Longitudinal TCR clonotype trajectories and transition datasets across detectability thresholds

This dataset contains longitudinal T cell receptor (TCR) clonotype trajectories and derived temporal transition tables generated within the ClonoDynamics pipeline tcr-dynamics-noise-pipeline. In particular, trajectory tables were produced with 3-trajectory_builder.py, and transition tables were generated from these trajectories with 4-build_transition_patterns.py. The pipeline is available in the ClonoDynamics GitHub repository: https://github.com/ClonoDynamics/tcr-dynamics-noise-pipeline. For each detectability threshold (α), the dataset provides: Trajectory tables describing clonotype abundance over time Transition tables describing pairwise temporal changes in log-frequency These representations enable reproducible inference of clonotype dynamics, including drift, diffusion, and stochastic modeling under different detectability regimes. 📊 Data structure 1. Trajectory tables (trajectories_long.csv) Each row represents a clonotype at a given time: subject — subject identifier time — sampling time point aaSeqCDR3 — clonotype identifier freq — clonotype frequency log_freq — natural logarithm of frequency observable — Boolean detectability indicator 2. Transition tables (transitions_all.csv) Each row represents a temporal transition between two timepoints for the same clonotype. Core variables subject — subject identifier aaSeqCDR3 — clonotype identifier t0, t1 — initial and final timepoints dt = t1 − t0 — time lag x0, x1 — log-frequency at t0t0 and t1t1 dx = x1 − x0 — log-change in abundance Δx=log⁡f(t1)−log⁡f(t0)Δx=logf(t1)−logf(t0) Detectability information obs0, obs1 — detectability at endpoints (T/F) obs_class — transition class: TT: detectable → detectable TF: detectable → non-detectable FT: non-detectable → detectable FF: non-detectable → non-detectable Transition structure pattern — transition type (all = all valid pairs) n_steps — number of time steps (equal to dt) Weights To support balanced statistical inference, each transition is associated with weight components: w_clone — inverse number of transitions for that clonotype w_dt — inverse frequency of transitions with the same time lag dtdt w_class — class-specific weight (TT, TF, FT, FF) w — total weight: w=w_clone⋅w_dt⋅w_class