🌌 Spatial Transcriptomics Tools

Other resource → awesome_spatial_omics

• Spatial-omic SIB tutorial

Table of Contents

• 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
  • Transcription Factors & Gene Regulatory Networks
• Technical Enhancements
  • Slide Alignment
  • Super Resolution
  • Transcripts + Histology
• Benchmarks
• Datasets & Foundation Models
• Nextflow Pipelines

General Tools

• Best practices Bioconductor - Principles for statistical analysis of spatial transcriptomics data
• squidpy - Spatial single cell analysis toolkit from scverse
• Giotto - Comprehensive spatial data analysis suite
• Vitessce - Visual integration tool for exploration of spatial single cell experiments
• Voyager - Spatial transcriptomics visualization from Pachter lab
• BASS - Multiple sample analysis
• SpaVAE - All-purpose tool for dimension reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, and resolution enhancement
• sopa - Spatial omics processing and analysis
• SpatialAgent - An autonomous AI agent for spatial biology
• LazySlide - Framework for whole slide image (WSI) analysis
• pasta - Point pattern and lattice data analysis from Robinson lab

Nextflow / Pipelines

• nf-core/spatialxe - Nextflow pipeline for Xenium spatial transcriptomics analysis
• nf-core/sopa - Spatial Omics Pipeline Analysis (SOPA) for processing spatial transcriptomics data
• Allen Immunology Xenium Pipeline - HISE platform pipeline for Xenium data processing

Analysis Pipeline Steps

ROI Selection

• S2Omics - Designing smart spatial omics experiments with S2Omics

QC

• SpaceTrooper - Quality control for spatial transcriptomics
• GrandQC - Comprehensive solution for quality control in digital pathology
• SpotSweeper - Spatially aware quality control for spatial transcriptomics
• MerQuaCo - A computational tool for quality control in image-based spatial transcriptomics

Normalization

• Cell volume normalization - Recommended for imaging-based techniques, especially with small probe lists
• SpaNorm - Spatially-aware normalisation for spatial transcriptomics data

Gene Imputation & Denoising

• Note: Gene imputation is not recommended for deconvolution tasks
• SpaGE - Spatial gene expression prediction with best overall performance
• SpaGCN - Spatial graph convolutional network for gene correlation analysis
• Tangram - Transcript distribution prediction and spatial mapping
• SpaOTsc - Spatial imputation via optimal transport
• Seurat integration workflow - Transfer gene expression from scRNA-seq reference
• Sprod - Spatial denoising method

Bias Correction

• ResolVI - Bias correction method
• Statial - Correction of spill-over effects
• ovrl.py - A python tool to investigate vertical signal properties of imaging-based spatial transcriptomics data
• SPLIT - SPLIT effectively resolves mixed signals and enhances cell-type purity

Cell Segmentation

Imaging-based Segmentation

• Baysor - Bayesian segmentation of spatial transcriptomics data
• Cellpose - 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 - Deep learning library for single cell analysis
• Bo Wang's method - Better than SOTA segmentation (Nature Methods 2024)
• Proseg - Probabilistic segmentation method
• ComSeg - Transcript-based point cloud segmentation
• FICTURE - Feature-based image segmentation
• Xenium cell boundary - Alternative when interior staining fails
• Bioimage.io - Repository of AI models for segmentation
• ST-cellseg - Segmentation for spatial transcriptomics
• CelloType - Cell type detection and segmentation
• SAINSC - Segmentation for sequencing-based spatial data
• BIDCell - Biologically-informed deep learning for subcellular spatial transcriptomics segmentation
• FastReseg - Using transcript locations to refine image-based cell segmentation results
• Segger - Fast and accurate cell segmentation of imaging-based spatial transcriptomics data

Segmentation-free methods:

• SSAM - Subcellular segmentation-free analysis by multidimensional mRNA density
• Points2Regions - Transcript-based region identification without segmentation

VisiumHD Segmentation

• Bin2Cell - Segmentation for VisiumHD data
• ENACT - Enhanced accuracy for VisiumHD segmentation
• STHD - Cell annotation for VisiumHD

Cell Annotation

• STEM - Cell type annotation method
• TACIT - Automated cell type identification
• moscot - Optimal transport-based cell mapping
• RCTD - Cell type annotation from reference data
• Annotability - Assessment of cell annotation quality
• CELLama - Cell annotation model
• TACCO - Transfer of annotations between single-cell datasets
• TANGRAM - Mapping single-cell to spatial data
• MMoCHi - Cell annotation method
• CytoSPACE - High-resolution alignment of single-cell and spatial transcriptomes
• ABCT - Anchor-based Cell Typer
• STHD - Cell annotation for VisiumHD
• STELLAR - Annotation of spatially resolved single-cell data with STELLAR
• Vesalius - Multi-scale and multi-context interpretable mapping of cell states across heterogeneous spatial samples

Cell Deconvolution

• RCTD - Robust cell type decomposition
• Cell2location - Mapping scRNA-seq to spatial data
• SPOTlight - Seeded NMF regression to deconvolute spatial spots
• CARD - Spatially informed cell-type deconvolution

Differential Expression

• C-SIDE - Cell type-Specific Inference of Differential Expression in spatial transcriptomics
• Niche-DE - Niche-differential gene expression analysis identifying context-dependent cell-cell interactions
• Vespucci - Prioritize spatial regions involved in the response to an experimental perturbation in spatial transcriptomics

Spatially Variable Genes

• PROST - Detection of spatially variable genes
• SpatialDE - Spatial differential expression analysis
• SPARK-X - Detection of spatially variable genes, best performing
• Hotspot - Identify informative gene modules with lowest false positive rate
• SOMDE - Self-organizing map for spatially variable gene detection with optimization
• trendsceek - Identification of spatial expression trends
• nnSVG - Scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

