Dysregulated ligand-receptor interactions from single-cell transcriptomics
Motivation: Intracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Altho...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Oxford University Press
2022
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Online Access: | View Fulltext in Publisher |
Summary: | Motivation: Intracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Although computational methods have been developed to infer cell type-specific ligand-receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles. Results: We developed scLR, a statistical method for examining dysregulated ligand-receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-β signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis. © 2022 The Author(s) 2022. Published by Oxford University Press. |
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ISBN: | 13674803 (ISSN) |
ISSN: | 13674803 (ISSN) |
DOI: | 10.1093/bioinformatics/btac294 |