BioTIP

BioTIP identifies Critical Transition Signals (CTS) and lineage-determining transcription factors from single-cell transcriptomic datasets to characterize biological tipping points and regulated stochasticity during semi-stable cellular state transitions.

Key Features:

• Critical Transition Signal (CTS) Identification: Identifies small groups of genes (CTS) that mark regulated stochasticity at semi-stable transitions.
• Lineage-Determining Transcription Factors Inference: Infers lineage-determining transcription factors that govern critical transitions.
• Handling of stochasticity and population variability: Addresses unexplainable stochasticity, variable population sizes, and alternative trajectory constructions in single-cell data.
• Feature selection: Implements feature selection to focus analysis on informative genes.
• Network decomposition: Applies network decomposition to detect interconnected gene modules associated with CTSs.
• Accurate estimation of correlations: Uses accurate correlation estimation to quantify gene–gene dependencies relevant to transitions.
• Optimization techniques: Employs optimization techniques to refine feature and network selection.
• Validation across datasets: Demonstrates CTS identification and validation on mouse gastrulation datasets.
• Comparison with existing methods: Benchmarked against three existing methods across six datasets, showing capture of interconnected and reproducible CTSs and independence from pseudo-temporal trajectory construction.

Scientific Applications:

• Tipping-point characterization: Characterizes biological tipping points in single-cell transcriptomic studies.
• Marker discovery: Identifies CTS genes that serve as markers of regulated stochasticity during state transitions.
• Regulatory inference: Infers lineage-determining transcription factors to study regulatory mechanisms in differentiation and development.
• Cross-dataset validation: Enables validation of transition signals across independent datasets such as mouse gastrulation.
• Trajectory-independent analysis: Supports analysis of transitions without reliance on pseudo-temporal trajectory construction.

Methodology:

Uses CTS identification, feature selection, network decomposition, accurate estimation of correlations, and optimization techniques to detect gene modules and infer lineage-determining transcription factors; validated via cross-dataset comparison.