Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity
Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity
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DOI：
10.1016/j.isci.2020.101126
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发表时间：
2020-06-26
期刊：
影响因子：
5.8
通讯作者：
Canzar, Stefan
中科院分区：
文献类型：
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作者：
Do, Van Hoan;Elbassioni, Khaled;Canzar, Stefan
单细胞 RNA 测序数据集的庞大规模通常超出了当前计算分析方法解决细胞类型检测等常规任务的能力。最近，引入了几何草图作为均匀子采样的替代方案。它选择均匀覆盖原始数据集占据的转录组空间的细胞子集（草图），以加速下游分析并突出显示稀有细胞类型。在这里，我们提出了算法 Spetcher，它利用阈值技术来有效地挑选覆盖整个转录组空间的球体（而不是通常使用的等大小框）内的代表性细胞。我们表明，Sphetcher 计算的球形草图构成了原始转录组景观的更准确表示。我们的优化方案允许包括可以编码先前的生物学或实验知识的公平性方面。我们展示了公平采样如何为人类骨骼肌成肌细胞分化轨迹的推断提供信息。
The massive size of single-cell RNA sequencing datasets often exceeds the capability of current computational analysis methods to solve routine tasks such as detection of cell types. Recently, geometric sketching was introduced as an alternative to uniform subsampling. It selects a subset of cells (the sketch) that evenly cover the transcriptomic space occupied by the original dataset, to accelerate downstream analyses and highlight rare cell types. Here, we propose algorithm Sphetcher that makes use of the thresholding technique to efficiently pick representative cells within spheres (as opposed to the typically used equal-sized boxes) that cover the entire transcriptomic space. We show that the spherical sketch computed by Sphetcher constitutes a more accurate representation of the original transcriptomic landscape. Our optimization scheme allows to include fairness aspects that can encode prior biological or experimental knowledge. We show how a fair sampling can inform the inference of the trajectory of human skeletal muscle myoblast differentiation.