SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems. Issue 2 (27th January 2023)

• Record Type: Journal Article
• Authors: Zhong, Jiayuan; Ding, Dandan; Liu, Juntan; Liu, Rui; Chen, Pei
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 2(2023)
• Publication Date: 2023-01-27
• Subjects: sample-perturbed network entropy (SPNE) -- critical state -- dynamic network biomarker (DNB) -- sample-perturbed directed network -- tipping point; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbad028
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Complex biological systems do not always develop smoothly but occasionally undergo a sharp transition; i.e. there exists a critical transition or tipping point at which a drastic qualitative shift occurs. Hunting for such a critical transition is important to prevent or delay the occurrence of catastrophic consequences, such as disease deterioration. However, the identification of the critical state for complex biological systems is still a challenging problem when using high-dimensional small sample data, especially where only a certain sample is available, which often leads to the failure of most traditional statistical approaches. In this study, a novel quantitative method, sample-perturbed network entropy (SPNE), is developed based on the sample-perturbed directed network to reveal the critical state of complex biological systems at the single-sample level. Specifically, the SPNE approach effectively quantifies the perturbation effect caused by a specific sample on the directed network in terms of network entropy and thus captures the criticality of biological systems. This model-free method was applied to both bulk and single-cell expression data. Our approach was validated by successfully detecting the early warning signals of the critical states for six real datasets, including four tumor datasets from The Cancer Genome Atlas (TCGA) and two single-cell datasets of cell differentiation. In addition, the functional analyses of signaling biomarkers demonstrated the effectiveness of the analytical and computational results.