Data from: ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

Numerous biological systems oscillate over time or space. Despite these oscillators’ importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning. We present ZeitZeiger, a method to predict a periodic variable (e.g. time of day) from a high-dimensional observation. ZeitZeiger learns a sparse representation of the variation associated with the periodic variable in the training observations, then uses maximum-likelihood to make a prediction for a test observation. We applied ZeitZeiger to a comprehensive dataset of genome-wide gene expression from the mammalian circadian oscillator. Using the expression of 13 genes, ZeitZeiger predicted circadian time (internal time of day) in each of 12 mouse organs to within ∼1 h, resulting in a multi-organ predictor of circadian time. Compared to the state-of-the-art approach, ZeitZeiger was faster, more accurate and used fewer genes. We then validated the multi-organ predictor on 20 additional datasets comprising nearly 800 samples. Our results suggest that ZeitZeiger not only makes accurate predictions, but also gives insight into the behavior and structure of the oscillator from which the data originated. As our ability to collect high-dimensional data from various biological oscillators increases, ZeitZeiger should enhance efforts to convert these data to knowledge.诸多生物系统会随时间或空间呈现振荡行为。尽管这类振荡器具有重要研究价值，但现有正则化监督学习（regularized supervised learning）方法难以处理振荡系统产生的观测数据。我们提出了ZeitZeiger：一种可从高维观测数据中预测周期性变量（如每日时段）的方法。该方法会先从训练观测数据中学习与目标周期性变量相关的变异的稀疏表示（sparse representation），随后借助最大似然（maximum-likelihood）法对测试观测数据进行预测。我们将ZeitZeiger应用于一套涵盖哺乳动物昼夜节律振荡器（circadian oscillator）的全基因组基因表达（genome-wide gene expression）综合数据集。仅利用13个基因的表达量，ZeitZeiger就能准确预测12种小鼠器官内的昼夜节律时间（circadian time，即内部日时段），误差约在1小时以内，由此构建出可预测昼夜节律时间的多器官预测模型。与当前最先进的方法相比，ZeitZeiger的运算速度更快、预测精度更高，且所需的基因数量更少。随后我们利用涵盖近800个样本的20套独立数据集，对该多器官预测模型进行了验证。研究结果表明，ZeitZeiger不仅能够实现高精度预测，还可帮助研究者深入理解数据来源振荡器的行为模式与结构特征。随着我们从各类生物振荡器中采集高维数据的能力不断提升，ZeitZeiger将助力推动将这些数据转化为科学知识的研究进程。