bioconductor-trajectoryutils

name: bioconductor-trajectoryutilsdescription: 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 inferewhen_to_use: "Use when: 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().. Not for: 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"user-invocable: false

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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