Squidpy: a scalable framework for spatial omics analysis

[1]  Evan Z. Macosko,et al.  Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram , 2021, Nature Methods.

[2]  P. Kharchenko,et al.  Cell segmentation in imaging-based spatial transcriptomics , 2021, Nature Biotechnology.

[3]  B. Göttgens,et al.  Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis , 2021, Nature biotechnology.

[4]  Raphael Gottardo,et al.  Spatial transcriptomics at subspot resolution with BayesSpace , 2021, Nature Biotechnology.

[5]  J. Saez-Rodriguez,et al.  Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data , 2021, bioRxiv.

[6]  M. Carlén,et al.  Spatial Transcriptomics: Molecular Maps of the Mammalian Brain. , 2021, Annual review of neuroscience.

[7]  Joakim Lundeberg,et al.  sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling , 2021, Bioinform..

[8]  L. Cai,et al.  Giotto: a toolbox for integrative analysis and visualization of spatial expression data , 2021, Genome Biology.

[9]  Fabian J Theis,et al.  Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis , 2021, Molecular systems biology.

[10]  D. Pe’er,et al.  Sparcle: assigning transcripts to cells in multiplexed images , 2021, bioRxiv.

[11]  Helena L. Crowell,et al.  SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor , 2021, bioRxiv.

[12]  X. Zhuang Spatially resolved single-cell genomics and transcriptomics by imaging , 2021, Nature Methods.

[13]  J. Lundeberg,et al.  Spatially resolved transcriptomics adds a new dimension to genomics , 2021, Nature Methods.

[14]  Evan Z. Macosko,et al.  Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 , 2020, Nature Biotechnology.

[15]  Tong Li,et al.  Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics , 2020, bioRxiv.

[16]  C. Conrad,et al.  Single nucleus and in situ RNA sequencing reveals cell topographies in the human pancreas. , 2020, Gastroenterology.

[17]  Cindy C. Guo,et al.  High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue , 2020, Cell.

[18]  Soham Mandal,et al.  SplineDist: Automated Cell Segmentation with Spline Curves , 2020, bioRxiv.

[19]  Sean C. Bendall,et al.  Single-cell metabolic profiling of human cytotoxic T cells , 2020, Nature biotechnology.

[20]  Katrin Amunts,et al.  Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture , 2020, Science.

[21]  J. Lundeberg,et al.  Seamless integration of image and molecular analysis for spatial transcriptomics workflows , 2020, BMC Genomics.

[22]  T. Alexandrov Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. , 2020, Annual review of biomedical data science.

[23]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[24]  Q. Nguyen,et al.  stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues , 2020, bioRxiv.

[25]  Gabriele Partel,et al.  TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data , 2020, Bioinform..

[26]  Mirjana Efremova,et al.  CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes , 2020, Nature Protocols.

[27]  Marius Pachitariu,et al.  Cellpose: a generalist algorithm for cellular segmentation , 2020, Nature Methods.

[28]  H. Moch,et al.  The single-cell pathology landscape of breast cancer , 2020, Nature.

[29]  Roland Eils,et al.  Cell segmentation-free inference of cell types from in situ transcriptomics data , 2019, Nature Communications.

[30]  Garry Nolan,et al.  MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure , 2019, Science Advances.

[31]  Johannes L. Schönberger,et al.  SciPy 1.0: fundamental algorithms for scientific computing in Python , 2019, Nature Methods.

[32]  Guo-Cheng Yuan,et al.  Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+ , 2019, Nature.

[33]  Nimrod D. Rubinstein,et al.  Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region , 2018, Science.

[34]  Lucas Pelkmans,et al.  Multiplexed protein maps link subcellular organization to cellular states , 2018, Science.

[35]  Anne E Carpenter,et al.  CellProfiler 3.0: Next-generation image processing for biology , 2018, PLoS biology.

[36]  Eugene W. Myers,et al.  Cell Detection with Star-convex Polygons , 2018, MICCAI.

[37]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[38]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[39]  Bernd Bodenmiller,et al.  miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data , 2017, Nature Methods.

[40]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[41]  Stephan Hoyer,et al.  xarray: N-D labeled arrays and datasets in Python , 2017 .

[42]  P. Sorger,et al.  Cyclic Immunofluorescence (CycIF), A Highly Multiplexed Method for Single‐cell Imaging , 2016, Current protocols in chemical biology.

[43]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[44]  Patrik L. Ståhl,et al.  Visualization and analysis of gene expression in tissue sections by spatial transcriptomics , 2016, Science.

[45]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[46]  G. Nolan,et al.  Mass Cytometry: Single Cells, Many Features , 2016, Cell.

[47]  Siu Kwan Lam,et al.  Numba: a LLVM-based Python JIT compiler , 2015, LLVM '15.

[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]  J. Buhmann,et al.  Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry , 2014, Nature Methods.

[51]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[52]  A. Getis The Analysis of Spatial Association by Use of Distance Statistics , 2010 .

[53]  S. Schultz Principles of Neural Science, 4th ed. , 2001 .

[54]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[55]  Sergio J. Rey,et al.  PySAL: A Python Library of Spatial Analytical Methods , 2010 .

[56]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.