monocle3-trajectory-embedding

monocle3-trajectory-embedding

Summary

Compute pseudotime-ordered cell-state trajectories from single-cell ATAC-seq data using Monocle 3 within ArchR. This skill reconstructs developmental or differentiation trajectories by embedding cells along learned paths of gene regulatory change.

When to use

Use this skill when you have an ArchR project object with dimensionality reduction results (LSI or combined dimensions from scATAC-seq ± scRNA-seq) and want to infer pseudotime trajectories and cell-state transitions. Appropriate when your biological question involves understanding developmental progression, cell fate decisions, or temporal ordering of cell populations within a single-cell ATAC-seq experiment.

When NOT to use

• Your ArchR project has not yet been processed with LSI or combined dimensionality reduction (addIterativeLSI or addCombinedDims must precede this skill).
• Your biological question does not require pseudotime ordering or trajectory inference (e.g., static cell-type annotation, snapshot comparisons).
• You prefer an alternative trajectory method; use addSlingShotTrajectories instead if Slingshot is your chosen algorithm.

Inputs

• ArchR project object with computed LSI or combined dimensions
• Cell metadata (optional: cell type or batch annotations to guide trajectory direction)

Outputs

• ArchR project object with embedded trajectory (pseudotime, cell state assignments)
• Monocle 3 trajectory object (CDS) containing manifold and pseudotime coordinates

How to apply

Load the prepared ArchR project object containing computed LSI or combined dimensions. Call getMonocleTrajectories to extract trajectory information, or directly invoke addMonocleTrajectory to compute and embed trajectory data using the Monocle 3 algorithm. Monocle 3 will learn a reduced-dimensional manifold within the ArchR project's existing embedding space, order cells by pseudotime, and identify branch points. The resulting ArchR project object will contain trajectory metadata (pseudotime values, cell state assignments) that can be visualized and used for downstream differential accessibility analysis along the trajectory.