Unsupervised discovery of tissue architecture in multiplexed imaging
暂无分享,去创建一个
O. Elemento | J. Mosquera | S. Randell | André F. Rendeiro | Junbum Kim | S. Rustam | Renat Shaykhiev | A. Rendeiro
[1] Y. Shyr,et al. Scalable and model-free detection of spatial patterns and colocalization , 2022, bioRxiv.
[2] O. Elemento,et al. A Unique Cellular Organization of Human Distal Airways and Its Disarray in Chronic Obstructive Pulmonary Disease , 2022, bioRxiv.
[3] S. Tavaré,et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment , 2021, Nature Cancer.
[4] M. Yacoub,et al. Potential long-term effects of SARS-CoV-2 infection on the pulmonary vasculature: a global perspective , 2021, Nature Reviews Cardiology.
[5] G. Nolan,et al. Annotation of Spatially Resolved Single-cell Data with STELLAR , 2021, bioRxiv.
[6] O. Elemento,et al. The evolution of genomic, transcriptomic, and single-cell protein markers of metastatic upper tract urothelial carcinoma , 2021, bioRxiv.
[7] A. Nag. Survival Analysis with Python , 2021 .
[8] K. Otto,et al. 3D-mapping of human lymph node and spleen reveals integrated neuronal, vascular, and ductal cell networks , 2021 .
[9] G. Nolan,et al. Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors. , 2021, Cell systems.
[10] C. Loddenkemper,et al. Human small intestinal infection by SARS-CoV-2 is characterized by a mucosal infiltration with activated CD8+ T cells , 2021, Mucosal Immunology.
[11] Fabian J Theis,et al. Learning cell communication from spatial graphs of cells , 2021, bioRxiv.
[12] J. Hipp,et al. An Unsupervised Graph Embeddings Approach to Multiplex Immunofluorescence Image Exploration , 2021, bioRxiv.
[13] B. Roysam,et al. Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks , 2021, Nature Communications.
[14] Tyler T. Risom,et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning , 2021, Nature Biotechnology.
[15] Fabian J Theis,et al. Squidpy: a scalable framework for spatial single cell analysis , 2021, bioRxiv.
[16] Toby C. Cornish,et al. In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution , 2020, bioRxiv.
[17] Evan Z. Macosko,et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 , 2020, Nature Biotechnology.
[18] Soham Mandal,et al. SplineDist: Automated Cell Segmentation with Spline Curves , 2020, bioRxiv.
[19] André F. Rendeiro,et al. The spatial landscape of lung pathology during COVID-19 progression , 2021, Nature.
[20] Salil S. Bhate,et al. Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front , 2020, Cell.
[21] Bernd Bodenmiller,et al. A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids , 2020, bioRxiv.
[22] Axel Haverich,et al. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. , 2020, The New England journal of medicine.
[23] G. Mills,et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue , 2020, Nature Biotechnology.
[24] Zhenghao Chen,et al. Modeling Multiplexed Images with Spatial-LDA Reveals Novel Tissue Microenvironments , 2020, J. Comput. Biol..
[25] Marius Pachitariu,et al. Cellpose: a generalist algorithm for cellular segmentation , 2020, Nature Methods.
[26] Carlos Caldas,et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer , 2020, Nature Cancer.
[27] H. Moch,et al. The single-cell pathology landscape of breast cancer , 2020, Nature.
[28] Fred A. Hamprecht,et al. ilastik: interactive machine learning for (bio)image analysis , 2019, Nature Methods.
[29] Hayden Kwok-Hay So,et al. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells , 2019, bioRxiv.
[30] Salil S. Bhate,et al. Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front , 2019, Cell.
[31] Eugene W. Myers,et al. Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[32] Cameron Davidson-Pilon,et al. lifelines: survival analysis in Python , 2019, J. Open Source Softw..
[33] Peter K. Sorger,et al. Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer , 2019, Scientific Data.
[34] Ajit Singh,et al. Machine Learning With Python , 2019 .
[35] Guo-Cheng Yuan,et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+ , 2019, Nature.
[36] Shila Ghazanfar,et al. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program , 2019, Nature.
[37] Bernd Bodenmiller,et al. A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry. , 2019, Cell metabolism.
[38] Raphael Vallat,et al. Pingouin: statistics in Python , 2018, J. Open Source Softw..
[39] B. Engelhardt,et al. Joint analysis of expression levels and histological images identifies genes associated with tissue morphology , 2018, Nature Communications.
[40] Patrik L. Ståhl,et al. Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections , 2018, Nature Protocols.
[41] Vincent A. Traag,et al. From Louvain to Leiden: guaranteeing well-connected communities , 2018, Scientific Reports.
[42] Sean C. Bendall,et al. A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging , 2018, Cell.
[43] Fabian J Theis,et al. SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.
[44] Salil S. Bhate,et al. Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging , 2017, Cell.
[45] Scott M. Palmer,et al. LungMAP: The Molecular Atlas of Lung Development Program , 2017, American journal of physiology. Lung cellular and molecular physiology.
[46] M. Harrison. A Global Perspective , 2015, Bulletin of the history of medicine.
[47] Peter K. Sorger,et al. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method , 2015, Nature Communications.
[48] X. Zhuang,et al. Spatially resolved, highly multiplexed RNA profiling in single cells , 2015, Science.
[49] Emmanuelle Gouillart,et al. scikit-image: image processing in Python , 2014, PeerJ.
[50] Sean C. Bendall,et al. Multiplexed ion beam imaging of human breast tumors , 2014, Nature Medicine.
[51] J. Buhmann,et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry , 2014, Nature Methods.
[52] Qing Li,et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue , 2013, Proceedings of the National Academy of Sciences.
[53] Arthur T. Johnson,et al. Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring , 2012, Diagnostic Pathology.
[54] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[55] P. Liberali,et al. Population context determines cell-to-cell variability in endocytosis and virus infection , 2009, Nature.
[56] Julia Hirschberg,et al. V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.
[57] Joe Tien,et al. Mechanotransduction at cell-matrix and cell-cell contacts. , 2004, Annual review of biomedical engineering.
[58] William J. Godinez,et al. Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures. , 2019, Nature communications.
[59] Stephen Lynch,et al. Image Processing with Python , 2018 .
[60] Alexander Rakhlin,et al. Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis , 2018, bioRxiv.
[61] Aric Hagberg,et al. Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.
[62] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.