Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism’s phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

中文翻译：

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使用 GloScope 在群体规模上可视化 scRNA-Seq 数据


scRNA-Seq 研究越来越多地探索不同样品中的细胞群以及样品异质性对生物体表型的影响。然而，已经开发的生物信息学方法相对较少，可以充分解决此类人群水平分析的样本之间的差异。我们提出了一个框架来表示样本的整个单细胞图谱，我们称之为 GloScope 表示。我们在 12 个到 300 多个样本的研究设计的 scRNA-Seq 数据集上实施了 GloScope，并展示了 GloScope 如何允许研究人员在样本水平执行基本的生物信息学任务，特别是可视化和质量控制评估。