R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data
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Updated
Jan 6, 2024 - R
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data
NicheNet: predict active ligand-target links between interacting cells
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
LIANA+: an all-in-one framework for cell-cell communication
LIANA: a LIgand-receptor ANalysis frAmework
MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data
Learning cell communication from spatial graphs of cells
characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities
User-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins
Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data
Code used to create the core and extended GBmap, including downstream analyses (cell-cell interactions, spatial transcriptomics deconvolution) and how to produce the figures.
Collection of computational tools for cell-cell communication inference for single-cell and spatially resolved omics
R package to do the Ligand Receptor Analysis Visualization
A manually curated database of literature-supported ligand-receptor interactions in human and mouse
Spatial direct messaging detected by bivariate Moran
Single-cell RNA-seq data-based inference of multilayer inter- and intra-cellular signaling networks
A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies
Fine-grained Spatial Transcriptomics by integrating paired histology image
HoloNet. Reveal the holograph of functional communication events in spatial transcriptomics. Help understand how microenvironments shaping cellular phenotypes
SoptSC for single cell data analysis: unsupervised inference of clustering, cell lineage, pseudotime and cell-cell communication network from scRNA-seq data.
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