Trajectory inference methods reconstruct developmental and differentiation trajectories from single-cell RNA-seq data. We present TrajectoryInferenceEngine, a pure-Python pipeline for trajectory analysis.
We present RNAVelocity, a complete RNA velocity analysis engine implemented entirely in Python using NumPy and SciPy — no scVelo, velocyto, loom, or anndata required. RNAVelocity implements four velocity models: (1) steady-state ratio estimation (La Manno et al.
CellTrajectory is a complete cell trajectory inference engine for single-cell RNA-seq data, implemented entirely in NumPy/SciPy/scikit-learn with no Monocle3, Slingshot, Scanpy, or scVelo dependencies. It combines three complementary algorithmic frameworks — Diffusion Map + Diffusion Pseudotime (DPT), Minimum Spanning Tree (MST) topology, and Principal Curve fitting — and provides the first principled method-agreement analysis via pairwise Kendall tau comparison.
Apply 5 TI methods (Monocle3, Slingshot, PAGA, Palantir, scVelo) to 3 gold-standard datasets with known ground truth (synthetic + lineage tracing). Pairwise Kendall τ between pseudotime orderings: mean 0.