Cell lineage and communication network inference via optimization for single-cell transcriptomics

Wang, Shuxiong; Karikomi, Matthew; MacLean, Adam L.; Nie, Qing

doi:10.1093/nar/gkz204

Cited by 131 publications

(126 citation statements)

References 85 publications

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“…from the scRNA-seq data [6][7][8][9][10][11][12] , such as SingleCellSignalR 7 , iTALK 8 and NicheNet 11 . However, these and other similar methods usually use only one ligand /one receptor gene pairs, often neglecting that many receptors function as multi-subunit complexes.…”

Section: Several Methods Have Been Recently Developed To Infer Cell-cmentioning

confidence: 99%

Inference and analysis of cell-cell communication using CellChat

Jin¹,

Guerrero‐Juarez²,

Zhang³

et al. 2020

Preprint

Self Cite

254

417

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Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build a cell-cell communication atlas in diverse tissues.

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Section: Several Methods Have Been Recently Developed To Infer Cell-cmentioning

confidence: 99%

Inference and analysis of cell-cell communication using CellChat

Jin¹,

Guerrero‐Juarez²,

Zhang³

et al. 2020

Preprint

Self Cite

254

417

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“…Among these four methods, Zhou's and Skelly's methods are similar, which consider the gene expression levels of a ligand and its receptor separately. Based on Zhou's method 3 , for each gene i, we calculated the mean 7 h and standard deviation ! of the gene expression values across all cell types.…”

Section: Running Of Published Methodsmentioning

confidence: 99%

“…of the gene expression values across all cell types. If the average expression values of the ligand gene in cell type A and the receptor gene in cell type B are both larger than 7 h + 3 ! , this ligand-receptor pair is predicted to be active by Zhou's method.…”

Section: Running Of Published Methodsmentioning

confidence: 99%

“…We compared CytoTalk to six published algorithms, four designed for predicting ligand-receptor pairs 1-4 and two designed for predicting full signaling pathways based on known pathway annotations 7,8 . Since a comprehensive list of true ligand-receptor pairs are not available for the three cell type pairs, we first asked what fractions of the predicted ligand-receptor pairs are shared among the six methods.…”

Section: Performance Evaluation Using Spatial Transcriptomics Datamentioning

confidence: 99%

“…Due to these two features, simply examining expression levels of ligand and receptor genes cannot reliably capture the overall activities of signaling pathways and interactions among them 5,6 . As a step forward, Wang et al 7 developed SoptSC and Browaeys et al 8 developed NicheNet to identify both ligandreceptor pairs and genes downstream of them. However, these methods are based on known annotations of signaling pathways.…”

Section: Introductionmentioning

confidence: 99%

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CytoTalk:De novoconstruction of signal transduction networks using single-cell RNA-Seq data

Peng

Gao

et al. 2020

Preprint

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Single-cell technology has opened the door for studying signal transduction in a complex tissue at unprecedented resolution. However, there is a lack of analytical methods for de novo construction of signal transduction pathways using single-cell omics data. Here we present CytoTalk, a computational method for de novo constructing cell type-specific signal transduction networks using single-cell RNA-Seq data. CytoTalk first constructs intracellular and intercellular gene-gene interaction networks using an information-theoretic measure between two cell types. Candidate signal transduction pathways in the integrated network are identified using the prizecollecting Steiner forest algorithm. We applied CytoTalk to a single-cell RNA-Seq data set on mouse visual cortex and evaluated predictions using high-throughput spatial transcriptomics data generated from the same tissue. Compared to published methods, genes in our inferred signaling pathways have significantly higher spatial expression correlation only in cells that are spatially closer to each other, suggesting improved accuracy of CytoTalk. Furthermore, using single-cell RNA-Seq data with receptor gene perturbation, we found that predicted pathways are enriched for differentially expressed genes between the receptor knockout and wild type cells, further validating the accuracy of CytoTalk. In summary, CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

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From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients

Guo,

Li,

Gong

et al. 2024

Advanced Science

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With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high‐throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high‐throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi‐omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

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Cell lineage and communication network inference via optimization for single-cell transcriptomics

Cited by 131 publications

References 85 publications

Inference and analysis of cell-cell communication using CellChat

Inference and analysis of cell-cell communication using CellChat

CytoTalk:De novoconstruction of signal transduction networks using single-cell RNA-Seq data

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients

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