Early detection of respiratory virus infections, such as influenza A (H3N2), is critical for timely intervention and disease management. Conventional biomarkers often overlook the complex and dynamic nature of gene regulatory changes, while existing predictive models frequently lack automation and robust external validation. Thus, we present CRISGI (Critical tran-Sient Gene Interaction), a computational framework that detects early-warning signals of infection by identifying dynamic changes in gene-gene interactions—termed critical transient interactions—from bulk RNA-seq data. CRISGI leverages critical transition (CT) theory to capture a GRN’s unstable intermediate state, known as the CT stage, before irreversible phenotypic shifts. Applied to a human challenge study with H3N2, CRISGI identified 128 critical transition edges (128-TER). These were used to train predictive models capable of forecasting symptom status and onset timing. 128-TER was then validated across six temporal transcriptomic datasets involving three respiratory viruses (H3N2, H1N1, HRV). The 128-TER consistently distinguished symptomatic individuals, predicted infection onset, and revealed phenotype-specific enrichment patterns. Notably, CRISGI captured immune-related transitions involving interferon-stimulated genes (e.g., IFIT1, CXCL10), underscoring their role in early host defense. CRISGI advances early-warning biomarker discovery by integrating interaction-level dynamics and predictive modeling. Its reproducibility across viruses highlights shared immune activation pathways, supporting its utility in both research and clinical contexts.
@article{lyu2025crisgi,title={Predicting Early Transitions in Respiratory Virus Infections via Critical Transient Gene Interactions},author={Lyu, Chengshang and Jiang, Anna and Ng, Ka Ho and Liu, Xiaoyu and Chen, Lingxi},journal={bioRxiv},pages={2025--04},year={2025},publisher={Cold Spring Harbor Laboratory},doi={10.1101/2025.04.18.649619},}
Knowledge-driven annotation for gene interaction enrichment analysis
Xiaoyu Liu†, Anna Jiang†, Chengshang Lyu, and Lingxi Chen*
Gene Set Enrichment Analysis (GSEA) is a cornerstone for interpreting gene expression data, yet traditional approaches overlook gene interactions by focusing solely on individual genes, limiting their ability to detect subtle or complex pathway signals. To overcome this, we present GREA (Gene Interaction Enrichment Analysis), a novel framework that incorporates gene interaction data into enrichment analysis. GREA replaces the binary gene hit indicator with an interaction overlap ratio, capturing the degree of overlap between gene sets and gene interactions to enhance sensitivity and biological interpretability. It supports three enrichment metrics: Enrichment Score (ES), Enrichment Score Difference (ESD) from a Kolmogorov-Smirnov-based statistic, and Area Under the Curve (AUC) from a recovery curve. GREA evaluates statistical significance using both permutation testing and gamma distribution modeling. Benchmarking on transcriptomic datasets related to respiratory viral infections shows that GREA consistently outperforms existing tools such as blitzGSEA and GSEApy, identifying more relevant pathways with greater stability and reproducibility. By integrating gene interactions into pathway analysis, GREA offers a powerful and flexible tool for uncovering biologically meaningful insights in complex datasets. The source code is available at https://github.com/compbioclub/GREA.
@article{liu2025grea,title={Knowledge-driven annotation for gene interaction enrichment analysis},author={Liu, Xiaoyu and Jiang, Anna and Lyu, Chengshang and Chen, Lingxi},journal={bioRxiv},pages={2025--04},year={2025},publisher={Cold Spring Harbor Laboratory},doi={10.1101/2025.04.15.649030},}
Biologically Informative NA Deconvolution (BIND) excavates hidden features of the proteome from missing values in large-scale datasets
The fast-advancing mass spectrometry and related technologies have greatly extended the depth of coverage in large-scale proteomics studies, including single-cell applications. As sample numbers grow rapidly, it is often challenging to interpret the proteins with missing values that are often presented as “NA” (not available). It could be the evidence of no expression, low expression below the detection threshold, or false negative detection due to technical issues. Existing methods for missing values imputation, while generally useful, rarely consider the non-random NA values that inform biological significance. In the current study, we developed Biologically Informative NA Deconvolution (BIND) that applies an adaptive neighborhood-based modeling to deconvolve the nature of NAs as “biological” (low/no expression) or technical (experimental errors). Applying to multiple cell line datasets and human tissue extracellular vesicle datasets, BIND excavated the NAs that indicated “hallmark absence” of unique proteins. This led to improvements in protein-protein interaction analysis and the identification of novel disease biomarkers. To facilitate its public accessibility, we compiled BIND into a web server that features functional online operations and interactive visualizations. Furthermore, we demonstrated that the BIND server could deconvolve the NAs and improve the analyses of single-cell proteomics datasets. Overall, BIND delineates the biological significance of missing values rather than treating them as a burden, providing a critical perspective for understanding the complex proteome in various biological contexts.
@article{weiheng2025bind,title={Biologically Informative NA Deconvolution (BIND) excavates hidden features of the proteome from missing values in large-scale datasets},author={Guo, Weiheng and Jin, Wenyi and Zheng, Jieyi and Pan, Yilin and Wang, Rui and Zhang, Jian and Feng, Xikang and Chen, Lingxi and Zhang, Liang},journal={bioRxiv},pages={2025--06},year={2025},publisher={Cold Spring Harbor Laboratory},doi={10.1101/2025.06.19.660508},}
