# Bioconductor Trajectoryutils

> Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime infere

- Skill: `biomate-ai/bioconductor-trajectoryutils` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-trajectoryutils`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-trajectoryutils/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/bioconductor-trajectoryutils

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# TrajectoryUtils

## Dependencies & Environment

> Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

- **Version:** 1.20.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Depends:** SingleCellExperiment
- **Imports:** Matrix, igraph, S4Vectors, SummarizedExperiment
- **Install:** `BiocManager::install("TrajectoryUtils")`

## When to Use
- Developing custom single-cell trajectory inference workflows or extending existing packages.
- Constructing cluster-level minimum spanning trees (MST) from low-dimensional single-cell embeddings using `createClusterMST()`.
- Standardizing pseudotime inference results and path structures using the `PseudotimeOrdering` class.
- Guessing possible root nodes for trajectory paths using `guessMSTRoots()`.

## When NOT to Use
- For end-to-end, user-friendly trajectory analysis with built-in visualization, use `slingshot` or `TSCAN` instead because `TrajectoryUtils` provides low-level developer utilities rather than high-level inference.
- For standard clustering or dimensionality reduction, use standard single-cell workflows instead.

## Data Requirements
- **Input format**: A numeric matrix of low-dimensional coordinates (e.g., PCA) and a vector of cluster assignments.
- **Structure**: Rows in the coordinate matrix represent cells, and columns represent dimensions.
- **Normalization state**: Input coordinates must be derived from normalized and dimensionally reduced expression data.

## Key Parameters
- **outgroup** (FALSE): Logical in `createClusterMST()` to add an outgroup to break apart distant clusters.
- **dist.method** (None): Method for distance calculation in `createClusterMST()` (e.g., `"mnn"` or `"slingshot"`).
- **use.median** (FALSE): Logical in `createClusterMST()` to compute centroids by taking the median instead of the mean.
- **method** (None): Strategy used in `guessMSTRoots()` to guess the root node (e.g., `"maxstep"` or `"minstep"`).
- **roots** (None): The starting node(s)/cluster(s) specified in `defineMSTPaths()`.
- **times** (None): Timing information (e.g., from RNA velocity) used in `defineMSTPaths()` to define paths based on local minima/maxima.

## Best Practices
- Use `dist.method="slingshot"` in `createClusterMST()` to account for the shape and spread of clusters via Mahalanobis distance.
- Use `use.median=TRUE` when constructing the MST to protect against clusters with many outliers.
- Store metadata on cells and paths systematically using `cellData()` and `pathData()` within a `PseudotimeOrdering` object.
- Use `splitByBranches()` for a root-free method of defining paths through the MST to interpret sections in a modular manner.

## Common Pitfalls
- **Spurious links**: Spurious links forming between unrelated parts of the dataset during MST construction; fix this by setting `outgroup=TRUE` in `createClusterMST()`.
- **Penalizing adjacent clusters**: Penalizing the formation of edges between adjacent heterogeneous clusters; fix this by using `dist.method="mnn"` to base distances on mutually nearest neighbors.
- **Multiple pseudotime values**: Requiring a single set of pseudotime values for downstream visualization when multiple paths exist; fix this by using `averagePseudotime()` to compute a single average per cell.

## Alternatives
- **slingshot**: A high-level package for trajectory inference that uses `TrajectoryUtils` under the hood but provides a complete user-facing workflow.
- **TSCAN**: Another high-level trajectory package based on MSTs, which also relies on these utilities for path finding.

## Citations
- Aaron Lun (2020). Trajectory utilities for package developers.

## References
- Homepage: https://bioconductor.org/packages/TrajectoryUtils
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TrajectoryUtils/inst/doc/TrajectoryUtils.html

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## Run this on BioMate

This skill is the **knowledge layer** — when, why, and how to use `trajectoryutils`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=trajectoryutils)** — free to start.

▶ **[Open `trajectoryutils` on BioMate →](https://www.biomate.ai?ref=kb&pkg=trajectoryutils)**