paper.bib

@article{berg_ilastik_2019,
  title        = {ilastik: interactive machine learning for (bio)image analysis},
  volume       = {16},
  rights       = {2019 The Author(s), under exclusive licence to Springer Nature America, Inc.},
  issn         = {1548-7105},
  url          = {https://www.nature.com/articles/s41592-019-0582-9},
  doi          = {10.1038/s41592-019-0582-9},
  shorttitle   = {ilastik},
  abstract     = {We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than {RAM}. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.},
  pages        = {1226--1232},
  number       = {12},
  journaltitle = {Nature Methods},
  shortjournal = {Nat Methods},
  author       = {Berg, Stuart and Kutra, Dominik and Kroeger, Thorben and Straehle, Christoph N. and Kausler, Bernhard X. and Haubold, Carsten and Schiegg, Martin and Ales, Janez and Beier, Thorsten and Rudy, Markus and Eren, Kemal and Cervantes, Jaime I. and Xu, Buote and Beuttenmueller, Fynn and Wolny, Adrian and Zhang, Chong and Koethe, Ullrich and Hamprecht, Fred A. and Kreshuk, Anna},
  urldate      = {2023-07-28},
  date         = {2019-12},
  langid       = {english},
  note         = {Number: 12
                  Publisher: Nature Publishing Group},
  keywords     = {Image processing, Machine learning, Software}
}

@article{marconato_spatialdata_2024,
  title        = {{SpatialData}: an open and universal data framework for spatial omics},
  rights       = {2024 The Author(s)},
  issn         = {1548-7105},
  url          = {https://www.nature.com/articles/s41592-024-02212-x},
  doi          = {10.1038/s41592-024-02212-x},
  shorttitle   = {{SpatialData}},
  abstract     = {Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce {SpatialData}, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. {SpatialData} facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.},
  pages        = {1--5},
  journaltitle = {Nature Methods},
  shortjournal = {Nat Methods},
  author       = {Marconato, Luca and Palla, Giovanni and Yamauchi, Kevin A. and Virshup, Isaac and Heidari, Elyas and Treis, Tim and Vierdag, Wouter-Michiel and Toth, Marcella and Stockhaus, Sonja and Shrestha, Rahul B. and Rombaut, Benjamin and Pollaris, Lotte and Lehner, Laurens and Vöhringer, Harald and Kats, Ilia and Saeys, Yvan and Saka, Sinem K. and Huber, Wolfgang and Gerstung, Moritz and Moore, Josh and Theis, Fabian J. and Stegle, Oliver},
  urldate      = {2024-04-09},
  date         = {2024-03-20},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Computational platforms and environments, Data integration, Molecular imaging, Software}
}

@article{virshup_scverse_2023,
  title        = {The scverse project provides a computational ecosystem for single-cell omics data analysis},
  volume       = {41},
  rights       = {2023 The Author(s), under exclusive licence to Springer Nature America, Inc.},
  issn         = {1546-1696},
  url          = {https://www.nature.com/articles/s41587-023-01733-8},
  doi          = {10.1038/s41587-023-01733-8},
  pages        = {604--606},
  number       = {5},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  author       = {Virshup, Isaac and Bredikhin, Danila and Heumos, Lukas and Palla, Giovanni and Sturm, Gregor and Gayoso, Adam and Kats, Ilia and Koutrouli, Mikaela and Berger, Bonnie and Pe’er, Dana and Regev, Aviv and Teichmann, Sarah A. and Finotello, Francesca and Wolf, F. Alexander and Yosef, Nir and Stegle, Oliver and Theis, Fabian J.},
  urldate      = {2024-06-06},
  date         = {2023-05},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Bioinformatics, Computational platforms and environments, Machine learning, Software}
}

