Table 2 The analytical tools for 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. |
Sequencing | ||||||
 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. |