awesome-cell-cell-communication

Collection of computational tools for cell-cell communication inference for single-cell and spatially resolved omics, including epigenomics, transcriptomics, proteomics, metabolomics, etc. Welcome contribution. Please folk this repository and create a pull request if you have an update.

Computational tools based on single-cell transcriptomic data

• CCCExplorer -[Java]- CCCExplorer is a java-based software that predicts and visualizes the gene signaling network to aid research on crosstalk identification in the tumor microenvironment.
• cell2cell -[python]- Tensor-cell2cell is an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells.
• CellCall -[R]- CellCall integrates paired ligand-receptor and transcription factor activities for cell-cell communication inference.
• CellChat -[R]- CellChat enables inference and analysis of cell-cell communication for systematically detecting dysregulated cell-cell communication across biological conditions.
• CellComNet -[python]- CellComNet: Integrating potential ligand-receptor interactions and single-cell RAN sequencing data for cell-cell communication inference.
• CellDialog -[python]- CellDialog: reconstruct an intercellular connectivity network based on the combined expression of ligands and receptors involved in sender and receiver cells.
• CellEnBoost -[R]- A boosting-based ligand-receptor interaction identification model for cell-to-cell communication inference.
• CellPhoneDB -[python]- CellPhoneDB is a publicly available repository of curated receptors, ligands and their interactions.
• celltalker -[R]- celltalker can infer putative ligand and receptor interactions from single-cell RNAseq data.
• CLARIFY -[python]- Multi-level Graph Autoencoder (GAE) to clarify cell cell interactions and gene regulatory network inference from spatially resolved transcriptomics.
• CommPath -[R]- CommPath is an R package for inference and analysis of ligand-receptor interactions from single cell RNA sequencing data.
• COMUNET -[R]- COMUNET uses multiplex networks to represent and cluster all potential communication pathways between cell types.
• CrossTalkeR -[R]- CrossTalkeR is a framework for network analysis and visualisation of LR networks. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological states: disease vs. homeostasis.
• CytoTalk -[R]- CytoTalk algorithm is for de novo construction of a signaling network between two cell types using single-cell transcriptomics data.
• exFINDER -[R]- exFINDER is a method that identifies external signals (received signals from the external system, exSigNet) in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways.
• GraphComm -[python]- GraphComm is a graph-based deep learning method to predict cell-cell communication in single-cell RNAseq data.
• iCELLNET -[R]- iCELLNET enables dissection of intercellular communication using the transcriptome-based framework.
• InterCellar -[R]- InterCellar is an R/Shiny app for interactive analysis and exploration of cell-cell communication based on single-cell transcriptomics data.
• iTALK -[R]- iTALK is an R toolkit for characterizing and illustrating intercellular communication.
• LRLoop -[R]- LRLoop is a full-featured R package for analyzing LR-Loops from bulk & single-cell RNA-seq data.
• MEBOCOST -[python]- MEBOCOST is a Python-based computational tool for inferring metabolite, such as lipid, mediated cell-cell communication events using single-cell RNA-seq data.
• mistyR -[R]- mistyR facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships.
• multinichenetr -[R]- multinichenetr is a R package containing multiple functionalities to computationally study cell-cell communication from single-cell transcriptomics data with complex multi-sample, multi-condition designs.
• NATMI -[python]- NATMI (Network Analysis Toolkit for Multicellular Interactions) is a Python-based toolkit for multi-cellular communication network construction and network analysis of multispecies single-cell and bulk gene expression and proteomic data.
• NeuronChat -[R]- The goal of NeuronChat is to infer, visualize and analyze neural-specific cell-cell communication from single cell transcriptomics or spatially resolved transcriptomics data.
• NicheNet -[R]- NicheNet can study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths.
• NICHES -[R]- Niche Interactions and Cellular Heterogeneity in Extracellular Signaling.
• PyMINEr -[python]- PyMINEr can fully automate cell type identification, cell type-specific pathway analyses, graph theory-based analysis of gene regulation, and detection of autocrine-paracrine signaling networks in silico.
• RaCInG -[python]- RaCInG used patient specific bulk RNA-seq input together with non-patient specific prior knowledge on possible ligand-receptor interactions to reconstruct cell-cell interaction networks in an indivudal patient's tumour.
• RSoptSC -[R]- RSoptSC enables cell-cell communication and lineage inference for scRNA-seq data.
• scCrossTalk -[R]- scCrossTalk can find and visulize significant LR pairs between pairwise clusters and significant crosstalk between pairwise clusters for single-cell RNA-seq data.
• scDiffCom -[R]- scDiffCom stands for “single-cell Differential Communication” and infers changes in intercellular communication between two biological conditions from scRNA-seq data.
• scMLnet -[R/python]- scMLnet is an R package developed to construct inter-/intracellular multilayer singaling network based on single-cell RNA-seq expression data.
• scriabin -[R]- scriabin aims to provide a comprehensive view of cell-cell communication at the single-cell level without requiring subsampling or aggregation.
• scTenifoldXct -[python]- scTenifoldXct leverages manifold alignment of gene regression networks to detect LR-mediated cell-cell interactions, including (1) weak but biologically important interactions (2) differential interactions between two different samples
• scTensor -[R]- scTensor is a R package for detection of cell-cell interaction using Non-negative Tensor Decomposition.
• SingleCellSignalR -[R]- SingleCellSignalR is a R package to study Cell Signaling Using Single Cell RNAseq Data.
• SPRUCE -[python]- SPRUCE is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data.
• TraSig -[python]- TraSig (Trajectory-based Signalling genes inference) identifies interacting cell types pairs and significant ligand-receptors based on the expression of genes as well as the pseudo-time ordering of cells.

