README.md

awesome spatial omics

Review papers

• Spatial landscapes of cancers: insights and opportunities
• The emerging landscape of spatial profiling technologies
• The expanding vistas of spatial transcriptomics
• Exploring tissue architecture using spatial transcriptomics
• Statistical and machine learning methods for spatially resolved transcriptomics data analysis. first author Zexian was my colleague when I was at DFCI.
• Spatial omics and multiplexed imaging to explore cancer biology
• Method of the Year: spatially resolved transcriptomics
• Computational challenges and opportunities in spatially resolved transcriptomic data analysis by Jean Fan.
• Spatial components of molecular tissue biology
• Methods and applications for single-cell and spatial multi-omics
• The dawn of spatial omics

Tutorials

• Orchestrating Spatially-Resolved Transcriptomics Analysis with Bioconductor
• Deconvolution vs Clustering Analysis for Multi-cellular Pixel-Resolution Spatially Resolved Transcriptomics Data A blog post by Jean Fan.
• Analysis, visualization, and integration of spatial datasets with Seurat

Benchmarking

• Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots.
• Robust alignment of single-cell and spatial transcriptomes with CytoSPACE
• A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics
• Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Databases

• SODB facilitates comprehensive exploration of spatial omics data [website]
• Museum of Spatial Transcriptomics

Methods

Generally grouped according to Rao et al, though several current in situ sequencing methods also rely on ex situ sequencing

Ex situ sequencing-based

• Spatial Transcriptomics - Visualization and analysis of gene expression in tissue sections by spatial transcriptomics (Note now commercialized as Visium, 10x Genomics)
• HDST - High-definition spatial transcriptomics for in situ tissue profiling
• STRS - In situ polyadenylation enables spatial mapping of the total transcriptome [code]
• Expansion Spatial Transcritptomics - Expansion spatial transcriptomics
• SlideSeq - Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
• SlideSeq2 - Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Note now commercialized as Seeker, Curio Bioscience)
• SlideTags - Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics
• StereoSeq - Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays (Note now commercialized as STOmics, MGI/BGI/Complete Genomics)
• DNA Microscopy - DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction
• Volumetric DNA Microscopy - Volumetric imaging of an intact organism by a distributed molecular network

In situ sequencing-based

• STARmap - Three-dimensional intact-tissue sequencing of single-cell transcriptional states
• STARmap PLUS - Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer’s disease
• IGS - In situ genome sequencing resolves DNA sequence and structure in intact biological samples

Imaging/hybridization-based

• MERFISH - Spatially resolved, highly multiplexed RNA profiling in single cells
• HiPR-FISH - Highly Multiplexed Spatial Mapping of Microbial Communities
• EEL-FISH - Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH

normalization

• Gene count normalization in single-cell imaging-based spatially resolved transcriptomic

• Spatial omics data analysts sometimes use the “log1p” (y=log[1+x’]) transform incorrectly. Key fact: x’ represents normalized, not raw, umis/spot counts. And it really matters how you normalize! The figure shows three normalizations of the raw count

https://twitter.com/shyam_lab/status/1698170321155850433?s=51&t=sLukUyq0ReWrcwOwgUR_XA

Computational Tools

• Sopa enables processing and analyses of image-based spatial-omics using a standard data structure and output. We currently support the following technologies: Xenium, MERSCOPE, CosMX, PhenoCycler, MACSIMA, Hyperion. Sopa was designed for generability and low-memory consumption on large images (scales to 1TB+ images).
• Monkeybread A python package developed at Immunitas to do spatial analysis for Merfish data.
• Giotto a toolbox for integrative analysis and visualization of spatial expression data
• Voyager is a package that facilitates exploratory spatial data analysis and visualization for spatial genomics data represented by SpatialFeatureExperiment objects.
• nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
• DestVI identifies continuums of cell types in spatial transcriptomics data. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org).
• Here we present spateo, a open source framework that welcomes community contributions for quantitative spatiotemporal modeling of spatial transcriptomics.
• SpaGene: Scalable and model-free detection of spatial patterns and colocalization
• Palo: Spatially-aware color palette optimization for single-cell and spatial data
• squidpy - paper - code: Squidpy: a scalable framework for spatial omics analysis
• ncem - paper - code: Learning cell communication from spatial graphs of cells
• Spatially informed cell-type deconvolution for spatial transcriptomics Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. https://github.com/YingMa0107/CARD
• Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace
• SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure
• RCTD: Robust decomposition of cell type mixtures in spatial transcriptomics
• Supervised spatial inference of dissociated single-cell data with SageNet: a graph neural network approach that spatially reconstructs dissociated single cell data using one or more spatial references. code
• SpotClean adjusts for spot swapping in spatial transcriptomics data: A quality issue in spatial transcriptomics data, and a statistical method to adjust for it. R Package.
• Nonnegative spatial factorization
• SPICEMIX: Integrative single-cell spatial modeling of cell identity
• De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc
• Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process
• Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics
• BANKSY unifies cell-type clustering and domain segmentation by constructing a product space of cells' own and microenvironment transcriptomes. R and python code.
• StereoCell - StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset
• cell2location
• STcEM - Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury

Deconvolution

• spacedeconv is a unified interface to 31 deconvolution tools with a focus on spatial transcriptomics datasets. The package is able to directly estimate cell type proportions of immune cells and can deconvolute any cell type if an annotation single-cell reference dataset is available https://github.com/omnideconv/spacedeconv

Differential expression

• A statistical method to uncover gene expression changes in spatial transcriptomics Cell type-specific inference of differential expression (C-SIDE) is a statistical model that identifies which genes (within a determined cell type) are differentially expressed on the basis of spatial position, pathological changes or cell–cell interactions.
• Niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions https://www.biorxiv.org/content/10.1101/2023.01.03.522646v1

Spatial domain

• BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis https://www.nature.com/articles/s41588-024-01664-3

Integration

• Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST
• High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE
• Search and Match across Spatial Omics Samples at Single-cell Resolution
• Alignment of spatial genomics data using deep Gaussian processes
• Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs

3D reconstruction

• Alignment and integration of spatial transcriptomics data

Clustering

• BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies

• DeepST: A versatile graph contrastive learning framework for spatially informed clustering, integration, and deconvolution of spatial transcriptomics

Cell-cell interaction

• De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc

• Modeling intercellular communication in tissues using spatial graphs of cells

Imputation

• Accurate inference of genome-wide spatial expression with iSpatial

Interactive tool

• VITESSCE Visual Integration Tool for Exploration of Spatial Single-Cell Experiments