SpatialDE: identification of spatially variable genes

Svensson, Valentine, Teichmann, Sarah A, Stegle, Oliver·2018·Nature Methods

Cited by (710)

Items 101–200 of 710

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  3. SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
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  4. Metastatic heterogeneity of the consensus molecular subtypes of colorectal cancer
    Eide, Peter W., Moosavi, Seyed H., Eilertsen, Ina A., Brunsell, Tuva H., Langerud, Jonas, Berg, Kaja C. G., Røsok, Bård I., Bjørnbeth, Bjørn A., Nesbakken, Arild, Lothe, Ragnhild A. et al. · 2021 · npj Genomic Medicine
  5. Identification of spatially variable genes with graph cuts
    Ke Zhang, Wanwan Feng, Peng Wang · 2022 · Nature Communications
  6. Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease
    Xinyi Zhang, Xiao Wang, G. V. Shivashankar, Caroline Uhler · 2022 · Nature Communications
  7. Dependency-aware deep generative models for multitasking analysis of spatial omics data
    Tian Tian, Jie Zhang, Xiang Lin, Zhi Wei, Hakon H. Hakonarson · 2024 · Nature Methods
  8. sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling
    Alma Andersson, Joakim Lundeberg · 2021 · Bioinformatics
  9. Spatial transcriptomics in development and disease
    Ran Zhou, Gaoxia Yang, Yan Zhang, Yuan Wang · 2023 · Molecular Biomedicine
  10. Scalable and model-free detection of spatial patterns and colocalization
    Qi Liu, Chih–Yuan Hsu, Yu Shyr · 2022 · Genome Research
  11. Bayesian modeling of spatial molecular profiling data via Gaussian process
    Qiwei Li, Minzhe Zhang, Yang Xie, Guanghua Xiao · 2021 · Bioinformatics
  12. Enablers and challenges of spatial omics, a melting pot of technologies
    Theodore Alexandrov, Julio Sáez-Rodríguez, Sinem K. Saka · 2023 · Molecular Systems Biology
  13. Deep learning in spatially resolved transcriptomics: a comprehensive technical view
    Roxana Zahedi, Mohammad Reza Eftekhariyan Ghamsari, Ahmadreza Argha, Callum MacPhillamy, Amin Beheshti, Roohallah Alizadehsani, Nigel Hamilton Lovell, Mohammad Lotfollahi, Hamid Alinejad‐Rokny · 2024 · Briefings in Bioinformatics
  14. Mapping the topography of spatial gene expression with interpretable deep learning
    Uthsav Chitra, Brian J. Arnold, Hirak Sarkar, Kohei Sanno, Cong Ma, Sereno L. Lopez-Darwin, Benjamin J. Raphael · 2025 · Nature Methods
  15. Single‐Cell Sequencing Methodologies: From Transcriptome to Multi‐Dimensional Measurement
    Yingwen Chen, Jia Tao Song, Qingyu Ruan, Xi Zeng, Lingling Wu, Linfeng Cai, Xuanqun Wang, Chaoyong James Yang · 2021 · Small Methods
  16. Modeling zero inflation is not necessary for spatial transcriptomics
    Peiyao Zhao, Jiaqiang Zhu, Ying Ma, Xiang Zhou · 2022 · Genome biology
  17. Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments
    Siyu Jing, He-qi Wang, Ping Lin, Jiao Yuan, Z Tang, Hong Li · 2025 · npj Precision Oncology
  18. SpatialDE2: Fast and localized variance component analysis of spatial transcriptomics
    Ilia Kats, Roser Vento‐Tormo, Oliver Stegle · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
  19. Transcriptome dynamics of CD4+ T cells during malaria maps gradual transit from effector to memory
    Megan S. F. Soon, Hyun Jae Lee, Jessica A. Engel, Jasmin Straube, Bryce S. Thomas, Clara P. S. Pernold, Lachlan S. Clarke, Pawat Laohamonthonkul, Rohit Haldar, Cameron G. Williams et al. · 2020 · Nature Immunology
  20. Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry
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  21. STARCH: copy number and clone inference from spatial transcriptomics data
    Rebecca Elyanow, Ron Zeira, Max Land, Benjamin J. Raphael · 2020 · Physical Biology
  22. Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning
    Yuzhou Chang, Fei He, Juexin Wang, Shuo Chen, Jingyi Li, Jixin Liu, Yang Yu, Li Su, Anjun Ma, Carter Allen et al. · 2022 · Computational and Structural Biotechnology Journal
  23. Voyager: exploratory single-cell genomics data analysis with geospatial statistics
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  24. Graph representation learning for single-cell biology
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  25. Emerging artificial intelligence applications in Spatial Transcriptomics analysis
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  26. SPASCER: spatial transcriptomics annotation at single-cell resolution
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  27. Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data
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  28. STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning
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  29. CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq
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  30. A Roadmap for the Human Gut Cell Atlas
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  31. Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images