Computational tools based on spatially resolved transcriptomic data

• BulkSignalR -[R]- BulkSignalR is a R package to infer ligand-receptor networks with downstream pathways from bulk data or spatial data (localized bulk data).
• CCPLS -[R]- CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling) is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data.
• CellPhoneDB -[python]- CellPhoneDB allows the incorporation of spatial information of the cells to define possible pairs of interacting cells (i.e. pairs of clusters sharing/coexisting in a microenvironment).
• COMMOT -[python]- COMMOT uses collective optimal transport to infer CCC in spatial transcriptomics, which accounts for the competition among different ligand and receptor species as well as spatial distances between cells.
• DeepLinc -[python]- DeepLinc is a method for de novo reconstruction of cell interaction networks from single-cell spatial transcriptomic data based on a deep generative model of variational graph autoencoder (VGAE).
• GCNG -[python]- GCNG uses graph convolutional neural network and spaital transcriptomics data to infer cell-cell interactions.
• Giotto -[R]- Giotto introduces a two-way comparison method to identify interaction changed genes by comparing the gene expression pattern between subsets of cells within the same cell type but surrounded by different neighboring cells.
• HoloNet -[python]- Functional cell–cell communication events (FCEs) is mediated by ligand–receptor pairs and works directly for specific downstream response (expression of FCEs regulated target genes) in a particular microenvironment. HoloNet is designed for decoding FCEs using spatial transcriptomic data by integrating ligand–receptor pairs, cell-type spatial distribution and downstream gene expression into a deep learning model.
• ncem -[python]- ncem can learn cell communication from spatial graphs of cells.
• NeST -[python]- NeST can identify nested hierarchical structure in spatial transcriptomic data through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes.
• NeuronChat -[R]- The goal of NeuronChat is to infer, visualize and analyze neural-specific cell-cell communication from single cell transcriptomics or spatially resolved transcriptomics data.
• NICHES -[R]- Niche Interactions and Cellular Heterogeneity in Extracellular Signaling.
• Renoir -[python]- Renoir is an information-theory-based scoring metric for quantifying the activity between a ligand and its target gene given a specific spatial context.
• SCVA -[R/python]- SCVA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression.
• SpaCET -[R]- SpaCET is an R package for analyzing cancer spatial transcriptomics (ST) datasets to estimate cell lineage and intercellular interactions in tumor microenvironment.
• spaCI -[python]- spaCI, a novel adaptive graph model with attention mechanisms, incorporates both spatial locations and gene expression profiles of cells to identify the active ligandreceptor signaling axis across neighboring cells.
• SpaOTsc -[python]- SpaOTsc can infer space-constrained cell-cell communications, infer spatial distance for intercellular signaling, and construct a spatial map of intercellular gene-gene regulatory information flow.
• spARC -[python]- spARC, a diffusion geometric framework that integrates in situ location and gene expression information to denoise spatial transcriptomics data and identify paracrine receptor-ligand signaling interactions between cells within their spatial contexts.
• SpaTalk -[R]- SpaTalk is a spatially resolved cell-cell communication inference method relying on the graph network and knowledge graph to model ligand-receptor-target signaling network for either single-cell or spot-based spatially resolved transcriptomic data, e.g., STARmap, MERFISH, seqFISH, Slide-seq, 10X Visium.
• SpatialDM -[python]- SpatialDM is a Python package for identifying spatial co-expressed ligand and receptor using Moran's bivariant extension.
• STcomm -[R]- an R package to illustrate the spatially resolved cell interactions by combined the spatial cellular colocalization with their enriched ligand-receptor co-expression patterns inferred from both spatial and single-cell transcriptomic data.
• stMLnet -[R]- stMLnet is a tool to infer spatial intercellular communication and multilayer signaling regulations from spatial transcriptomic data (ST) by quantifying distance-weighted ligand–receptor (LR) signaling activity based on diffusion and mass action models and mapping it to intracellular targets.