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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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从单细胞和空间转录组学数据推断路径活动,使用PaaSc.

Xiqi Liao1, Yuyang Hong1, Yan Feng1

  • 1Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.

PLoS computational biology
|November 10, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了PaaSc,一种新的计算方法来分析单个细胞中的通路活动. 这种工具有助于从复杂的转录基因数据中理解细胞异质性和疾病机制.

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科学领域:

  • 单细胞和空间转录组学
  • 计算生物学是一种计算生物学.
  • 系统生物学 系统生物学

背景情况:

  • 单细胞和空间转录组学为细胞异质性提供了前所未有的见解.
  • 将高维的转录基因数据转化为功能通路洞察力仍然是一个重大挑战.
  • 现有的方法很难在单细胞分辨率下准确推断路径活动.

研究的目的:

  • 开发一种计算方法,PaaSc (单细胞路径活动分析),以在单细胞分辨率下推断路径活动.
  • 为了能够更深入地了解细胞异质性,动态,衰老和疾病机制.
  • 提供一个强大的工具,用于分析各种转录数据模式.

主要方法:

  • PaaSc利用多重对应分析将细胞和基因投射到共享的潜在空间.
  • 路径活动得分是通过选择路径相关的维度通过线性回归推断的.
  • 在各种基准测试数据集上进行了验证,包括多组和大规模癌症scRNA-seq数据.

主要成果:

  • 与最先进的方法相比,PaaSc在各种应用中表现出更高的性能.
  • 该方法准确地得分了细胞类型特定的基因组,并确定了与衰老相关的途径.
  • PaaSc在不同数据模式 (scRNA-seq,scATAC-seq,空间转录学) 中表现出强的性能,尽管有批量效应,但仍保持了准确性.

结论:

  • PaaSc准确地捕捉动态细胞状态和空间模式,推进转录数据的分析.
  • 该方法提供了一种强大的方法来推断通路活动,这对于理解细胞动态和疾病至关重要.
  • 通过剖析细胞异质性,PaaSc可以更深入地了解衰老和复杂的疾病机制.