SEnSCA:识别可能的联体受体相互作用及其在细胞间通信推断中的应用
Liqian Zhou1, Xiwen Wang1, Lihong Peng1
1School of Life Sciences and Chemistry, Hunan University of Technology, Hunan, China.
Journal of cellular and molecular medicine
|May 15, 2024
概括
SEnSCA是一种新的工具,通过从单细胞RNA测序和蛋白质组数据中识别潜在的联体受体相互作用 (LRIs),推断细胞-细胞通信 (CCC),改进CCC网络分析.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 多细胞生物依赖复杂的细胞-细胞通信 (CCC) 进行协调活动.
- 目前推断CCC的方法仅限于已知的联体受体相互作用 (LRIs).
研究的目的:
- 开发一个全面的计算工具,SENSCA,通过整合单细胞RNA测序和蛋白质组数据来分析CCC.
- 通过发现潜在的LRI,克服现有方法的局限性.
主要方法:
- 在特征提取和尺寸缩小后,SENSCA采用一个堆叠组合模型 (SVM,1D-CNN,多头注意力) 进行潜在的LRI获取.
- 负的LRI样本是使用K-means集群生成的.
- 通过LRI过和三点估计方法结合单细胞RNA测序数据来评估CCC强度.
主要成果:
- 与现有方法相比,SENSCA在预测潜在的LRI方面表现出卓越的表现,实现了更高的精度,回忆,准确性,F1得分,AUC和AUPR.
- 该工具提供了三个可视化选项 (热图,泡图,网络图),以清晰地表示CCC网络.
- 对人类黑色素瘤组织的应用证实了SENSCA在CCC检测中的可靠性.
结论:
- SEnSCA提供了一种强大而通用的工具,用于推断细胞-细胞通信网络.
- 该方法增强了潜在的联体受体相互作用的发现,促进了对细胞协调的理解.
- SEnSCA是免费的,促进生物研究的更广泛的可访问性.
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