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基准测试单细胞标签 oligo 解复式化方法.

George Howitt1,2, Yuzhou Feng1, Lucas Tobar1,2

  • 1Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, 3010 Australia.

NAR genomics and bioinformatics
|October 13, 2023
PubMed
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七个标签的oligo (HTO) 解复合工具被评估为单细胞RNA测序 (scRNA-seq). 性能因HTO数据质量而异,一些方法在质量较低的数据集上扎.

科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 在单细胞RNA测序 (scRNA-seq) 中样本多重化可以降低成本和批量效应.
  • 标签Oligo (HTO) 标签允许从不同样本的细胞被组合在一起并进行测序.
  • 精确的HTO数据去复数对于将细胞分配到它们的原始样本至关重要.

研究的目的:

  • 批判性地评估和比较七个流行的HTO解倍化工具的性能.
  • 评估工具性能与HTO分配的遗传"基础真相".
  • 确定可靠的方法,并建议HTO数据的质量评估战略.

主要方法:

  • 评价了七个HTO去复数工具:hashedDrops,HTODemux,GMM-Demux,demuxmix,deMULTIplex,BFF,和HashSolo.这些工具的使用情况.
  • 使用scRNA-seq数据集,通过遗传变异独立验证样本来源.
  • 根据使用HTO数据对样本分配单元的准确性评估工具性能.

主要成果:

  • 所有评估的HTO解倍器工具都在高质量的HTO标签数据上进行了类似的性能.
  • 假设双模计数分布的方法在质量较差的HTO数据上表现较差.

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  • 提出了评估HTO计数质量的启发式方法.

    • 结论:

    • 选择HTO脱多元化工具可能会影响结果,特别是在低于最佳的HTO标签方面.
    • 在去复数之前或在去复数期间,对HTO计数的质量评估至关重要.
    • 在具有挑战性的HTO数据场景中,可能需要进一步开发强大的脱多倍化.