将LIANA和Tensor-cell2cell结合起来,在多个样本中破译细胞-细胞通信.
Hratch M Baghdassarian1, Daniel Dimitrov2, Erick Armingol1
1Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
Cell reports methods
|April 17, 2024
概括
这项研究整合了LIANA和Tensor-cell2cell工具,以进行强大的细胞-细胞通信推断. 综合方法提供了灵活的方法选择和无监督的解卷方法,用于从多样本数据集获得生物见解.
科学领域:
- 计算生物学 计算生物学
- 单细胞基因组学 单细胞基因组学
- 系统生物学 系统生物学
背景情况:
- 细胞间的通信对于理解协调的生物过程至关重要.
- 数据驱动的推断方法越来越多地用于识别通信通道.
- 现有的工具可能缺乏灵活性或需要特定的方法选择.
研究的目的:
- 整合LIANA和Tensor-cell2cell以进行增强的细胞-细胞通信分析.
- 为在多个样本中识别通信程序提供灵活和强大的工作流.
- 为了促进方法选择和无监督的生物洞察力的解.
主要方法:
- 集成LIANA和Tensor-cell2cell计算工具. 这是一个很好的方法.
- 部署多种现有方法和资源用于细胞间通信推断.
- 在Python和R中提供一步一步的分析协议,并提供在线教程.
- 无监督解卷用于总结生物学见解.
主要成果:
- 结合工作流程的演示,以实现牢固和灵活的细胞间通信识别.
- 促进明智的方法选择,以推断细胞与细胞之间的通信.
- 成功的无监督解卷,以提取和总结生物学见解.
- 从安装到可视化的完整工作流通常在大数据集的1.5小时内完成.
结论:
- 集成的LIANA和Tensor-cell2cell工作流提高了细胞-细胞通信程序的识别.
- 这种方法为多样本单细胞数据分析提供了灵活,稳健和高效的解决方案.
- 提供的教程和协议使这些计算工具的广泛采用和应用成为可能.
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