@article{armingol_diversification_2024,
  title        = {The diversification of methods for studying cell–cell interactions and communication},
  volume       = {25},
  rights       = {2024 Springer Nature Limited},
  issn         = {1471-0064},
  url          = {https://www.nature.com/articles/s41576-023-00685-8},
  doi          = {10.1038/s41576-023-00685-8},
  abstract     = {No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell–cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell–cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.},
  pages        = {381--400},
  number       = {6},
  journaltitle = {Nature Reviews Genetics},
  shortjournal = {Nat Rev Genet},
  author       = {Armingol, Erick and Baghdassarian, Hratch M. and Lewis, Nathan E.},
  urldate      = {2024-06-12},
  date         = {2024-06},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Computational biology and bioinformatics, Gene expression, Molecular engineering, Transcriptomics}
}

@article{cang_screening_2023,
  title        = {Screening cell–cell communication in spatial transcriptomics via collective optimal transport},
  volume       = {20},
  rights       = {2023 The Author(s)},
  issn         = {1548-7105},
  url          = {https://www.nature.com/articles/s41592-022-01728-4},
  doi          = {10.1038/s41592-022-01728-4},
  abstract     = {Spatial transcriptomic technologies and spatially annotated single-cell {RNA} sequencing datasets provide unprecedented opportunities to dissect cell–cell communication ({CCC}). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of {CCC} remains a major challenge. Here, we present {COMMOT} ({COMMunication} analysis by Optimal Transport) to infer {CCC} in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply {COMMOT} to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial {CCC} in data with varying spatial resolutions and gene coverages. Finally, {COMMOT} identifies new {CCCs} during skin morphogenesis in a case study of human epidermal development.},
  pages        = {218--228},
  number       = {2},
  journaltitle = {Nature Methods},
  shortjournal = {Nat Methods},
  author       = {Cang, Zixuan and Zhao, Yanxiang and Almet, Axel A. and Stabell, Adam and Ramos, Raul and Plikus, Maksim V. and Atwood, Scott X. and Nie, Qing},
  urldate      = {2024-06-12},
  date         = {2023-02},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Cellular signalling networks, Computational models, Software, Transcriptomics}
}

@misc{zhang_integration_2023,
  title     = {Integration of Multiple Spatial Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer},
  rights    = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  url       = {https://www.biorxiv.org/content/10.1101/2023.08.28.555056v1},
  doi       = {10.1101/2023.08.28.555056},
  abstract  = {Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus {RNA}-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.},
  publisher = {{bioRxiv}},
  author    = {Zhang, Wanqiu and Spotbeen, Xander and Vanuytven, Sebastiaan and Kint, Sam and Sarretto, Tassiani and Socciarelli, Fabio and Vandereyken, Katy and Dehairs, Jonas and Idkowiak, Jakub and Wouters, David and Larizgoitia, Jose Ignacio Alvira and Partel, Gabriele and Ly, Alice and Laat, Vincent de and Mantas, Maria José Q. and Gevaert, Thomas and Devlies, Wout and Mah, Chui Yan and Butler, Lisa M. and Loda, Massimo and Joniau, Steven and Moor, Bart De and Sifrim, Alejandro and Ellis, Shane R. and Voet, Thierry and Claesen, Marc and Verbeeck, Nico and Swinnen, Johannes V.},
  urldate   = {2024-06-12},
  date      = {2023-08-28},
  langid    = {english},
  note      = {Pages: 2023.08.28.555056
               Section: New Results}
}

@misc{tang_search_2023,
  title     = {Search and Match across Spatial Omics Samples at Single-cell Resolution},
  rights    = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  url       = {https://www.biorxiv.org/content/10.1101/2023.08.13.552987v1},
  doi       = {10.1101/2023.08.13.552987},
  abstract  = {Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match, and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce {CAST} (Cross-sample Alignment of {SpaTial} omics), a deep graph neural network ({GNN})-based method enabling spatial-to-spatial searching and matching at the single-cell level. {CAST} aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. {CAST} enables spatially resolved differential analysis (ΔAnalysis) to pinpoint and visualize disease-associated molecular pathways and cell-cell interactions, and single-cell relative translational efficiency ({scRTE}) profiling to reveal variations in translational control across cell types and regions. {CAST} serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities, and disease conditions, analogous to {BLAST} in sequence alignment.},
  publisher = {{bioRxiv}},
  author    = {Tang, Zefang and Luo, Shuchen and Zeng, Hu and Huang, Jiahao and Wu, Morgan and Wang, Xiao},
  urldate   = {2024-06-12},
  date      = {2023-08-15},
  langid    = {english},
  note      = {Pages: 2023.08.13.552987
               Section: New Results}
}

