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DiCoLo:在单细胞数据中检测局部差异性基因共同表达的无集成和无集群检测
Ruiqi Li1,2, Junchen Yang1,2, Pei-Chun Su3
1Computational Biology & Biomedical Informatics Program, Yale University, New Haven, CT, USA.
bioRxiv : the preprint server for biology
|December 17, 2025
概括
迪科洛可以检测特定细胞社区内基因协调的变化,从而改善单细胞分析. 这种方法可以识别差异性基因程序,而无需集群或对齐,即使有批量效应.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 单细胞分析 单细胞分析
背景情况:
- 鉴定基因协调和细胞群的变化对于单细胞分析至关重要.
- 目前的方法与小子群和批量效应作斗争,限制了发现.
研究的目的:
- 介绍DiCoLo,一个新的框架来检测在单细胞数据中基因的差异性同定位.
- 解决现有方法在识别基因协调局部变化的局限性.
主要方法:
- DiCoLo使用最佳运输距离构建基因图,以捕捉基因同定位模式.
- 它通过分析跨条件的基因图连接的变化来识别差异性基因程序.
- 该框架在不需要细胞聚类或交叉条件对齐的情况下运行.
主要成果:
- DiCoLo可稳定地识别差异性基因同定位,在基准数据集上表现优于现有的方法.
- 该框架有效地处理弱信号和复杂的批量效应.
- 应用于小鼠毛囊发育,DiCoLo揭示了与形态原信号相关的协调基因程序.
结论:
- DiCoLo是一个强大的新框架,用于在单细胞数据中发现局部差异化转录协调.
- 它增强了在特定细胞群体内识别微妙生物变化的能力.
- 该方法为分析复杂的单细胞数据集提供了强大的替代方案.
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