Abstract
BackgroundDeveloping effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. However, it is a challenging task to identify such pre-disease state or tipping point in clinics, where only one single sample is available and thus results in the failure of most statistic approaches.MethodsIn this study, we presented a single-sample-based computational method to detect the early-warning signal of critical transition during the progression of complex diseases. Specifically, given a set of reference samples which were regarded as background, a novel index called single-sample Kullback–Leibler divergence (sKLD), was proposed to explore and quantify the disturbance on the background caused by a case sample. The pre-disease state is then signaled by the significant change of sKLD.ResultsThe novel algorithm was developed and applied to both numerical simulation and real datasets, including lung squamous cell carcinoma, lung adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, colon adenocarcinoma, and acute lung injury. The successful identification of pre-disease states and the corresponding dynamical network biomarkers for all six datasets validated the effectiveness and accuracy of our method.ConclusionsThe proposed method effectively explores and quantifies the disturbance on the background caused by a case sample, and thus characterizes the criticality of a biological system. Our method not only identifies the critical state or tipping point at a single sample level, but also provides the sKLD-signaling markers for further practical application. It is therefore of great potential in personalized pre-disease diagnosis.
Highlights
Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease
We used a single-sample with high-throughput omics data, to identify the pre-disease state or early warning signals of the disease deterioration based on the sample Kullback–Leibler divergence (sKLD) score
The successful identification of the pre-disease states in these diseases validated the effectiveness of sKLD method in quantifying the tipping point just before the critical transitions into severe disease states
Summary
Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. We proposed a theoretic framework, called the dynamical network biomarker (DNB) concept [10, 14] for identifying the pre-disease state of complex diseases This DNB concept, directly from the critical slowingdown theory [15, 16], provides statistical method to select relevant variables for the pre-disease state, that is, a small group of closely related variables (DNBs) convey early warning signals for the impending critical transition by some drastic statistical indices [17, 18]. When there is only a single case sample available, it requires new computational method to explore the critical information, detect the early-warning signal and identify the pre-disease state
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