Other resource → awesome_spatial_omics
- General Tools
- Analysis Pipeline Steps
- ROI Selection
- QC
- Normalization
- Gene Imputation & Denoising
- Bias Correction
- Cell Segmentation
- Cell Annotation
- Cell Deconvolution
- Differential Expression
- Spatially Variable Genes
- Integration
- Cell Niches & Tissue Domains
- Cell Distances & Neighborhood
- Spatial Trajectories
- Cell-Cell Communication
- Metacells & Scalability
- Subcellular Analysis
- Copy Number Variations
- Isoform Analysis
- Transcription Factors & Gene Regulatory Networks
- Technical Enhancements
- Benchmarks
- Datasets & Foundation Models
- Best practices Bioconductor - [R] - Principles for statistical analysis of spatial transcriptomics data
- squidpy - [Python] - Spatial single cell analysis toolkit from scverse
- Giotto - [R/Python] - Comprehensive spatial data analysis suite
- Vitessce - [JavaScript] - Visual integration tool for exploration of spatial single cell experiments
- Voyager - [R] - Spatial transcriptomics visualization from Pachter lab
- BASS - [R] - Multiple sample analysis
- SpaVAE - [Python] - All-purpose tool for dimension reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, and resolution enhancement
- sopa - [Python] - Spatial omics processing and analysis
- SpatialAgent - [Python] - An autonomous AI agent for spatial biology
- ChatSpatial - [Python] - MCP server enabling spatial transcriptomics analysis via natural language, integrating 60+ methods including SpaGCN, Cell2location, LIANA+, CellRank for Visium, Xenium, MERFISH | Docs | PyPI
- LazySlide - [Python] - Framework for whole slide image (WSI) analysis
- pasta - [R] - Point pattern and lattice data analysis from Robinson lab
- rakaia - [JavaScript] - Scalable interactive visualization and analysis of spatial omics including spatial transcriptomics, in the browser (Website)
- semla - [R] - Useful tools for Spatially Resolved Transcriptomics data analysis and visualization
- sosta - [Python] - Spatial Omic Structure Analysis
- SPATA2 - [R] - Spatial transcriptomics analysis toolkit
- Thor - [Python] - Comprehensive platform for cell-level analysis with anti-shrinking Markov diffusion and 10 modular tools paired with Mjolnir web interface
- VR-Omics - [GUI] - Free platform-agnostic software with end-to-end automated processing of multi-slice spatial transcriptomics data through biologist-friendly GUI | Windows | MacOS | GitHub
- CosMx-Analysis-Scratch-Space - [R/Python] - Analysis resources and tools for CosMx SMI spatial transcriptomics | GitHub
- SpaceSequest - [R] - Unified pipeline for analysis, visualization, and publication of spatial transcriptomics data from Visium, Visium HD, Xenium, GeoMx, and CosMx | Tutorial
- nf-core/spatialxe - [Nextflow] - Nextflow pipeline for Xenium spatial transcriptomics analysis
- nf-core/sopa - [Nextflow] - Spatial Omics Pipeline Analysis (SOPA) for processing spatial transcriptomics data
- Allen Immunology Xenium Pipeline - [Web] - HISE platform pipeline for Xenium data processing
- S2Omics - [Python] - Designing smart spatial omics experiments with S2Omics
- SpaceTrooper - [R] - Quality control for spatial transcriptomics
- GrandQC - [Python] - Comprehensive solution for quality control in digital pathology
- SpotSweeper - [R] - Spatially aware quality control for spatial transcriptomics
- MerQuaCo - [Python] - A computational tool for quality control in image-based spatial transcriptomics
- Cell volume normalization - [R] - Recommended for imaging-based techniques, especially with small probe lists
- SpaNorm - [R] - First spatially-aware normalization method that concurrently models library size effects and underlying biology | Bioconductor
- Note: Gene imputation is not recommended for deconvolution tasks
- SpaGE - [Python] - Spatial gene expression prediction with best overall performance
- SpaGCN - [Python] - Spatial graph convolutional network for gene correlation analysis
- Tangram - [Python] - Transcript distribution prediction and spatial mapping
- SpaOTsc - [Python] - Spatial imputation via optimal transport
- Seurat integration workflow - [R] - Transfer gene expression from scRNA-seq reference
- Sprod - [Python] - Spatial denoising method
- TISSUE - [Python] - Transcript imputation with spatial single-cell uncertainty estimation
- SpaIM - [Python] - Single-cell Spatial Transcriptomics Imputation via Style Transfer
- ResolVI - [Python] - Bias correction method
- Statial - [R] - Correction of spill-over effects
- ovrl.py - [Python] - A python tool to investigate vertical signal properties of imaging-based spatial transcriptomics data
- SPLIT - [R] - SPLIT effectively resolves mixed signals and enhances cell-type purity
- cellAdmix - [R] - From Kharchenko lab - Evaluating and correcting cell admixtures in imaging-based spatial transcriptomics data.
