改进t-SNE算法，让单细胞测序数据可视化更准确反映生物差异。
Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data
- 引入概率表示，显式建模单细胞数据中的转录噪声。
- 在模拟和真实数据中均能清晰区分高噪声与低噪声细胞群。
- 适合关注数据不确定性的生物学家与单细胞数据分析者。
基于t分布随机邻域嵌入（t-SNE）的非线性数据可视化，可将复杂的单细胞转录组景观映射到二维或三维空间，以准确呈现生物群体。然而，传统t-SNE常忽略原始数据中的不确定性，导致高噪声细胞亚群在可视化中无法区分，产生误导性结果。为此，我们提出不确定性感知t-SNE（Ut-SNE），一种专为不确定单细胞RNA-seq数据设计的抗噪可视化工具。通过为每个样本构建概率表示，Ut-SNE能准确将转录变异性中的噪声纳入视觉解释，揭示显著的转录不确定性。在多个实例中，我们展示了Ut-SNE的实际价值，并强调将不确定性意识融入可视化实践的重要性。该工具具有高度通用性，可拓展至单细胞测序以外的其他科学领域，成为高维数据分析的重要资源。
原文摘要 · Abstract (English)
Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcriptomic landscapes in two or three dimensions to depict biological populations accurately. However, t-SNE often fails to account for uncertainties in the original dataset, leading to misleading visualizations where cell subsets with noise appear indistinguishable. To address these challenges, we introduce uncertainty-aware t-SNE (Ut-SNE), a noise-defending visualization tool tailored for uncertain single-cell RNA-seq data. By creating a probabilistic representation for each sample, Our Ut-SNE accurately incorporates noise about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data, revealing significant uncertainties in transcriptomic variability. Through various examples, we showcase the practical value of Ut-SNE and underscore the significance of incorporating uncertainty awareness into data visualization practices. This versatile uncertainty-aware visualization tool can be easily adapted to other scientific domains beyond single-cell RNA sequencing, making them valuable resources for high-dimensional data analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。