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01855nam a2200169Ia 4500 |
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10.1093-bioinformatics-btac294 |
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220718s2022 CNT 000 0 und d |
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|a 13674803 (ISSN)
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245 |
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|a Dysregulated ligand-receptor interactions from single-cell transcriptomics
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260 |
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|b Oxford University Press
|c 2022
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|z View Fulltext in Publisher
|u https://doi.org/10.1093/bioinformatics/btac294
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|a 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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|a Hsu, C.-Y.
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|a Li, J.
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|a Liu, Q.
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|a Shyr, Y.
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773 |
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|t Bioinformatics
|x 13674803 (ISSN)
|g 38 12, 3216-3221
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