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.