Modeling intercellular communication in tissues using spatial graphs of cells

[1]  Michael I. Jordan,et al.  DestVI identifies continuums of cell types in spatial transcriptomics data , 2022, Nature Biotechnology.

[2]  S. Quake The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans , 2021, bioRxiv.

[3]  Fabian J Theis,et al.  Spatial components of molecular tissue biology , 2022, Nature Biotechnology.

[4]  Fabian J Theis,et al.  Squidpy: a scalable framework for spatial omics analysis , 2022, Nature Methods.

[5]  M. Gerstung,et al.  Cell2location maps fine-grained cell types in spatial transcriptomics , 2022, Nature Biotechnology.

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

[7]  D. Busch,et al.  Multiplexed imaging and automated signal quantification in formalin-fixed paraffin-embedded tissues by ChipCytometry , 2021, Cell reports methods.

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

[9]  S. Weissman,et al.  Spatial transcriptome profiling by MERFISH reveals fetal liver hematopoietic stem cell niche architecture , 2021, Cell Discovery.

[10]  Fabian J Theis,et al.  Graph representation learning for single-cell biology , 2021 .

[11]  L. Philipsen,et al.  Multiplexed histology analyses for the phenotypic and spatial characterization of human innate lymphoid cells , 2021, Nature Communications.

[12]  Lia S. Campos,et al.  Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro , 2021, Nature Genetics.

[13]  Guocheng Yuan,et al.  Giotto, a toolbox for integrative analysis and visualization of spatial expression data , 2020 .

[14]  Z. Bar-Joseph,et al.  GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data , 2020, Genome biology.

[15]  Z. Bar-Joseph,et al.  GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data , 2020, Genome Biology.

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

[17]  Hongkui Zeng,et al.  Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomics , 2020, bioRxiv.

[18]  Q. Nie,et al.  Inferring spatial and signaling relationships between cells from single cell transcriptomic data , 2020, Nature Communications.

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

[20]  Salil S. Bhate,et al.  Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front , 2019, Cell.

[21]  Y. Saeys,et al.  NicheNet: modeling intercellular communication by linking ligands to target genes , 2019, Nature Methods.

[22]  O. Stegle,et al.  Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis , 2018, bioRxiv.

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

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

[25]  Jason G. Cyster,et al.  A chemokine-driven positive feedback loop organizes lymphoid follicles , 2000, Nature.

[26]  T. Graham Faculty Opinions recommendation of CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. , 2022, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.