Package: BioTransition 2.1.0

BioTransition: Dynamic Network Biomarker Analysis for Critical Transitions

A comprehensive toolkit for detecting critical transitions and identifying dynamic network biomarkers (DNB) in biological systems. Critical transitions, characterized by sudden shifts between distinct states, are prevalent in complex biological processes including disease progression, cellular differentiation, and developmental transitions. This package implements seven complementary DNB methodologies: (1) conventional DNB (cDNB) based on the original DNB theory (Chen et al. 2012 <doi:10.1038/srep00342>); (2) topological DNB (tDNB), a novel approach utilizing network topology and scale-free properties; (3) landscape DNB (LDNB) for quantifying state transitions (Liu et al. 2019 <doi:10.1093/nsr/nwy162>); (4) local DNB (LcDNB) leveraging protein-protein interaction networks; (5) module-based DNB (MDNB) for modular analysis (Li et al. 2022 <doi:10.1016/j.xinn.2022.100364>); (6) time-series network module biomarker (TSNMB) for temporal dynamics (Zhong et al. 2022 <doi:10.1093/jmcb/mjac052>); and (7) time-series leading edge (TSLE) analysis (Liu et al. 2020 <doi:10.1093/bioinformatics/btz758>). Core computational routines are implemented in C++ via 'Rcpp' for optimal performance. Compatible with bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data. Includes curated protein-protein interaction networks for human and mouse from the STRING database.

Authors:Zaoqu Liu [aut, cre], Chuhan Zhang [ctb]