ArticleNucleic acids research2023
exFINDER: identify external communication signals using single-cell transcriptomics data.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Single-cell and spatial omics in liver identify cell-cell communication regulators in aging and insulin resistance.Metabolism: clinical and experimental · 2026Article
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- CausalCCC: a web server to explore intracellular causal pathways enabling cell-cell communication.Nucleic acids research · 2025Article
- Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.Nature communications · 2025Article
- Dissecting multilayer cell-cell communications with signaling feedback loops from spatial transcriptomics data.Genome research · 2025Article
- Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data.Nature protocols · 2025Review
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data with miRTalk.Genome biology · 2025Article
- Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics.Nature methods · 2024Article
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states.Nucleic acids research · 2024Article
- Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.Nature machine intelligence · 2024Article
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Abstract
Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.