Trajectory inference

Asc-Seurat supports three trajectory methods that share the same Shiny controls:

  • Slingshot (default) — fits lineages on the clustered UMAP embedding. Start and end clusters can guide lineage orientation, and the results include a compact minimum-spanning-tree graph of the inferred cluster relationships.

  • PAGA — estimates connectivity between clusters and calculates pseudotime from the selected root cluster.

  • Monocle 3 — learns a Monocle 3 principal graph on the existing UMAP and orders cells from the selected root cluster.

Running trajectory inference

The Trajectory Inference tab takes a clustered Seurat object as input. You can load it in two ways:

  • Upload from browser — the default and most convenient option for moderate-sized objects.

  • Path on this computer/server — best for very large RDS files, where the browser upload limit is a concern. Relative paths are resolved against the working directory shown beneath the input.

For all three methods you can:

  • pick the trajectory method (Slingshot / PAGA / Monocle 3),

  • choose a root/start cluster; this is strongly recommended so the pseudotime orientation is explicit,

  • optionally fix an end cluster (Slingshot only),

  • inspect the resulting pseudotime plot and trajectory graph,

  • export the trajectory object.

Trajectory Inference tab.

Trajectory Inference tab. Upload a clustered Seurat RDS or provide a server path, pick Slingshot, PAGA, or Monocle 3, and set a root/start cluster before running inference.

Trajectory gene discovery

Each trajectory method exposes a gene-discovery workflow that matches the method’s underlying model.

For Slingshot, the Shiny workflow exposes three trajectory-DE engines:

  • PseudotimeDE-fast (recommended default). Fast, well-suited to the Slingshot pseudotime produced by Asc-Seurat.

  • scMaSigPro. Polynomial-regression alternative; identifies genes that change significantly along pseudotime.

  • tradeSeq. More flexible but slower; the UI labels it as a long-runtime option and lets you tune the number of knots.

Set the Top genes to display/plot control to limit how many top genes are shown in the displayed table and plots; all variable genes are tested regardless.

For PAGA, the workflow focuses on connected states rather than lineage-specific DE:

  • Connected cluster markers compares selected or top-connected PAGA cluster pairs with Seurat marker testing.

  • Pseudotime-associated genes ranks genes that vary along cells in the selected connected clusters.

For Monocle 3, Asc-Seurat uses Monocle 3’s graph-based test to rank genes that vary over the learned principal graph. The optional gene-module step groups significant trajectory-variable genes into modules.

Installing the Python and R dependencies

The Docker image bundles the trajectory and DE dependencies. The R package install also installs the required R trajectory packages; only the Python stack for PAGA is prepared separately:

  • PAGA: requires scanpy. Run ascseurat::setup_paga() from R to verify or prepare the Python environment.

  • Monocle 3, PseudotimeDE-fast, scMaSigPro, and tradeSeq are declared as app runtime dependencies.