Monocle3 workflow

Run Workflow in Galaxy

• Galaxy Main
• Galaxy Europe
• Galaxy Australia
• Galaxy France
• Import to another server (≥23.0+ only!)

flowchart TD
  0["ℹ️ Input Dataset\nExpression matrix input"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nCell annotations input"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Dataset\nGene annotations input"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["Monocle3 create"];
  1 -->|output| 3;
  0 -->|output| 3;
  2 -->|output| 3;
  30db218d-f9e5-426d-ac9b-7823743ab5fb["Output\nMonocle3 create on input dataset(s): cds3"];
  3 --> 30db218d-f9e5-426d-ac9b-7823743ab5fb;
  style 30db218d-f9e5-426d-ac9b-7823743ab5fb stroke:#2c3143,stroke-width:4px;
  4["Monocle3 preprocess"];
  3 -->|output_rds| 4;
  38996ee0-0c00-4a01-ad18-f2e505eaf780["Output\nMonocle3 preprocess on input dataset(s): cds3"];
  4 --> 38996ee0-0c00-4a01-ad18-f2e505eaf780;
  style 38996ee0-0c00-4a01-ad18-f2e505eaf780 stroke:#2c3143,stroke-width:4px;
  5["Monocle3 reduceDim"];
  4 -->|output_rds| 5;
  21edf68f-72df-457d-aad7-c5fc5afa2b6b["Output\nMonocle3 reduceDim on input dataset(s): cds3"];
  5 --> 21edf68f-72df-457d-aad7-c5fc5afa2b6b;
  style 21edf68f-72df-457d-aad7-c5fc5afa2b6b stroke:#2c3143,stroke-width:4px;
  6["Monocle3 partition"];
  5 -->|output_rds| 6;
  fdb0e520-27c4-43a0-8f27-a25832a0816d["Output\nMonocle3 partition on input dataset(s): cds3"];
  6 --> fdb0e520-27c4-43a0-8f27-a25832a0816d;
  style fdb0e520-27c4-43a0-8f27-a25832a0816d stroke:#2c3143,stroke-width:4px;
  7["Plot cell type"];
  5 -->|output_rds| 7;
  5e309658-e20f-4da1-8841-818f16fa8f01["Output\nCell type plot"];
  7 --> 5e309658-e20f-4da1-8841-818f16fa8f01;
  style 5e309658-e20f-4da1-8841-818f16fa8f01 stroke:#2c3143,stroke-width:4px;
  8["Plot genotype"];
  5 -->|output_rds| 8;
  aca3b2c0-2d3b-4a4e-8732-cf6ddc7844a4["Output\nGenotype plot"];
  8 --> aca3b2c0-2d3b-4a4e-8732-cf6ddc7844a4;
  style aca3b2c0-2d3b-4a4e-8732-cf6ddc7844a4 stroke:#2c3143,stroke-width:4px;
  9["Plot batch"];
  5 -->|output_rds| 9;
  c8eec2e4-1a64-47bb-812b-4002968c57c8["Output\nBatch plot"];
  9 --> c8eec2e4-1a64-47bb-812b-4002968c57c8;
  style c8eec2e4-1a64-47bb-812b-4002968c57c8 stroke:#2c3143,stroke-width:4px;
  10["Plot sex"];
  5 -->|output_rds| 10;
  c75954dd-34d5-4a20-bcf3-e5cd92d74885["Output\nSex plot"];
  10 --> c75954dd-34d5-4a20-bcf3-e5cd92d74885;
  style c75954dd-34d5-4a20-bcf3-e5cd92d74885 stroke:#2c3143,stroke-width:4px;
  11["Monocle3 top markers"];
  6 -->|output_rds| 11;
  12["Plot genes"];
  6 -->|output_rds| 12;
  f91c22a3-8c6d-432a-92c5-cb010115fa86["Output\nGene expression plot"];
  12 --> f91c22a3-8c6d-432a-92c5-cb010115fa86;
  style f91c22a3-8c6d-432a-92c5-cb010115fa86 stroke:#2c3143,stroke-width:4px;
  13["Plot partition"];
  6 -->|output_rds| 13;
  f4c5f89b-edd7-489c-b4a2-a7a3e7d6a89d["Output\nPartition plot"];
  13 --> f4c5f89b-edd7-489c-b4a2-a7a3e7d6a89d;
  style f4c5f89b-edd7-489c-b4a2-a7a3e7d6a89d stroke:#2c3143,stroke-width:4px;
  14["Plot cluster"];
  6 -->|output_rds| 14;
  a2c1a628-d79d-4e37-ad33-de92033021a8["Output\nCluster plot"];
  14 --> a2c1a628-d79d-4e37-ad33-de92033021a8;
  style a2c1a628-d79d-4e37-ad33-de92033021a8 stroke:#2c3143,stroke-width:4px;
  15["Monocle3 learnGraph"];
  6 -->|output_rds| 15;
  ed2003aa-4680-409f-8d79-e79b878a786c["Output\nMonocle3 learnGraph on input dataset(s): cds3"];
  15 --> ed2003aa-4680-409f-8d79-e79b878a786c;
  style ed2003aa-4680-409f-8d79-e79b878a786c stroke:#2c3143,stroke-width:4px;
  16["Plot learned trajectory"];
  15 -->|output_rds| 16;
  6fa555f1-b4f2-4e5d-bce1-ed462d8e056d["Output\nCell types & learned trajectory path plot"];
  16 --> 6fa555f1-b4f2-4e5d-bce1-ed462d8e056d;
  style 6fa555f1-b4f2-4e5d-bce1-ed462d8e056d stroke:#2c3143,stroke-width:4px;
  17["Monocle3 orderCells"];
  15 -->|output_rds| 17;
  96d10412-3d7f-4970-b541-6f4367fa3f37["Output\nMonocle3 orderCells on input dataset(s): cds3"];
  17 --> 96d10412-3d7f-4970-b541-6f4367fa3f37;
  style 96d10412-3d7f-4970-b541-6f4367fa3f37 stroke:#2c3143,stroke-width:4px;
  18["Monocle3 diffExp"];
  17 -->|output_rds| 18;
  db1dc457-2d6e-47cc-b7ce-d0a4d23e9498["Output\nDifferential expression of genes - table"];
  18 --> db1dc457-2d6e-47cc-b7ce-d0a4d23e9498;
  style db1dc457-2d6e-47cc-b7ce-d0a4d23e9498 stroke:#2c3143,stroke-width:4px;
  19["Plot pseudotime"];
  17 -->|output_rds| 19;
  d1b3efd2-0a81-4dad-8652-589c2678fbaa["Output\nPseudotime plot"];
  19 --> d1b3efd2-0a81-4dad-8652-589c2678fbaa;
  style d1b3efd2-0a81-4dad-8652-589c2678fbaa stroke:#2c3143,stroke-width:4px;

To use these workflows in Galaxy you can either click the links to download the workflows, or you can right-click and copy the link to the workflow which can be used in the Galaxy form to import workflows.

Importing into Galaxy

Hands-on: Importing a workflow

• Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
• Click on galaxy-upload Import at the top-right of the screen
• Provide your workflow
  • Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
  • Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
• Click the Import workflow button

Below is a short video demonstrating how to import a workflow from GitHub using this procedure:

Version History

For Admins

Installing the workflow tools

wget https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/workflows/Galaxy-Workflow-Monocle3_workflow.ga -O workflow.ga
workflow-to-tools -w workflow.ga -o tools.yaml
shed-tools install -g GALAXY -a API_KEY -t tools.yaml
workflow-install -g GALAXY -a API_KEY -w workflow.ga --publish-workflows