摘要
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.
原文 | English |
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頁(從 - 到) | 3216-3221 |
頁數 | 6 |
期刊 | Bioinformatics |
卷 | 38 |
發行號 | 12 |
DOIs | |
出版狀態 | Published - 2022 6月 15 |
All Science Journal Classification (ASJC) codes
- 統計與概率
- 生物化學
- 分子生物學
- 電腦科學應用
- 計算機理論與數學
- 計算數學