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.
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Statistical and computational analysis of high-throughput ‘omics’ datasets for understanding the etiology and pathogenesis of autoimmune diseases
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