Numerous time-course gene expression datasets have been generated for studying the biological dynamics that drive disease progression; and nearly as many methods have been proposed to analyse them. However, barely any method exists that can appropriately model time-course data while accounting for heterogeneity that entails many complex diseases. Most methods manage to fulfil either one of those qualities, but not both. The lack of appropriate methods hinders our capability of understanding the disease process and pursuing preventive treatments. We present a method that models time-course data in a personalised manner using Gaussian processes in order to identify differentially expressed genes (DEGs); and combines the DEG lists on a pathway-level using a permutation-based empirical hypothesis testing in order to overcome gene-level variability and inconsistencies prevalent to datasets from heterogenous diseases. Our method can be applied to study the time-course dynamics, as well as specific time-windows of heterogeneous diseases. We apply our personalised approach on three longitudinal type 1 diabetes (T1D) datasets, where the first two are used to determine perturbations taking place during early prognosis of the disease, as well as in time-windows before autoantibody positivity and T1D diagnosis; and the third is used to assess the generalisability of our method. By comparing to non-personalised methods, we demonstrate that our approach is biologically motivated and can reveal more insights into progression of heterogeneous diseases. With its robust capabilities of identifying disease-relevant pathways, our approach could be useful for predicting events in the progression of heterogeneous diseases and even for biomarker identification.

已经生成了许多时程基因表达数据集，用于研究驱动疾病进展的生物动力学；并且已经提出了几乎同样多的方法来分析它们。然而，几乎没有任何方法可以适当地模拟时间过程数据，同时考虑到许多复杂疾病的异质性。大多数方法都设法满足其中任何一种品质，但不能同时满足两者。缺乏适当的方法阻碍了我们了解疾病过程和进行预防性治疗的能力。我们提出了一种使用高斯过程以个性化方式对时间进程数据进行建模的方法，以识别差异表达基因 (DEG)；并使用基于排列的经验假设检验在通路水平上组合 DEG 列表，以克服异源性疾病数据集普遍存在的基因水平变异性和不一致性。我们的方法可用于研究时间过程动态，以及异质性疾病的特定时间窗。我们将我们的个性化方法应用于三个纵向 1 型糖尿病 (T1D) 数据集，其中前两个用于确定在疾病早期预后期间以及在自身抗体阳性和 T1D 诊断之前的时间窗口中发生的扰动；第三个用于评估我们方法的普遍性。通过与非个性化方法进行比较，我们证明我们的方法具有生物学动机，并且可以揭示对异质性疾病进展的更多见解。