References

Amezquita, Robert A., Aaron T. L. Lun, Etienne Becht, Vince J. Carey, Lindsay N. Carpp, Ludwig Geistlinger, Federico Marini, et al. 2020. “Orchestrating Single-Cell Analysis with Bioconductor.” Nature Methods 17: 137–45. https://doi.org/10.1038/s41592-019-0654-x.
Bressan, Dario, Giorgia Battistoni, and Gregory J. Hannon. 2023. “The Dawn of Spatial Omics.” Science 381 (6657). https://doi.org/10.1126/science.abq4964.
Canete, Nicolas P., Sourish S. Iyengar, John T. Ormerod, Heeva Baharlou, Andrew N. Harman, and Ellis Patrick. 2022. spicyR: Spatial Analysis of in Situ Cytometry Data in R.” Bioinformatics 38: 3099–3105. https://doi.org/10.1093/bioinformatics/btac268.
Hu, Jian, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee, Russell T. Shinohara, and Mingyao Li. 2021. SpaGCN: Integrating Gene Expression, Spatial Location and Histology to Identify Spatial Domains and Spatially Variable Genes by Graph Convolutional Network.” Nature Methods 18: 1342–51. https://doi.org/10.1038/s41592-021-01255-8.
Huber, Wolfgang, Vincent J. Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S. Carvalho, Hector Corrada Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nature Methods 12: 115–21. https://doi.org/10.1038/nmeth.3252.
Liu, Wei, Xu Liao, Ziye Luo, Yi Yang, Mai Chan Lau, Yuling Jiao, Xingjie Shi, et al. 2023. “Probabilistic Embedding, Clustering, and Alignment for Integrating Spatial Transcriptomics Data with PRECAST.” Nature Communications 14: 296. https://doi.org/10.1038/s41467-023-35947-w.
Lun, Aaron T. L., Davis J. McCarthy, and John C. Marioni. 2016. “A Step-by-Step Workflow for Low-Level Analysis of Single-Cell RNA-seq Data with Bioconductor.” F1000Research 5 (2122). https://doi.org/10.12688/f1000research.9501.2.
Maynard, Kristen R., Leonardo Collado-Torres, Lukas M. Weber, Cedric Uytingco, Brianna K. Barry, Stephen R. Williams, Joseph L. Catallini II, et al. 2021. “Transcriptome-Scale Spatial Gene Expression in the Human Dorsolateral Prefrontal Cortex.” Nature Neuroscience 24: 425–36. https://doi.org/10.1038/s41593-020-00787-0.
McCarthy, Davis J., Kieran R. Campbell, Aaron T. L. Lun, and Quin F. Wills. 2017. Scater: Pre-Processing, Quality Control, Normalization and Visualization of Single-Cell RNA-seq Data in R.” Bioinformatics 33 (8): 1179–86. https://doi.org/10.1093/bioinformatics/btw777.
Moses, Lambda, Pétur Helgi Einarsson, Kayla Jackson, Laura Luebbert, A. Sina Booeshaghi, Sindri Antonsson, Nicolas Bray, Páll Melsted, and Lior Pachter. 2023. Voyager: Exploratory Single-Cell Genomics Data Analysis with Geospatial Statistics.” bioRxiv. https://doi.org/10.1101/2023.07.20.549945.
Moses, Lambda, and Lior Pachter. 2022. “Museum of Spatial Transcriptomics.” Nature Methods 19: 534–46. https://doi.org/10.1038/s41592-022-01409-2.
Pardo, Brenda, Abby Spangler, Lukas M. Weber, Stephanie C. Page, Stephanie C. Hicks, Andrew E. Jaffe, Keri Martinowich, Kristen R. Maynard, and Leonardo Collado-Torres. 2022. spatialLIBD: An R/Bioconductor Package to Visualize Spatially-Resolved Transcriptomics Data.” BMC Genomics, no. 23: 434. https://doi.org/10.1186/s12864-022-08601-w.
Peters Couto, Bárbara Zita, Nicholas Robertson, Ellis Patrick, and Shila Ghazanfar. 2023. MoleculeExperiment Enables Consistent Infrastructure for Molecule-Resolved Spatial Transcriptomics Data in Bioconductor.” bioRxiv. https://doi.org/10.1101/2023.05.16.541040.
Righelli, Dario, Lukas M. Weber, Helena L. Crowell, Brenda Pardo, Leonardo Collado-Torres, Shila Ghazanfar, Aaron T. L. Lun, Stephanie C. Hicks, and Davide Risso. 2022. SpatialExperiment: Infrastructure for Spatially-Resolved Transcriptomics Data in R Using Bioconductor.” Bioinformatics 38 (11): 3128–31. https://doi.org/10.1093/bioinformatics/btac299.
Singhal, Vipul, Nigel Chou, Joseph Lee, Yifei Yue, Jinyue Liu, Wan Kee Chock, Li Lin, et al. 2024. BANKSY Unifies Cell Typing and Tissue Domain Segmentation for Scalable Spatial Omics Data Analysis.” Nature Genetics. https://doi.org/10.1038/s41588-024-01664-3.
Ståhl, Patrik L., Fredrik Salmén, Sanja Vickovic, Anna Lundmark, José Fernández Navarro, Jens Magnusson, Stefania Giacomello, et al. 2016. “Visualization and Analysis of Gene Expression in Tissue Sections by Spatial Transcriptomics.” Science 353 (6294): 78–82. https://doi.org/10.1126/science.aaf2403.
Stickels, Robert R., Evan Murray, Pawan Kumar, Jilong Li, Jamie L. Marshall, Daniela J. Di Bella, Paola Arlotta, Evan Z. Macosko, and Fei Chen. 2021. “Highly Sensitive Spatial Transcriptomics at Near-Cellular Resolution with Slide-seqV2.” Nature Biotechnology 39: 313–19. https://doi.org/10.1038/s41587-020-0739-1.
Sun, Shiquan, Jiaqiang Zhu, and Xiang Zhou. 2020. “Statistical Analysis of Spatial Expression Patterns for Spatially Resolved Transcriptomic Studies.” Nature Methods 17: 193–200. https://doi.org/10.1038/s41592-019-0701-7.
Svensson, Valentine, Sarah A. Teichmann, and Oliver Stegle. 2018. SpatialDE: Identification of Spatially Variable Genes.” Nature Methods 15: 343–46. https://doi.org/10.1038/nmeth.4636.
Weber, Lukas M., Arkajyoti Saha, Abhirup Datta, Kasper D. Hansen, and Stephanie C. Hicks. 2023. nnSVG for the Scalable Identification of Spatially Variable Genes Using Nearest-Neighbor Gaussian Processes.” Nature Communications 14: 4059. https://doi.org/10.1038/s41467-023-39748-z.
Zhao, Edward, Matthew R. Stone, Xing Ren, Jamie Guenthoer, Kimberly S. Smythe, Thomas Pulliam, Stephen R. Williams, et al. 2021. “Spatial Transcriptomics at Subspot Resolution with BayesSpace.” Nature Biotechnology 39: 1375–84. https://doi.org/10.1038/s41587-021-00935-2.
Zhu, Jiaqiang, Shiquan Sun, and Xiang Zhou. 2021. SPARK-X: Non-Parametric Modeling Enables Scalable and Robust Detection of Spatial Expression Patterns for Large Spatial Transcriptomic Studies.” Genome Biology 22: 184. https://doi.org/10.1186/s13059-021-02404-0.