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Table 2 The analytical tools for ligand-receptor interactions at single cell level

From: Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

Tools Type Algorithm analysis rationale Databases Ligand-receptor complexs Application Author
General analysis
 ProximID [69] Software Expression level No No Build a cellular network based on physical cell interaction and single-cell mRNA sequencing, discover new preferential cellular interactions without prior knowledge of component cell types Jean-Charles Boisset et al.
 iTALK [70] R package Expression level No No Characterize and illustrate intercellular communication signals in the multicellular tumor ecosystem using single-cell RNA sequencing data Yuanxin Wang et al.
 PyMINEr [81] Python package Expression level No No Detection of autocrine-paracrine signaling networks Scott R. Tyler et al.
 scTensor [82] R package Tensor decomposition Yes No Detect some hypergraphs includingparacrine/autocrine cell–cell interactions patterns, which cannot be detected by previous methods Koki Tsuyuzaki et al.
 SoptSC [83] R package Cell–cell similarity matrix Yes No Predict cell–cell communication networks, enabling reconstruction of complex cell lineages that include feedback or feedforward interactions Shuxiong Wang et al.
 cellTalker [71] R package Differentially expressed genes No No Evaluate cell–cell communication Anthony R Cillo et al.
 CellPhoneDB [46] Python package Expression level Yes Yes Predict enriched cellular interactions between two cell types from single-cell transcriptomics data Mirjana Efremova et al.
 SingleCellSignalR [45] R package Expression level Yes No Provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways Simon Cabello-Aguilar et al.
Signal pathways
 NicheNet [75] R package Weighting network No No Infering ligands and their gene regulatory effects Robin Browaeys et al.
 CellChat [77] R package Weighting network Yes Yes Predict major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches Suoqin Jin et al.
Spatical cellular communication
 SpaOTsc [79] Python package Spatial cell–cell distance and average enrichment of genes No No (1) infer space-constrained cell–cell communications, (2) infer spatial distance for intercellular signaling, and (3) construct a spatial map of intercellular gene–gene regulatory information flow Zixuan Cang et al.
CSOmap [78] Matlab package Abundance of interacting ligands and receptors, and their affinity No No Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly Xianwen Ren et al.
 PIC-seq [80] Sequencing technology Sequencing physically interacting cells No No Map in situ cellular interactions and characterizes their molecular crosstalk Amir Giladi et al.
  1. Databases: if there are databases constructed for these tools
  2. Ligand-receptor complexs: if the structures of ligand-receptor comlpexs were considered by the tools