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轨迹推断与细胞间相互作用 (TICCI):细胞间通信提高了轨迹推断方法的准确性
Yifeng Fu1, Hong Qu2, Dacheng Qu1,3
1School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China.
Bioinformatics (Oxford, England)
|February 3, 2025
概括
轨迹推断与细胞相互作用 (TICCI) 通过整合细胞间通信来增强单细胞转录组分析. 这种方法准确地推断细胞分化轨迹,克服现有算法的局限性.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 发展生物学 发展生物学
背景情况:
- 单细胞转录组分析对于理解细胞分化和发育至关重要.
- 现有的轨迹推断算法与高维度,噪声和数据特定的生物知识要求作斗争.
- 细胞间通信在发育过程中起着至关重要的作用.
研究的目的:
- 引入轨迹推断与细胞相互作用 (TICCI),一种用于单细胞转录组分析的新方法.
- 通过在单细胞分辨率上整合细胞间通信信息来解决轨迹推断方面的挑战.
- 为了利用基因表达相似性模式作为细胞之间的生物分子信息交换的代理.
主要方法:
- 构建一个由细胞间相似性和细胞间相互作用 (CCI) 信息加权的细胞邻居矩阵.
- 使用卢瓦恩分区用于轨道分支识别和噪声减弱.
- 使用单细胞 (scEntropy) 进行分化状态评估.
- 应用Chu-Liu算法用于轨迹分支的定向最小方形建模.
- 在非连接的拓中实现了改进的扩散配合时间算法,用于在非连接的拓中进行细胞配合时间计算.
主要成果:
- 在单细胞RNA测序 (scRNA-seq) 数据集上,TICCI准确地重建了细胞分化轨迹,与已知的基因分支和基因标记者相关联.
- 使用外部标签的验证证实了CCI信息在提高轨迹推断精度方面的实用性.
- 对比分析表明,TICCI在精确的细胞时间排序方面表现优越.
- 该方法有效地减轻噪声,并评估差异化状态.
结论:
- 通过结合细胞间通信,TICCI提供了一种强大而准确的方法来推断细胞分化轨迹.
- 集成CCI信息提高了时间排序和轨迹重建的精度.
- TICCI为单细胞转录组分析提供了一个有价值的工具,克服了以前方法的局限性.
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