Monocle3 Tools

Configuration File: remote_tools/monocle3_tools.json Tool Type: Remote Tools Count: 1

This page contains all tools defined in the monocle3_tools.json configuration file.

Available Tools

run_monocle3_pseudotime (Type: RemoteTool)

Infer single-cell pseudotime with Monocle3 (Cao 2019, Trapnell lab): build a cell_data_set from r…

run_monocle3_pseudotime tool specification

Tool Information:

  • Name: run_monocle3_pseudotime

  • Type: RemoteTool

  • Description: Infer single-cell pseudotime with Monocle3 (Cao 2019, Trapnell lab): build a cell_data_set from raw counts and run the standard preprocess -> UMAP -> cluster_cells -> learn_graph -> order_cells pipeline, returning each cell’s pseudotime along a learned principal graph (with branches/loops) — unlike Slingshot, Monocle3 learns its own graph rather than ordering preset clusters. Specify the trajectory root with root_cluster (+ cluster_key) or explicit root_cells. Input is a server-accessible .h5ad of RAW counts.

Parameters:

  • adata_path (string) (required) Server-accessible path to an .h5ad AnnData of RAW counts (Monocle3 normalizes internally).

  • counts_layer (string) (optional) layers key holding raw counts if .X is not raw (optional; default: use .X).

  • cluster_key (string) (optional) obs column with input cluster labels; required when rooting via root_cluster, and enables per-cluster mean pseudotime.

  • root_cluster (string) (optional) Name of the input cluster (in cluster_key) whose cells are the trajectory root.

  • root_cells (array) (optional) Explicit root cell ids (obs_names); alternative to root_cluster.

  • num_dim (integer) (optional) PCA dimensions for preprocess_cds (default 50).

Example Usage:

query = {
    "name": "run_monocle3_pseudotime",
    "arguments": {
        "adata_path": "example_value"
    }
}
result = tu.run(query)