README.md

Resources for Spatial Transcriptomics data analysis

Data Processing

• 10X provides a dedicated pipeline for the analysis of Visium data, similar to the already existing cellranger for scRNA-seq

Integrated and Initial Data Analysis

• Squidpy: an analytical framework based on the Scanpy platform. Palla, G., Spitzer, H., Klein, M. et al. Squidpy: a scalable framework for spatial omics analysis. Nat Methods 19, 171–178 (2022). https://doi.org/10.1038/s41592-021-01358-2
• SpaceMake, an integrated snakemake pipeline for the analysis of spatial data. Tamas Ryszard Sztanka-Toth, Marvin Jens, Nikos Karaiskos, Nikolaus Rajewsky bioRxiv 2021.11.07.467598 https://doi.org/10.1101/2021.11.07.467598
• Nextflow-based spatial transcriptomics pipeline from nf-core with the main downstream analytical approaches for Visium data.

Segmentation and Territories

• Vesalius, a machine-learning approach exploiting image-analysis techniques identifies tissue anatomies based on transcriptional data. Martin P.C.N. et al., bioRxiv 2021.08.13.456235; doi: https://doi.org/10.1101/2021.08.13.456235
• SPACE: deep learning-based image segmentation approach for spatial transcriptomics.

Data Integration

• CellTrek, mapping gene expression onto spatial coordinates. Wei, R., He, S., Bai, S. et al. Spatial charting of single-cell transcriptomes in tissues. Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01233-1
• Tangram Biancalani, T., Scalia, G., Buffoni, L. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods 18, 1352–1362 (2021). https://doi.org/10.1038/s41592-021-01264-7
• MultiVI by Gayoso, A., Lopez, R., Xing, G. et al. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol 40, 163–166 (2022). https://doi.org/10.1038/s41587-021-01206-w
• DestVI for deconvolution (uses deep-learning based Variational Inference, requiring a GPU for fast computation). Lopez, R., Li, B., Keren-Shaul, H. et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01272-8
• cell2location by Kleshchevnikov, V., Shmatko, A., Dann, E. et al., Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol (2022) https://doi.org/10.1038/s41587-021-01139-4
• TACCO by Mages, S. et al., TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01657-3

Deconvolution and Reconstruction

• BayesSpace, a Bayesian model for clustering and enhancing the resolution of spatial gene expression experiments. Zhao, E., Stone, M.R., Ren, X. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat Biotechnol 39, 1375–1384 (2021). https://doi.org/10.1038/s41587-021-00935-2
• BayesPrism: a fully Bayesian approach to deconvolve the tumor microenvironment, also applicable to spatial transcriptomics data. Chu, T., Wang, Z., Pe’er, D. et al. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer (2022). https://doi.org/10.1038/s43018-022-00356-3
• NovoSpaRc: a framework for spatial tissue reconstruction starting from scRNA-seq data. introductory paper and methodological paper
• RCTD, an R package to inspect celltype admixtures in spatial transcriptomics data. Robust decomposition of cell type mixtures in spatial transcriptomics, Nat Biotechnol. https://doi.org/10.1038/s41587-021-00830-w

Further downstream analysis

• ncem by David S. Fischer, Anna C. Schaar, Fabian J. Theis, Learning cell communication from spatial graphs of cells, bioRxiv (2021), https://doi.org/10.1101/2021.07.11.451750
• C-SIDE by Dylan M. Cable, Evan Murray, Fei Chen et al., Cell type-specific inference of differential expression in spatial transcriptomics, Nature Methods (2022). https://doi.org/10.1038/s41592-022-01575-3
• CellCharter by Marco Varrone et al., CellCharter: a scalable framework to chart and compare cell niches across multiple samples and spatial -omics technologies, bioRxiv (2022). https://doi.org/10.1101/2023.01.10.523386
• COMMOT by Cang, Z. et al., Screening cell–cell communication in spatial transcriptomics via collective optimal transport Nat Methods (2023). https://doi.org/10.1038/s41592-022-01728-4
• SpiceMix by Childester, B. et al., SpiceMix enables integrative single-cell spatial modeling of cell identity Nat Genet (2023). https://doi.org/10.1038/s41588-022-01256-z

Reviews

• Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods (2022). https://doi.org/10.1038/s41592-022-01409-2
• Longo, S.K., Guo, M.G., Ji, A.L. et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 22, 627–644 (2021). https://doi.org/10.1038/s41576-021-00370-8. Nice review with a focus on studying the tumor micronvironment.
• Palla, G., Fischer, D.S., Regev, A. et al. Spatial components of molecular tissue biology. Nat Biotechnol 40, 308–318 (2022). https://doi.org/10.1038/s41587-021-01182-1
• Zeng, Z., Li, Y., Li, Y. et al. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol 23, 83 (2022). https://doi.org/10.1186/s13059-022-02653-7
• Liu, B., Li, Y., Zhang, L. Analysis and Visualization of Spatial Transcriptomic Data Front. Genet. (2022). https://doi.org/10.3389/fgene.2021.785290
• Williams, C.G. et al., An introduction to spatial transcriptomics for biomedical research Genome Medicine (2022). https://doi.org/10.1186/s13073-022-01075-1
• Liu, Z. et al. Evaluation of cell‑cell interaction methods by integrating single‑cell RNA sequencing data with spatial information Genome Biology (2022). https://doi.org/10.1186/s13059‑022‑02783‑y
• Li, H. et al., A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics Nat Commun (2023). https://doi.org/10.1038/s41467-023-37168-7

Other resources

Online workshops and explanations on several analytical approaches for spatial transcriptomics data analysis can be found here. A book on geographical data science in Python: https://geographicdata.science/book/intro.html