让时间顺序与数据拓扑同时显现，看清细胞演化动态
IRIS: time-structured manifold projections
- 引入时间结构约束的流形投影，兼顾时序与拓扑关系
- 在单细胞转录组等数据中准确还原细胞演化路径
- 适合研究发育、分化等动态生物过程的科研人员
高维生物医学数据（如细胞-基因矩阵）正日益以时间序列方式生成。然而，t-SNE、UMAP等传统流形学习算法无法在可视化中体现时间顺序，掩盖了细胞类型等类别的动态变化。为此，我们提出IRIS——一种新型流形学习算法，能同时将布局结构化为时间序列和流形拓扑。IRIS可有效可视化多种动态生物医学数据，包括单细胞RNA测序（scRNA-seq）、比较宏基因组学及文献数据，揭示随时间演变的生物学过程。
原文摘要 · Abstract (English)
High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SNE and UMAP, cannot incorporate time-ordering in their layouts, obfuscating the dynamics of cell types or other classes. As a solution, we present IRIS, a new Manifold Learning algorithm that structures layouts both chronologically and by manifold topology. IRIS can visualize a wide range of dynamic biomedical data, including scRNA-seq, comparative metagenomics, and literature.
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