spriyansh/awesome_spatial_omics

tools and notes for spatial omics

awesome spatial omics

review papers

• 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

tutorial

• Deconvolution vs Clustering Analysis for Multi-cellular Pixel-Resolution Spatially Resolved Transcriptomics Data A blog post by Jean Fan.

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

methods

In situ polyadenylation enables spatial mapping of the total transcriptome

tools

• Giotto a toolbox for integrative analysis and visualization of spatial expression data
• 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

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.

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

imputation

• Accurate inference of genome-wide spatial expression with iSpatial

Interactive tool

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