and protein).
NovoSpaRc: (Python, single cell) A python package for predicting locations of single cells in space by solely using single cell RNA sequencing data.
Points2Regions: (Python, spatial) A computational tool for identifying regions with similar mRNA compositions.
RCTD: (R, spatial) A tool that assigns single cell types or cell type mixtures to spatial transcriptomics spots.
ROGUE: (Web, single cell and spatial) An application that performs differentially expressed gene analysis, gene ontology, and pathway enrichment analysis, potential biomarker identification, and advanced statistical analyses.
SCENIC+: (Python, single cell) A python package to build gene regulatory networks (GRNs) using combined or separate single cell gene expression (scRNA-seq) and single cell chromatin accessibility (scATAC-seq) data.
scFates: (Python, single cell) A scalable python suite for tree inference and advanced pseudotime analysis from scRNAseq data.
scmap: (R, single cell) A tool for unsupervised projection of single cell RNA-seq data.
scPred: (R, single cell) A supervised method for cell-type classification from single cell RNA-seq data.
Scrublet: (Python, single cell) Python code for identifying doublets in single cell RNA-seq data.
scVelo: (Python, single cell and poly-A based Visium) A scalable toolkit for RNA velocity analysis in single cells.
SNP2Cell: (Python, single cell and spatial) A package for identifying gene regulation involved in specific traits and cell types. It combines three elements: 1) GWAS summary statistics, 2) single cell data, and 3) a base gene regulatory network.
SIID: (Python, spatial) A method for joint imputation and deconvolution across two spatial datasets.
SINFONIA: (Python, spatial) Scalable method for identifying spatially variable genes via ensemble strategies.
SingleR: (R and Python, single cell and spatial) Computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes.
SOMDE: (Python, spatial) An algorithm for finding gene spatial patterns based on a Gaussian process accelerated by self-organizing map (SOM) neural network.
Souporcell: (Python, single cell) A method for clustering mixed-genotype scRNAseq experiments by individual.
SoupX: (R, single cell) An R package for the estimation and removal of cell free mRNA contamination in droplet based single cell RNA-seq data.
SpaGCN: (Python, spatial) A graph convolutional network approach that integrates gene expression, spatial location, and histology in spatial transcriptomics data analysis.
SpatialDE: (Python, spatial) A method to identify genes that significantly depend on spatial coordinates in non-linear and non-parametric ways.
SPACEL: (Python, spatial) Spatial architecture characterization by deep learning for spatial transcriptomics data.
Spacexr: (R, spatial) An R package that implements RCTD for assigning cell types and cell type-specific differential expression to spatial transcriptomics data.
SpaGE: Method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration.
SpaOTsc: (Python, single cell and spatial) Method that uses structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes.
SpotClean: (R, Visium v1/v2) A computational method to adjust for spot swapping in spatial transcriptomics data.
SSAM: (Python, spatial) Spot-based spatial cell-type analysis by multidimensional mRNA density estimation.
STAGATE (Scanpy, spatial) A method for spatial domain identification using spatial transcriptomics data
StarDist: (Python, spatial) A deep learning nuclei segmentation method. A custom StarDist model is used in Space Ranger v4.
Tangram: (Python, spatial) A Python package for mapping single cell (or single nucleus) gene expression data onto spatial gene expression data.
TissueTag: (Python, spatial) Pixel-based annotation of Visium datasets.
Velocyto: (Python, single cell and poly-A based Visium) A package for the analysis of expression dynamics in single cell RNA seq data.
Watershed: (Python, spatial) An image segmentation algorithm.