CellTrans

CellTrans quantifies stochastic transitions between defined cell states from FACS and flow cytometry proportion data to estimate transition probabilities, predict future population compositions, and determine equilibrium state proportions.

Key Features:

• Stochastic Transition Quantification: Models cell state transitions as stochastic processes with rates dependent solely on the current state to quantify state-to-state movements over time.
• Data-Driven Analysis: Automates estimation of transition probabilities from FACS and flow cytometry experimental data and applies to populations with marker-defined stable compositions, including normal and cancerous cell lines.
• Predictive Capabilities: Predicts future cell-line compositions at specified time points, estimates equilibrium state proportions, and provides estimates of the time required to reach equilibrium.
• Handling Analytical Challenges: Incorporates approaches to address analytical challenges inherent in quantifying stochastic transitions to produce robust results from complex datasets.

Scientific Applications:

• Cell lineage and population dynamics: Inferring transition probabilities and temporal dynamics to analyze cell lineage compositions and population behavior.
• Cancer research: Studying state transitions in cancer cell lines to investigate tumor progression and potential treatment responses.
• Equilibrium analysis: Estimating equilibrium state proportions and the timescales for reaching equilibrium in heterogeneous cell populations.

Methodology:

Fits a mathematical model capturing stochastic cell-state alterations to cell-state proportion data from FACS or flow cytometry to automate estimation of transition probabilities; implemented as an R package.