@article{zhu_srtsim_2023,
  title        = {{SRTsim}: spatial pattern preserving simulations for spatially resolved transcriptomics},
  volume       = {24},
  rights       = {2023 The Author(s)},
  issn         = {1474-760X},
  url          = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02879-z},
  doi          = {10.1186/s13059-023-02879-z},
  shorttitle   = {{SRTsim}},
  abstract     = {Spatially resolved transcriptomics ({SRT})-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated {SRT} data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for {SRT} simulation as they cannot incorporate spatial information. We present {SRTsim}, an {SRT}-specific simulator for scalable, reproducible, and realistic {SRT} simulations. {SRTsim} not only maintains various expression characteristics of {SRT} data but also preserves spatial patterns. We illustrate the benefits of {SRTsim} in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.},
  pages        = {1--30},
  number       = {1},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biol},
  author       = {Zhu, Jiaqiang and Shang, Lulu and Zhou, Xiang},
  urldate      = {2024-06-12},
  date         = {2023-12},
  langid       = {english},
  note         = {Number: 1
                  Publisher: {BioMed} Central}
}

@article{song_scdesign3_2024,
  title        = {{scDesign}3 generates realistic in silico data for multimodal single-cell and spatial omics},
  volume       = {42},
  rights       = {2023 The Author(s), under exclusive licence to Springer Nature America, Inc.},
  issn         = {1546-1696},
  url          = {https://www.nature.com/articles/s41587-023-01772-1},
  doi          = {10.1038/s41587-023-01772-1},
  abstract     = {We present a statistical simulator, {scDesign}3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, {scDesign}3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.},
  pages        = {247--252},
  number       = {2},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  author       = {Song, Dongyuan and Wang, Qingyang and Yan, Guanao and Liu, Tianyang and Sun, Tianyi and Li, Jingyi Jessica},
  urldate      = {2024-06-12},
  date         = {2024-02},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Computational models, Software, Statistical methods}
}

@article{baker_silico_2023,
  title        = {In silico tissue generation and power analysis for spatial omics},
  volume       = {20},
  rights       = {2023 The Author(s)},
  issn         = {1548-7105},
  url          = {https://www.nature.com/articles/s41592-023-01766-6},
  doi          = {10.1038/s41592-023-01766-6},
  abstract     = {As spatially resolved multiplex profiling of {RNA} and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue ({IST}) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate {ISTs} in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization.},
  pages        = {424--431},
  number       = {3},
  journaltitle = {Nature Methods},
  shortjournal = {Nat Methods},
  author       = {Baker, Ethan A. G. and Schapiro, Denis and Dumitrascu, Bianca and Vickovic, Sanja and Regev, Aviv},
  urldate      = {2024-06-12},
  date         = {2023-03},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Software, Statistical methods, Transcriptomics}
}

@article{stouffer_cross-modality_2024,
  title        = {Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections},
  volume       = {15},
  rights       = {2024 The Author(s)},
  issn         = {2041-1723},
  url          = {https://www.nature.com/articles/s41467-024-47883-4},
  doi          = {10.1038/s41467-024-47883-4},
  abstract     = {This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through {LDDMM} and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from {MERFISH} and {BARseq} technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.},
  pages        = {3530},
  number       = {1},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  author       = {Stouffer, Kaitlin M. and Trouvé, Alain and Younes, Laurent and Kunst, Michael and Ng, Lydia and Zeng, Hongkui and Anant, Manjari and Fan, Jean and Kim, Yongsoo and Chen, Xiaoyin and Rue, Mara and Miller, Michael I.},
  urldate      = {2024-06-12},
  date         = {2024-04-25},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Computational neuroscience, Genetics of the nervous system}
}

