Forum:Cataloguing available spatial transcriptomics tools, then linking them together into a workflow?
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3 months ago
LauferVA 4.2k

Hello Biostars,

I am interested in spatial transcriptomics (ST) data produced with nanostring technology, or with 10x genomics Xenium or Visium technologies. To get up to speed in this area, I have begun cataloguing available tools for ST (table below). But, I wanted to ask those of you with first hand experience with ST data for your insight, recommendations, etc. Questions below.

Tool        Link                                            Compatible ST platforms                                             Niches                                                      Brief Description
Seurat          https://satijalab.org/seurat/               Compatible with 10x Genomics Visium, Slide-seq, and others. Widely-used for single-cell genomics, adapted for spatial transcriptomics.  Integrates spatial information with gene expression data.
SpatialDE   https://github.com/Teichlab/SpatialDE            Agnostic to technology.    Statistical method identifying spatially variable expression genes. Uses Gaussian process framework for spatial variance.
STUtility   https://github.com/jbergenstrahle/STUtility Tailored for 10x Genomics Visium.   Tools for preprocessing, analysis, and visualization of spatial transcriptomics.    User-friendly interface, optimized for 10x Visium.
histoCAT    https://bodenmillergroup.github.io/histoCAT/    Compatible with multiplexed imaging technologies.   Interactive software for analyzing and visualizing transcriptomics data.    Analyzes cellular composition and interactions in tissues.
Giotto      https://rubd.github.io/Giotto_site/.        Works with various technologies including 10x Genomics Visium, MERFISH, Slide-seq.  Comprehensive toolkit for analysis and visualization.   Highly interactive user interface, flexible data handling.
STACAS          https://github.com/BiCroLab/stacas.         Designed for 10x Genomics Visium.   R package for normalization, analysis, and visualization.   Specializes in normalization techniques.
BayesSpace  https://bioconductor.org/packages/release/bioc/html/BayesSpace.html Compatible with 10x Genomics Visium and others. Enhances resolution of spatial transcriptomics data.    Uses Bayesian clustering to refine spatial resolution.
SpaceMakr   https://academic.oup.com/nar/article/48/7/e42/5804176   Handles all major spatial transcriptomics datasets. Processes and analyzes large-scale spatial data.    Processes multiple samples in parallel, adaptable to different methods.
SpaceRanger https://www.10xgenomics.com/products/spatial-gene-expression/   Works with FFPE, FxF, and FF tissues.   Maps the whole transcriptome in various tissues.    Designed for comprehensive transcriptome mapping across tissue types.
nf-co.re    https://nf-co.re/spatialtranscriptomics Primarily for 10X spatial data. Bioinformatics pipeline for spatial transcriptomics.    Integrates with Space Ranger for raw data processing.
Longcell    https://doi.org/10.1101/2023.02.23.529769   No specific technology listed.  Single cell and spatial alternative splicing analysis with long read sequencing.    Not specified in abstract.
STQ         https://www.biorxiv.org/content/10.1101/2023.07.27.550727v1 Designed for 10x Genomics Visium and H&E-stained WSIs.  Nextflow pipeline for Visium and H&E data from PDX samples. Optimized for PDX cancer specimens, integrates transcriptomics data with image analysis.
StereoCell  https://github.com/STOmics/StereoCell   High-resolution spatial transcriptomics.    Software for generating single-cell gene expression profiles.   High accuracy in single-cell gene expression generation.
GraphST         https://github.com/JinmiaoChenLab/GraphST   Spatial transcriptomics, incorporating spatial location information and gene expression profiles.   Versatile model for spatial clustering, integration, and deconvolution. Uses graph self-supervised contrastive learning.
Open-ST         https://www.biorxiv.org/content/10.1101/2023.07.27.550727v1 Sequencing-based, open-source experimental and computational resource.  High-resolution, cost-efficient, and 3D-scalable method.    3D scalability and focus on high resolution.
STAr        https://www.biorxiv.org/content/10.1101/2023.07.27.550727v1 Platform for identifying spatially variable genes.  Integrated platform for analysis of spatially resolved transcriptomics data.    Focus on identifying spatially variable genes.
CellCharter https://github.com/CSOgroup/cellcharter 10x Visium and Xenium, Nanostring CosMx, Vizgen MERSCOPE, Stereo-seq, DBiT-seq, MERFISH, seqFISH.   Python package for spatial transcriptomics and epigenomics data analysis.   Supports a wide range of technologies including Visium, Xenium, and NanoString.
CytoSPACE   https://github.com/digitalcytometry/cytospace   Single-cell and spatial transcriptomics data alignment. Computational tool for assigning single-cell transcriptomes to spatial data.    Solves cell/spot assignment through a correlation-based cost function.
VistoSeg    https://www.biorxiv.org/content/10.1101/2023.07.27.550727v1 High-resolution Visium/Visium-IF platforms. Processing utilities for high-resolution Visium/Visium-IF platforms.    Quantifies spatially-resolved gene expression in intact tissue sections.

My hope is to ask those of you who have worked with ST data whether you have recommendations regarding the following:

1) which software tools are best for nanostring, xenium, or visium data? 2) several of the tools listed claim to be able to work with all ST platforms. do any of the tools do a reasonably good job with all of these? 3) are there any notable omissions from this list? what would you put on here, instead? 4) Finally, in terms of knitting these tools together into an end-to-end pipeline, have you noticed combinations of tools that work well together (i tried to comment on this in the table, but I am likely missing a lot of information here).

Thank you for your input!

cytassist transcriptomics nanostring xenium visium • 389 views
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Relevant manuscripts would be helpful, too, e.g.: https://www.biorxiv.org/content/10.1101/2023.12.07.570603v1

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