BioTransition

Overview

BioTransition is a comprehensive R package for detecting critical transitions in biological systems using Dynamic Network Biomarker (DNB) theory. Critical transitions—abrupt shifts between alternative stable states—are ubiquitous in complex biological processes, including disease onset, cellular differentiation, and developmental transitions. Identifying the pre-transition state (tipping point) before an irreversible shift occurs is crucial for early warning and therapeutic intervention.

This package implements seven complementary DNB methodologies, including tDNB (topological DNB), a novel algorithm that leverages network topology and scale-free properties for robust critical transition detection.

Theoretical Background

The DNB theory, proposed by Chen et al. (2012), provides a model-free approach to detect early warning signals of critical transitions. The core principle is that a dominant group of molecules (DNB module) exhibits three key characteristics near the tipping point:

1. Increased variation: Standard deviation (SD) of DNB members increases dramatically
2. Enhanced correlation: Pearson correlation coefficient (PCC) among DNB members strengthens
3. Decreased correlation: PCC between DNB members and non-DNB molecules weakens

The composite index (CI) quantifies the transition signal:

$$CI = \frac{\overline{SD_{in}} \cdot \overline{|PCC_{in}|}}{\overline{|PCC_{out}|}}$$

Implemented Methods

Method	Description	Network	Reference
cDNB	Conventional DNB based on correlation screening	—	Chen et al., 2012
tDNB	Topological DNB using scale-free network topology	—	Liu Z. (this package)
LcDNB	Local DNB with protein-protein interaction constraints	PPI	—
LDNB	Landscape DNB for state transition quantification	PPI	Liu et al., 2019
MDNB	Module-based DNB for modular network analysis	PPI	Li et al., 2022
TSNMB	Time-series network module biomarker	PPI	Zhong et al., 2022
TSLE	Time-series leading edge analysis	PPI	Liu et al., 2020

Installation

# Install from R-universe (recommended)
install.packages("BioTransition", repos = "https://zaoqu-liu.r-universe.dev")

# Or install from GitHub
# install.packages("devtools")
devtools::install_github("SolvingLab/BioTransition")

Quick Start

library(BioTransition)

# Prepare sample annotation
sample_info <- data.frame(
  sample_id = colnames(expression_matrix),
  state = state_labels
)

# Run tDNB analysis (topological DNB)
result <- tDNB(
  expr = expression_matrix,
  state = sample_info,
  state.levels = c("Normal", "Pre-disease", "Disease")
)

# Examine results
result$DNB.score    # Composite index across states
result$DNB.genes    # Identified DNB module genes
result$Candidate    # Candidate modules per state

Data Compatibility

BioTransition supports multiple omics data types:

• Bulk RNA-seq: Population-level transcriptomics
• Single-cell RNA-seq: Cell-type specific analysis
• Spatial transcriptomics: Spatially resolved expression data

Built-in Resources

The package includes curated protein-protein interaction (PPI) networks:

• ppi_h: Human PPI network from STRING database
• ppi_m: Mouse PPI network from STRING database

Performance

Core computational routines are implemented in C++ via Rcpp, providing 2–20× speedup compared to pure R implementations for correlation analysis and module scoring.

References

1. Chen L, Liu R, Liu ZP, Li M, Aihara K. (2012). Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Scientific Reports, 2:342. doi: 10.1038/srep00342

2. Liu R, Chen P, Chen L. (2019). Single-sample landscape entropy reveals the imminent phase transition during disease progression. National Science Review, 7(7):1175-1185. doi: 10.1093/nsr/nwy162

3. Li M, Zeng T, Liu R, Chen L. (2022). Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis. The Innovation, 3(5):100364. doi: 10.1016/j.xinn.2022.100364

4. Zhong J, Han C, Wang Y, Chen P, Liu R. (2022). Identifying the critical state of complex diseases by the novel concept of sample-specific network module biomarker. Journal of Molecular Cell Biology, 14(6):mjac052. doi: 10.1093/jmcb/mjac052

5. Liu X, Chang X, Liu R, Yu X, Chen L, Aihara K. (2020). Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. Bioinformatics, 36(4):1068-1074. doi: 10.1093/bioinformatics/btz758

Citation

If you use BioTransition in your research, please cite:

Liu Z (2026). BioTransition: Dynamic Network Biomarker Analysis for Critical 
Transition Detection. R package version 2.0.0. 
https://github.com/SolvingLab/BioTransition

Author

Zaoqu Liu, MD, PhD
Chinese Academy of Medical Sciences and Peking Union Medical College
Email: liuzaoqu@163.com
ORCID: 0000-0002-0452-742X

License

GPL (>= 3)