@article{xia_spatial-linked_2023,
  title        = {Spatial-linked alignment tool ({SLAT}) for aligning heterogenous slices},
  volume       = {14},
  rights       = {2023 The Author(s)},
  issn         = {2041-1723},
  url          = {https://www.nature.com/articles/s41467-023-43105-5},
  doi          = {10.1038/s41467-023-43105-5},
  abstract     = {Spatially resolved omics technologies reveal the spatial organization of cells in various biological systems. Here we propose {SLAT} (Spatially-Linked Alignment Tool), a graph-based algorithm for efficient and effective alignment of spatial slices. Adopting a graph adversarial matching strategy, {SLAT} is the first algorithm capable of aligning heterogenous spatial data across distinct technologies and modalities. Systematic benchmarks demonstrate {SLAT}’s superior precision, robustness, and speed over existing state-of-the-arts. Applications to multiple real-world datasets further show {SLAT}’s utility in enhancing cell-typing resolution, integrating multiple modalities for regulatory inference, and mapping fine-scale spatial-temporal changes during development. The full {SLAT} package is available at https://github.com/gao-lab/{SLAT}.},
  pages        = {7236},
  number       = {1},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  author       = {Xia, Chen-Rui and Cao, Zhi-Jie and Tu, Xin-Ming and Gao, Ge},
  urldate      = {2024-06-12},
  date         = {2023-11-09},
  langid       = {english},
  note         = {Publisher: Nature Publishing Group},
  keywords     = {Bioinformatics, Data integration, Data mining, Machine learning, Software}
}

@misc{long_integrated_2023,
  title     = {Integrated analysis of spatial multi-omics with {SpatialGlue}},
  rights    = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  url       = {https://www.biorxiv.org/content/10.1101/2023.04.26.538404v2},
  doi       = {10.1101/2023.04.26.538404},
  abstract  = {Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. We introduce {SpatialGlue}, a graph neural network with dual-attention mechanism, to learn each modality’s significance at cross-omics and intra-omics integration. We demonstrate that {SpatialGlue} can accurately aggregate cell types into spatial domains at a higher resolution on different tissue types and technology platforms, as well as gain insights into cross-modality spatial correlations.},
  publisher = {{bioRxiv}},
  author    = {Long, Yahui and Ang, Kok Siong and Liao, Sha and Sethi, Raman and Heng, Yang and Zhong, Chengwei and Xu, Hang and Husna, Nazihah and Jian, Min and Ng, Lai Guan and Chen, Ao and Gascoigne, Nicholas {RJ} and Xu, Xun and Chen, Jinmiao},
  urldate   = {2024-06-12},
  date      = {2023-05-02},
  langid    = {english},
  note      = {Pages: 2023.04.26.538404
               Section: New Results}
}

@misc{oliveira_characterization_2024,
  title     = {Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling},
  rights    = {© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NoDerivs} 4.0 International), {CC} {BY}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},
  url       = {https://www.biorxiv.org/content/10.1101/2024.06.04.597233v1},
  doi       = {10.1101/2024.06.04.597233},
  abstract  = {Colorectal cancer ({CRC}) is the second-deadliest cancer in the world, yet a deeper understanding of spatial patterns of gene expression in the tumor microenvironment ({TME}) remains elusive. Here, we introduce the Visium {HD} platform (10x Genomics) and use it to investigate human {CRC} and normal adjacent mucosal tissues from formalin fixed paraffin embedded ({FFPE}) samples. The first assay available on Visium {HD} is a probe-based spatial transcriptomics workflow that was developed to enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised spatial clustering in Visium {HD} data that aligns with the hallmarks of colon tissue morphology and is notably improved over earlier Visium assays. Using serial sections from the same {FFPE} blocks we generate a single cell atlas of our samples, then we integrate the data to comprehensively characterize the immune cell types present in the {TME}, specifically at the tumor periphery. We observed enrichment of two pro-tumor macrophage subpopulations with differential gene expression profiles that were localized within distinct tumor regions. Further characterization of the T cells present in one of the samples revealed a clonal expansion that we were able to localize in the tissue using in situ gene expression analysis. In situ analysis also allowed us to perform in-depth characterization of the microenvironment of the clonally expanded T cell population and we identified a third macrophage subpopulation with gene expression profiles consistent with an anti-tumor response. Our study provides a comprehensive map of the cellular composition of the {CRC} {TME} and identifies phenotypically and spatially distinct immune cell populations within it. We show that the single cell-scale resolution afforded by Visium {HD} and the whole transcriptome nature of the assay allows investigations into cellular function and interaction at the tumor periphery in {FFPE} tissues, which has not been previously possible.},
  publisher = {{bioRxiv}},
  author    = {Oliveira, Michelli F. and Romero, Juan P. and Chung, Meii and Williams, Stephen and Gottscho, Andrew D. and Gupta, Anushka and Pilipauskas, Susan E. and Mohabbat, Syrus and Raman, Nandhini and Sukovich, David and Patterson, David and Team, Visium {HD} Development and Taylor, Sarah E. B.},
  urldate   = {2024-06-12},
  date      = {2024-06-05},
  langid    = {english},
  note      = {Pages: 2024.06.04.597233
               Section: New Results}
}

