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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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对转录和蛋白质组数据的单细胞聚类算法的比较比较

Yu-Hang Yin1,2, Fang Wang3, Wei Li4

  • 1College of Life Science, Northeast Forestry University, Harbin, 150040, China.

Genome biology
|September 3, 2025
PubMed
概括

本研究对单细胞转录和蛋白质组数据集群的28种算法进行了基准测试,揭示了特定模式的性能和指导方法选择,以在各种omics集成场景中获得最佳结果.

关键词:
功能集成蛋白质组学单细胞聚类转录组学

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科学领域:

  • 计算生物学
  • 单细胞多细胞分析
  • 生物信息学

背景情况:

  • 由于数据分布,维度和质量的差异,单细胞数据的聚类具有挑战性.
  • 现有的集群算法主要是为特定的奥米克类型 (例如,转录组,蛋白组) 开发的,对它们的交叉模式性能和集成能力的理解有限.

研究的目的:

  • 系统地对单细胞转录和蛋白质组数据中的计算集群算法的性能进行基准测试.
  • 评估诸如高度可变基因 (HVGs) 和细胞类型颗粒度对聚类结果的影响.
  • 评估整合多种经济学数据的好处,以改善聚类,并指导方法选择.

主要方法:

  • 对10个配对单细胞转录和蛋白质组数据集的28个计算算法的比较基准分析.
  • 使用集群精度,峰值内存使用和运行时间的指标对算法性能进行评估.
  • 使用30个模拟数据集评估方法的稳定性,并分析7种整合方法的综合多态数据.

主要成果:

  • 确定了各种聚类算法的模式特有的优点和局限性.
  • 证明了不同方法的互补性,并强调了HVG和细胞类型颗粒度的影响.
  • 展示了集成转录和蛋白质组数据的聚类性能,揭示了潜在的改进.

结论:

  • 根据特定的单细胞多组数据场景和用户优先级 (例如性能,内存,时间效率) 选择适当的集群方法.
  • 推 scAIDE,scDCC 和 FlowSOM 在两种领域的最佳性能;FlowSOM 值得注意的是稳定性.
  • 建议使用scDCC和scDeepCluster来提高内存效率,使用TSCAN,SHARP和MarkovHC来提高时间效率,以及使用社区检测方法来实现平衡.