Integration

• PRECAST - Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data

Cell Niches & Tissue Domains

(Smaller) Cell types → Cell modules/neighborhoods → Niches/tissue domains (Larger)

• BANKSY - Unified cell typing and tissue domain segmentation
• TISSUE - Transcript imputation with spatial single-cell uncertainty estimation
• CellCharter - Hierarchical niche detection
• SpatialGLUE - Multi-omics cell niche identification
• smoothclust - Spatial clustering
• SpaTopic - Spatial topic modeling
• hdWGCNA - Weighted gene correlation network analysis
• GASTON - Graph-based spatial domain detection
• SpatialMNN - Identification of shared niches between slides
• NicheCompass - End-to-end analysis of spatial multi-omics data
• Proust - Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies (multi-modal domains)

Cell Distances & Neighborhood

• CRAWDAD - Cell relationship analysis with directional adjacency distributions
• HoodscanR - Neighborhood analysis
• SpicyR - Spatial analysis in R
• MISTy - Explainable multiview framework for dissecting spatial relationships from highly multiplexed data
• SpatialCorr - Identifying gene sets with spatially varying correlation structure
• CatsCradle - Spatial analysis framework for tissue neighbourhoods

Spatial Trajectories

• spaTrack - Spatial trajectory analysis
• scSpace - Reconstruction of cell pseudo-space from single-cell RNA sequencing data
• SOCS - Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport

Cell-Cell Communication

• Spatia - Spatial cell-cell interaction analysis
• CellAgentChat - Agent-based cell communication modeling
• SpaTalk - Knowledge-graph-based cell-cell communication inference
• SpaOTsc - Inferring spatial and signaling relationships between cells
• MISTy - Explainable multi-view framework for dissecting intercellular signaling
• DeepLinc - De novo reconstruction of cell interaction landscapes
• CellChat - Inferrence of cell-cell communication from multiple spatially resolved transcriptomics datasets
• COMMOT - Screening cell-cell communication in spatial transcriptomics via collective optimal transport
• NicheNet - Linking ligands to downstream target gene regulation
• DeepTalk - Single-cell resolution cell-cell communication using deep learning
• CellNEST - Cell–cell relay networks using attention mechanisms on spatial transcriptomics
• FlowSig - Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics

Metacells & Scalability

• MetaSpot - Metacell analysis for spatial data
• SEraster - Rasterization method for spatial data processing

Subcellular Analysis

• Sprawl - Subcellular transcript localization
• Bento - Python toolkit for subcellular analysis of spatial transcriptomics data
• FISHfactor - Analysis of subcellular transcript patterns
• InSTAnT - Intracellular spatial transcript analysis

Copy Number Variations

• CalicoST - CNV detection in spatial data
• inSituCNV - Inference of Copy Number Variations in Image-Based Spatial Transcriptomics

Transcription Factors & Gene Regulatory Networks

• STAN - Spatial transcription factor analysis

Technical Enhancements

Slide Alignment

• PASTE/PASTE2 - Probabilistic alignment of spatial transcriptomics experiments
• SPIRAL - Integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
• TOAST - Topography Aware Optimal Transport for Alignment of Spatial Omics Data
• STalign - Alignment of spatial transcriptomics data using diffeomorphic metric mapping
• SANTO - A coarse-to-fine alignment and stitching method for spatial omics

Super Resolution

• TESLA - Super resolution for 10X Visium
• istar - Super resolution for Visium
• BayesSPACE - Subspot resolution
• Spotiphy - Super resolution tool for spatial data

Transcripts + Histology

• ST-Net - Integrating spatial gene expression and tumor morphology via deep learning
• SpaceDIVA - Integration of transcript data with histological images
• HEST - Dataset for Spatial Transcriptomics and Histology Image Analysis
• CellLENS - Cell Local Environment Neighborhood Scan
• DeepSpot - Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images
• SpotWhisperer - Molecularly informed analysis of histopathology images using natural language
• STPath - A Generative Foundation Model for Integrating Spatial Transcriptomics and Whole Slide Images
• AESTETIK - AutoEncoder for Spatial Transcriptomics Expression with Topology and Image Knowledge

Benchmarks

• Deconvolution benchmark - Comprehensive comparison
• RCTD and Cell2location benchmark - Claims these are the best methods
• Spatial clustering benchmark - Comparison of clustering methods
• Nature Communications review - Confirms Cell2location performance
• Open problems benchmark - Cell2location is top performer
• Neighborhod benchmark - New COZI method top performer
• Kaiko.ai FM benchmark EVA - WSI benchmark
• Benchmarking of spatial transcriptomics platforms across six cancer types - Comprehensive platform comparison
• PathBench - Pathology benchmark
• SPATCH Benchmark - 2025 - Showing Xenium performs best

Datasets & Foundation Models

Datasets

• HISSTA - Histopathology spatial transcriptomics dataset
• STOmicsDB - Spatial transcriptomics database

Foundation Models

• KRONOS - Foundation Model for Multiplex Spatial Proteomic Images
• scGPT-spatial - Language model for spatial transcriptomics
• Phikon-v2 - Spatial biology foundation model
• Bioptimus H-optimus-0 - Biology-focused foundation model
• Bioptimus H-optimus-1 - Latest biology-focused foundation model from Bioptimus
• DeepCell dataset - CNN + human features embeddings
• Virchow - Foundation model for computational pathology
• UNI and UNI2 - Universal pathology foundation models
• CONCH - Contrastive learning for histopathology
• GIGApath - Large-scale pathology foundation model
• OmiCLIP - A visual–omics foundation model to bridge histopathology with spatial transcriptomics