- DenoIST - [R] - Denoising Image-based Spatial Transcriptomics data
- MisTIC - [Python] - A probabilistic model for correcting mis-assigned transcripts due to cell segmentation errors
- Baysor - [Julia] - Bayesian segmentation of spatial transcriptomics data
- Cellpose - [Python] - Generalist algorithm for cellular segmentation
- Cellpose 3 - With supersampling/restoration capabilities
- Cellpose-SAM - Cell and nucleus segmentation with superhuman generalization, works in 3D with various image conditions
- DeepCell - [Python] - Deep learning library for single cell analysis
- Bo Wang's method - [Python] - Better than SOTA segmentation (Nature Methods 2024)
- Proseg - [Rust] - Probabilistic segmentation method
- ComSeg - [Python] - Transcript-based point cloud segmentation
- FICTURE - [Python] - Feature-based image segmentation
- Xenium cell boundary - [Web] - Alternative when interior staining fails
- Bioimage.io - [Web] - Repository of AI models for segmentation
- ST-cellseg - [Python] - Segmentation for spatial transcriptomics
- CelloType - [Python] - Cell type detection and segmentation
- SAINSC - [Python] - Segmentation for sequencing-based spatial data
- BIDCell - [Python] - Biologically-informed deep learning for subcellular spatial transcriptomics segmentation
- FastReseg - [R] - Using transcript locations to refine image-based cell segmentation results
- Segger - [Python] - Fast and accurate cell segmentation of imaging-based spatial transcriptomics data
- Bering - [Python] - Graph deep learning for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics
- STP - [Python] - Single-cell Partition for subcellular spatially-resolved transcriptomics integrating data with nuclei-stained images
- Deep learning-based segmentation - [Python] - Extensively trained nuclear and membrane segmentation models for precise transcript assignment in CosMx SMI data
- CellSAM - [Python] - Foundation model for cell segmentation achieving state-of-the-art performance across cellular targets (bacteria, tissue, yeast, cell culture) and imaging modalities (brightfield, fluorescence, phase, multiplexed) | Paper | Web App
Segmentation-free methods:
- SSAM - [Python] - Subcellular segmentation-free analysis by multidimensional mRNA density
- Points2Regions - [Python] - Transcript-based region identification without segmentation
- Bin2Cell - [Python] - Segmentation for VisiumHD data
- ENACT - [Python] - Enhanced accuracy for VisiumHD segmentation
- STHD - [Python] - Cell annotation for VisiumHD
- STEM - [Python] - Cell type annotation method
- TACIT - [Python] - Automated cell type identification
- moscot - [Python] - Optimal transport-based cell mapping
- CELLama - [Python] - Cell annotation model
- TACCO - [Python] - Transfer of annotations between single-cell datasets
- TANGRAM - [Python] - Mapping single-cell to spatial data
- MMoCHi - [Python] - Cell annotation method
- CytoSPACE - [Python] - High-resolution alignment of single-cell and spatial transcriptomes
- ABCT - [R] - Anchor-based Cell Typer
- STHD - [Python] - Cell annotation for VisiumHD
- STELLAR - [Python] - Annotation of spatially resolved single-cell data with STELLAR
- Vesalius - [R] - Multi-scale and multi-context interpretable mapping of cell states across heterogeneous spatial samples
- STALocator - [Python] - ST-Aided Locator using deep learning to localize cells from single-cell RNA-seq data onto tissue slices
- CMAP - [Python] - Cellular Mapping of Attributes with Position, maps large-scale individual cells to precise spatial locations using divide-and-conquer strategy
- TransST - [Python] - Transfer learning framework leveraging cell-labeled information from external sources for cell-level heterogeneity inference | GitHub
- InSituType - [R] - Cell typing for CosMx SMI spatial transcriptomics
- HieraType - [R] - Hierarchical cell typing using RNA + protein for CosMx SMI
- CosMx-Cell-Profiles - [R] - Collection of reference datasets for CosMx SMI
- GARDEN - [Python] - Graph-based dynamic attention framework for identifying rare pathogenic cell populations (disease-driving cells often missed by standard methods), enables 3D tissue reconstruction
- RCTD - [R] - Robust cell type decomposition
- Cell2location - [Python] - Mapping scRNA-seq to spatial data
- SPOTlight - [R] - Seeded NMF regression to deconvolute spatial spots
- CARD - [R] - Spatially informed cell-type deconvolution
- FlashDeconv - [R] - High-performance deconvolution using randomized sketching, achieves linear O(N) scaling for Visium HD and other high-resolution platforms
- C-SIDE - [R] - Cell type-Specific Inference of Differential Expression in spatial transcriptomics
- Niche-DE - [R] - Niche-differential gene expression analysis identifying context-dependent cell-cell interactions
- smiDE - [R] - Spatial differential expression method | GitHub
- spatialGE - [R] - Spatial gene expression analysis