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  32. Library size confounds biology in spatial transcriptomics data
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  33. SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics
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  34. Identifying signaling genes in spatial single-cell expression data
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  35. Single-cell and spatially resolved transcriptomics for liver biology
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  36. Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments
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  37. Inferring spatially transient gene expression pattern from spatial transcriptomic studies
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  38. An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus
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  40. Spatial multiplexing and omics
    Julienne L. Carstens, Santhoshi Krishnan, Arvind U.K. Rao, Anna G. Sorace, Erin H. Seeley, Sammy Ferri Borgogno, Jared K. Burks · 2024 · Nature Reviews Methods Primers
  41. STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration
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  42. Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data
    Guanao Yan, Shuo Harper Hua, Jingyi Jessica Li · 2025 · Nature Communications
  43. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
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  44. Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities
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  45. Aquila: a spatial omics database and analysis platform
    Yimin Zheng, Yitian Chen, Xianting Ding, Koon Ho Wong, Edwin Chong Wing Cheung · 2022 · Nucleic Acids Research
  46. Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks
    Xuejing Shi, Juntong Zhu, Yahui Long, Cheng Liang · 2023 · Briefings in Bioinformatics
  47. PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics
    Yuchen Liang, Guowei Shi, Runlin Cai, Yuchen Yuan, Ziying Xie, Long Yu, Yingjian Huang, Qian Shi, Lizhe Wang, Jun Li et al. · 2024 · Nature Communications
  48. Spatial transcriptomics reveals altered lipid metabolism and inflammation-related gene expression of sebaceous glands in psoriasis and atopic dermatitis
    Peter Seiringer, Christina Hillig, Alexander Schäbitz, Manja Jargosch, Anna Caroline Pilz, Stefanie Eyerich, Andrea Szegedi, Michaela Sochorová, Флориан Грубер, Christos C. Zouboulis et al. · 2024 · Frontiers in Immunology
  49. Spatial molecular profiling: platforms, applications and analysis tools
    Minzhe Zhang, Thomas Y. Sheffield, Xiaowei Zhan, Qiwei Li, Donghan M. Yang, Yunguan Wang, Shidan Wang, Yang Xie, Tao Wang, Guanghua Xiao · 2020 · Briefings in Bioinformatics
  50. Using Single-Cell and Spatial Transcriptomes to Understand Stem Cell Lineage Specification During Early Embryo Development
    Peng, Guangdun, Cui, Guizhong, Ke, Jincan, Jing, Naihe · 2020 · Annual Review of Genomics and Human Genetics
  51. The Human Cell Atlas: Technical approaches and challenges
    Chung-Chau Hon, Jay W. Shin, Piero Carninci, Michael J. T. Stubbington · 2017 · Briefings in Functional Genomics
  52. Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome
    Yingkun Zhang, Xinrui Lin, Zhixian Yao, Di Sun, Xin Lin, Xiaoyu Wang, Chaoyong James Yang, Jia Tao Song · 2022 · Computational and Structural Biotechnology Journal
  53. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses
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  54. Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace
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  55. A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics
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  56. SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq
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  57. CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis
    Yang Xu, Rachel Patton McCord · 2021 · BMC Bioinformatics
  58. Dissecting the brain with spatially resolved multi-omics
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  59. STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k -sums clustering
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  61. Spatially resolved transcriptomics: advances and applications
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  62. Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives
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  64. A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research
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  66. Selecting gene features for unsupervised analysis of single-cell gene expression data
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  67. SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning
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  68. HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
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