ArticleCell systems2023
scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs.
Article in Cell systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.Bioinformatics (Oxford, England) · 2025Article
- scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding.PLoS computational biology · 2025Article
- 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
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- scHyper: reconstructing cell-cell communication through hypergraph neural networks.Briefings in bioinformatics · 2024Article
- Controlled noise: evidence of epigenetic regulation of single-cell expression variability.Bioinformatics (Oxford, England) · 2024Article
- scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.Cell reports. Medicine · 2024Article
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.Journal of cellular and molecular medicine · 2024Article
- Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment.Current opinion in biotechnology · 2024Review
- Suppression of FOXO1 attenuates inflamm-aging and improves liver function during aging.Aging cell · 2023Article
- Review
- Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks.Nucleic acids research · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.
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Registered trials
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.