Comparison of Pseudotime and Trajectory Inference Tools for Single-Cell RNA-seq
Introduction
Pseudotime and trajectory inference are critical in modeling cellular transitions and fate decisions based on single-cell RNA-seq data. These methods help reconstruct the dynamic processes like development, differentiation, or disease progression such as atrial fibrillation.
This post provides a detailed comparison of popular tools for pseudotime and trajectory inference based on their features, scalability, and use cases.
🔬 Comparison of Tools for Pseudotime & Trajectory Inference
| Tool | Language | Trajectory Type | Strengths & Use Case | Scalability | Link |
|---|---|---|---|---|---|
| Monocle3 | R | Linear, Branching | Graph-based; integrates with Seurat; models complex trajectories | ★★★★☆ | Monocle3 |
| Slingshot | R | Branching | Simple and fast; fits PCA/UMAP; lightweight | ★★★★☆ | Slingshot |
| scVelo | Python | Dynamic (RNA velocity) | Velocity-based; latent time; very dynamic models | ★★★★★ | scVelo |
| Velocyto | Python | Dynamic (preprocessing) | Generates velocity input files (loom); used with scVelo | ★★★☆☆ | Velocyto |
| PAGA | Python | Graph abstraction | Excellent for global structure; scalable to large datasets | ★★★★★ | PAGA |
| TSCAN | R | Linear, Branching | Simple MST-based inference; fast on small data | ★★★☆☆ | TSCAN |
| SCORPIUS | R | Mostly Linear | Great for early-stage exploration; regression-based | ★★★☆☆ | SCORPIUS |
| STREAM | Python | Branching + Visualization | Interactive and visually intuitive; tree plotting | ★★★★☆ | STREAM |
| CellRank | Python | Probabilistic Fate Mapping | Combines velocity with Markov chains; future state prediction | ★★★★★ | CellRank |
| Palantir | Python | Linear / Branching | Diffusion maps + entropy for fate inference | ★★★★☆ | Palantir |
| Dynverse | R | Various (Unified Interface) | Wrapper for 50+ methods; ideal for benchmarking | ★★★★★ | Dynverse |
🧭 Recommended Tools by Use Case
| Use Case | Recommended Tools |
|---|---|
| Quick and simple in R | Slingshot, TSCAN |
| Branching and complex trajectories | Monocle3, STREAM |
| Dynamic time modeling | scVelo, CellRank |
| Large-scale datasets | PAGA, CellRank |
| Benchmarking multiple methods | Dynverse |
| Fate decision and prediction | Palantir, CellRank |
Let me know in the comments if you’ve used these tools in your own research or if you’d like a step-by-step walkthrough of one of them in a future post.