@misc{singhal_banksy_2022,
  title      = {{BANKSY}: A Spatial Omics Algorithm that Unifies Cell Type Clustering and Tissue Domain Segmentation},
  rights     = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  url        = {https://www.biorxiv.org/content/10.1101/2022.04.14.488259v1},
  doi        = {10.1101/2022.04.14.488259},
  shorttitle = {{BANKSY}},
  abstract   = {Each cell type in a solid tissue has a characteristic transcriptome and spatial arrangement, both of which are observable using modern spatial omics assays. However, the common practice is still to ignore spatial information when clustering cells to identify cell types. In fact, spatial location is typically considered only when solving the related, but distinct, problem of demarcating tissue domains (which could include multiple cell types). We present {BANKSY}, an algorithm that unifies cell type clustering and domain segmentation by constructing a product space of cell and neighbourhood transcriptomes, representing cell state and microenvironment, respectively. {BANKSY}’s spatial kernel-based feature augmentation strategy improves per-formance and scalability on both tasks when tested on {FISH}-based and sequencing-based spatial omics data. Uniquely, {BANKSY} identified hitherto undetected niche-dependent cell states in two mouse brain regions. Lastly, we show that quality control of spatial omics data can be formulated as a domain identification problem and solved using {BANKSY}. {BANKSY} represents a biologically motivated, scalable, and versatile framework for analyzing spatial omics data.},
  publisher  = {{bioRxiv}},
  author     = {Singhal, Vipul and Chou, Nigel and Lee, Joseph and Liu, Jinyue and Chock, Wan Kee and Lin, Li and Chang, Yun-Ching and Teo, Erica and Lee, Hwee Kuan and Chen, Kok Hao and Prabhakar, Shyam},
  urldate    = {2024-06-18},
  date       = {2022-04-15},
  langid     = {english},
  note       = {Pages: 2022.04.14.488259
                Section: New Results}
}

@article{chen_towards_2024,
	title = {Towards a general-purpose foundation model for computational pathology},
	volume = {30},
	copyright = {2024 The Author(s), under exclusive licence to Springer Nature America, Inc.},
	issn = {1546-170X},
	url = {https://www.nature.com/articles/s41591-024-02857-3},
	doi = {10.1038/s41591-024-02857-3},
	number = {3},
	urldate = {2024-06-21},
	journal = {Nature Medicine},
	author = {Chen, Richard J. and Ding, Tong and Lu, Ming Y. and Williamson, Drew F. K. and Jaume, Guillaume and Song, Andrew H. and Chen, Bowen and Zhang, Andrew and Shao, Daniel and Shaban, Muhammad and Williams, Mane and Oldenburg, Lukas and Weishaupt, Luca L. and Wang, Judy J. and Vaidya, Anurag and Le, Long Phi and Gerber, Georg and Sahai, Sharifa and Williams, Walt and Mahmood, Faisal},
	month = mar,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Machine learning, Pathology},
	pages = {850--862},
}