作为一个系统的计算框架来评估,选择和整合细胞-细胞通信推断方法的ESICCC
Jiaxin Luo1,2, Minghua Deng3, Xuegong Zhang4
1Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
Genome research
|October 12, 2023
概括
这项研究使用新的框架ESICCC.ESICCC对细胞-细胞通信 (CCC) 推断工具进行了基准测试. RNAMagnet,CellChat和scSeqComm在配体-受体推断方面表现出色,而stMLnet和HoloNet则以空间转录学数据领先于配体/受体目标调节.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 细胞-细胞通信 (CCC) 对多细胞生物的发展和功能至关重要.
- 单细胞RNA测序 (scRNA-seq) 和空间转录组学 (ST) 的进步刺激了许多CCC推断方法的开发.
- 缺乏对这些CCC推理方法的全面比较.
研究的目的:
- 系统地比较和比较现有的CCC推理方法的性能.
- 为了确定最准确和最强大的工具,用于联结体-受体和联结体/受体-标推断.
- 为应用CCC推理的研究人员提供实用指南和资源.
主要方法:
- 开发ESICCC基准指标框架.
- 评估了18种配体-受体 (LR) 和5种配体/受体-点推断方法.
- 利用了116个不同的数据集,包括scRNA-seq,ST,扰动和细胞类型特定数据.
主要成果:
- RNAMagnet,CellChat和scSeqComm展示了从scRNA-seq数据中进行细胞间LR推断的卓越性能.
- 使用ST数据,stMLnet和HoloNet被确定为使用ST数据预测连接体/受体标调节的顶级方法.
- 确定了影响CCC推断的关键因素,包括先前交互数据,评分算法,信号复杂性和空间上下文.
结论:
- 该ESICCC框架提供了对CCC推断工具的强有力的评估.
- 根据数据类型和推断目标 (LR vs. L/R-target) 建议使用特定的方法.
- 提供了一组管道,CCCbank和决策树指南,以帮助实际应用和未来的方法开发.
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