Transcompp

Transcompp quantifies phenotypic plasticity from single-cell and bulk phenotype measurements using Markov modeling to characterize stochastic cell-state transitions and proliferation kinetics.

Key Features:

• Markov modeling: Employs a Markov modeling approach to analyze probabilistic cell-state transitions.
• Optimization and resampling: Uses optimization combined with resampling to estimate best-fit stochastic transition rates, rate intervals, and phenotype-specific proliferation parameters.
• Bidirectional transitions and asymmetric proliferation: Handles simultaneous bidirectional transitions and asymmetric proliferation kinetics in cellular populations.

Scientific Applications:

• Time-series analysis of purified subpopulations: Applied to time-series datasets of purified stem-like and non-stem cancer cell subpopulations to quantify re-equilibration dynamics under different culture environments.
• Impact of culture reagents in MCF10CA1a: Used to demonstrate that hydrocortisone and cholera toxin shift equilibrium of basal-like breast cancer cell line MCF10CA1a toward stem-like or non-stem states, respectively.
• Predictive trajectory and equilibrium inference: Validated predictive capability for long-term trajectories and equilibrium convergence from short-term experiments using patient-derived cells.

Methodology:

Markov modeling with optimization and resampling to estimate stochastic transition rates, rate intervals, and phenotype-specific proliferation parameters, accommodating bidirectional transitions and asymmetric proliferation kinetics.