- Vespucci - [R] - Prioritize spatial regions involved in the response to an experimental perturbation in spatial transcriptomics
- CSDE - [Python] - Corrected Spatial Differential Expression using Prediction-Powered Inference to account for preprocessing uncertainties (segmentation, quantification, cell typing)
- PROST - [Python] - Detection of spatially variable genes
- SpatialDE - [Python] - Spatial differential expression analysis
- SPARK-X - [R] - Detection of spatially variable genes, best performing
- Hotspot - [Python] - Identify informative gene modules with lowest false positive rate
- SOMDE - [Python] - Self-organizing map for spatially variable gene detection with optimization
- trendsceek - [R] - Identification of spatial expression trends
- nnSVG - [R] - Scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
- SLOPER - [Python] - Score-based learning of Poisson-modeled expression rates for spatial gene modules and tissue organization patterns
- PRECAST - [R] - Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data
- MISO - [Python] - MultI-modal Spatial Omics for versatile feature extraction and clustering integrating multiple modalities including gene expression, protein, epigenetics, metabolomics, and histology
- SIMO - [Python] - Spatial Integration of Multi-Omics through probabilistic alignment integrating spatial transcriptomics with multiple single-cell modalities
- GSI - [Python] - Gene Spatial Integration using deep learning with representation learning to extract spatial distribution of genes | GitHub | Zenodo
- SPACE-seq - [Paper] - Unified molecular approach for spatial multiomics enabling simultaneous analysis of chromatin accessibility, mitochondrial DNA mutations, and gene expression on standard 10× Genomics Visium CytAssist platform
- LLOKI - [Python] - Cross-platform spatial transcriptomics integration using optimal transport and scGPT foundation models for unified features across different gene panels (RECOMB 2025)
(Smaller) Cell types → Cell modules/neighborhoods → Niches/tissue domains (Larger)
- BANKSY - [R/Python] - Unified cell typing and tissue domain segmentation
- CellCharter - [Python] - Hierarchical niche detection
- SpatialGLUE - [Python] - Multi-omics cell niche identification
- smoothclust - [R] - Spatial clustering
- SpaTopic - [R] - Spatial topic modeling
- hdWGCNA - [R] - Weighted gene correlation network analysis
- GASTON - [Python] - Graph-based spatial domain detection
- SpatialMNN - [R] - Identification of shared niches between slides
- NicheCompass - [Python] - End-to-end analysis of spatial multi-omics data
- Proust - [Python] - Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies (multi-modal domains)
- STAMP - [Python] - Spatial Transcriptomics Analysis with topic Modeling, provides interpretable dimension reduction through deep generative modeling discovering tissue domains and cellular communication patterns
- DeepGFT - [Python] - Combines deep learning with graph Fourier transform for spatial domain identification | GitHub | Zenodo
- novae - [Python] - Deep learning framework for spatial domain detection and tissue organization analysis
- CRAWDAD - [R] - Cell relationship analysis with directional adjacency distributions
- HoodscanR - [R] - Neighborhood analysis
- SpicyR - [R] - Spatial analysis in R
- MISTy - [R] - Explainable multiview framework for dissecting spatial relationships from highly multiplexed data
- SpatialCorr - [Python] - Identifying gene sets with spatially varying correlation structure
- CatsCradle - [R] - Spatial analysis framework for tissue neighbourhoods
- spaTrack - [Python] - Spatial trajectory analysis
- scSpace - [Python] - Reconstruction of cell pseudo-space from single-cell RNA sequencing data
- SOCS - [Python] - Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport
- STORIES - [Python] - Spatiotemporal Reconstruction Using Optimal Transport for cell trajectory inference from spatial transcriptomics profiled at multiple time points
- Spatia - [Python] - Spatial cell-cell interaction analysis
- CellAgentChat - [Python] - Agent-based cell communication modeling
- SpaTalk - [R] - Knowledge-graph-based cell-cell communication inference
- SpaOTsc - [Python] - Inferring spatial and signaling relationships between cells
- MISTy - [R] - Explainable multi-view framework for dissecting intercellular signaling
- DeepLinc - [Python] - De novo reconstruction of cell interaction landscapes
- CellChat - [R] - Inferrence of cell-cell communication from multiple spatially resolved transcriptomics datasets
- COMMOT - [Python] - Screening cell-cell communication in spatial transcriptomics via collective optimal transport
- NicheNet - [R] - Linking ligands to downstream target gene regulation
- DeepTalk - [Python] - Single-cell resolution cell-cell communication using deep learning
