AI 中文总结
针对高维生物数据集分析难题，提出端到端张量分析管道，采用张量分解方法处理稀疏性，用于三维哺乳动物脂质运输数据集，成功识别特定脂质-细胞器对、揭示脂质模块、提取潜在因素，且与ODE模型比较验证了方法有效性。
AI 中文摘要
具有分子、空间和时间维度的高维生物数据集日益常见。然而，其分析需要能整合多数据轴、处理噪声和缺失测量值，并捕捉动态相互作用和定位变化的方法。为此，我们提供了一个端到端张量分析管道，通过采用张量分解方法（HOSVD和CP）处理稀疏性，这些方法用二元掩码处理缺失数据并结合测量误差框架。我们在一个三维哺乳动物脂质运输数据集上展示了该方法，成功识别了经历快速时间演变的特定脂质-细胞器对，揭示了跨细胞器和时间共同变化的脂质模块，并提取了代表全球重新分布轨迹的潜在因素。与先前的动力学ODE模型直接比较证实张量分解忠实地再现了关键脂质通量特征。
英文摘要
High-dimensional biological datasets with molecular, spatial, and temporal dimensions are increasingly common. However, their analysis requires approaches that can integrate multiple data axes, accommodate noisy and missing measurements, and capture both dynamic interactions and localization changes. To address this, we provide an end-to-end tensor analysis pipeline that handles sparsity by employing tensor decomposition methods (HOSVD and CP) that are augmented with a binary mask for missing data and a framework for measurement error. We showcase this on a three-dimensional mammalian lipid transport dataset depending on lipid identities, organelle localizations, and time-series abundances. Our approach successfully identifies specific lipid-organelle pairs undergoing rapid temporal evolution, uncovers modules of lipids that co-vary across organelles and time, and extracts latent factors representing global redistribution trajectories. Direct comparison with a previous kinetic ODE model confirms that the tensor decompositions faithfully reproduce key lipid flux features.
Comments15 pages, 14 figures
