Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model.

Rui Liu,Jiayuan Zhong,Pei Chen,Yongjun Li,Xiangtian Yu

doi:10.3389/fgene.2019.00285

Abstract

The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex diseases. However, with only a case sample, it is challenging to detect a pre-disease state which has little significant differences comparing with a normal state in terms of phenotypes and gene expressions. In this study, by regarding the tipping point as the end point of a stationary Markov process, we proposed a single-sample-based hidden Markov model (HMM) approach to explore the dynamical differences between a normal and a pre-disease states, and thus can signal the upcoming critical transition immediately after a pre-disease state. Using this method, we identified the pre-disease state or tipping point in a numerical simulation and two real datasets including stomach adenocarcinoma and influenza infection, which demonstrate the effectiveness of the method.

Highlights

Considerable evidence suggests that during the progression of many complex diseases the deterioration is not necessarily smooth but abrupt (Litt et al, 2001; McSharry et al, 2003; Scheffer et al, 2009)
It is seen that the frequency for the occurrence of differential edges was significantly different in the vicinity of the tipping point (p = 0), which implies that much more edges would occur in the differential network when the system approaches the tipping point
Detecting the early-warning signal before a sudden deterioration into a severe disease state is crucial to patients all over the world

Summary

Introduction

Considerable evidence suggests that during the progression of many complex diseases the deterioration is not necessarily smooth but abrupt (Litt et al, 2001; McSharry et al, 2003; Scheffer et al, 2009). From a dynamical systems’ perspective, the general progression of complex diseases was modeled as three states or stages (Figure 1A): (i) a normal state, which represents a relative healthy stage with high stability and robustness to perturbations; (ii) a pre-disease state, which was defined as the limit of the normal state, and locating just before the occurrence of sudden deterioration, with low stability and robustness; (iii) a disease state, which represents a serious deteriorated stage generally with high stability and robustness, because it is usually very difficult to return to the normal state even with intensive treatment (Liu et al, 2014a). It is hard to detect a pre-disease state by traditional biomarkers since it is similar to the normal state in terms of the phenotype and gene expression

Methods

Results

Conclusion