- CellNEST - [Python] - Cell–cell relay networks using attention mechanisms on spatial transcriptomics
- FlowSig - [Python] - Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics
- SuperSpot - [R] - Metacell analysis for spatial data
- SEraster - [R] - Rasterization method for spatial data processing
- Sprawl - [Python] - Subcellular transcript localization
- Bento - [Python] - Python toolkit for subcellular analysis of spatial transcriptomics data
- FISHfactor - [Python] - Analysis of subcellular transcript patterns
- InSTAnT - [Python] - Intracellular spatial transcript analysis
- troutpy - [Python] - Analysis of transcripts outside segmented cells in spatial transcriptomics data
- CalicoST - [Python] - CNV detection in spatial data
- inSituCNV - [Python] - Inference of Copy Number Variations in Image-Based Spatial Transcriptomics
- SPLISOSM - [Python] - Spatial isoform statistical modeling for detecting isoform-resolution patterns (alternative splicing, polyadenylation) from spatial transcriptomics data | Paper | Docs
- STAN - [R] - Spatial transcription factor analysis
- PASTE/PASTE2 - [Python] - Probabilistic alignment of spatial transcriptomics experiments
- SPIRAL - [R] - Integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
- TOAST - [Python] - Topography Aware Optimal Transport for Alignment of Spatial Omics Data
- STalign - [Python] - Alignment of spatial transcriptomics data using diffeomorphic metric mapping
- SANTO - [Python] - A coarse-to-fine alignment and stitching method for spatial omics
- TESLA - [Python] - Super resolution for 10X Visium
- istar - [Python] - Super resolution for Visium
- BayesSPACE - [R] - Subspot resolution
- Spotiphy - [Python] - Super resolution tool for spatial data
- ST-Net - [Python] - Integrating spatial gene expression and tumor morphology via deep learning
- SpaceDIVA - [Python] - Integration of transcript data with histological images
- HEST - [Python] - Dataset for Spatial Transcriptomics and Histology Image Analysis
- CellLENS - [Python] - Cell Local Environment Neighborhood Scan
- DeepSpot - [Python] - Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images
- SpotWhisperer - [Python] - Molecularly informed analysis of histopathology images using natural language
- STPath - [Python] - A Generative Foundation Model for Integrating Spatial Transcriptomics and Whole Slide Images
- AESTETIK - [Python] - AutoEncoder for Spatial Transcriptomics Expression with Topology and Image Knowledge
- Deconvolution benchmark - [Paper] - Comprehensive comparison
- RCTD and Cell2location benchmark - [Paper] - Claims these are the best methods
- Spatial clustering benchmark - [Paper] - Comparison of clustering methods
- Spatialbench - [Python] - Benchmark for evaluating AI agents on spatial biology analysis tasks
- Nature Communications review - [Paper] - Confirms Cell2location performance
- Open problems benchmark - [Web] - Cell2location is top performer
- Neighborhod benchmark - [Paper] - New COZI method top performer
- Kaiko.ai FM benchmark EVA - [Python] - WSI benchmark
- Benchmarking of spatial transcriptomics platforms across six cancer types - [Paper] - Comprehensive platform comparison
- PathBench - [Python] - Pathology benchmark
- SPATCH Benchmark - 2025 - [Paper] - Showing Xenium performs best
- Thunder - [Python] - Pathology benchmark
- HISSTA - [Python/R] - Histopathology spatial transcriptomics dataset
- STOmicsDB - [Web] - Spatial transcriptomics database
- HistAI Pathology Datahub - [Python] - Skills repo / HistAI Whole Slide Image Data Hub
- KRONOS - [Python] - Foundation Model for Multiplex Spatial Proteomic Images
- scGPT-spatial - [Python] - Language model for spatial transcriptomics
- Phikon-v2 - [Python] - Spatial biology foundation model
- Bioptimus H-optimus-0 - [Python] - Biology-focused foundation model
- Bioptimus H-optimus-1 - [Python] - Latest biology-focused foundation model from Bioptimus
- DeepCell dataset - [Web] - CNN + human features embeddings
- TITAN - [Python] - A multimodal whole-slide foundation model for pathology
- Virchow - [Python] - Foundation model for computational pathology
- UNI and UNI2 - [Python] - Universal pathology foundation models
- CONCH - [Python] - Contrastive learning for histopathology
- GIGApath - [Python] - Large-scale pathology foundation model
- OmiCLIP - [Python] - A visual–omics foundation model to bridge histopathology with spatial transcriptomics
- Nicheformer - [Python] - Transformer-based foundation model pretrained on SpatialCorpus-110M containing over 110 million cells for spatial composition and label prediction | GitHub
- FOCUS - [Python] - Foundational generative model for cross-platform unified ST enhancement conditioned on H&E images, scRNA-seq references, and spatial co-expression priors (trained on 1.7M H&E-ST pairs, 10 platforms)
- Atlas 2 - [Python] - Foundation models for clinical deployment