Normalising_clustring_and_spatial_melanoma_without_scaling_xenium_workflow

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flowchart TD
  0["ℹ️ Input Dataset\nFiltered AnnData table"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nmelanoma_roi.spatialdata.zip"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["Filtered AnnData with counts layer"];
  0 -->|output| 2;
  0 -->|output| 2;
  896e63f9-0302-45c1-858c-98ac0fe4b317["Output\nFiltered AnnData with counts layer"];
  2 --> 896e63f9-0302-45c1-858c-98ac0fe4b317;
  style 896e63f9-0302-45c1-858c-98ac0fe4b317 stroke:#2c3143,stroke-width:4px;
  3["Normalised AnnData"];
  0 -->|output| 3;
  85a4008b-7b4b-413e-b9c5-1be6ca079a5d["Output\nNormalised AnnData"];
  3 --> 85a4008b-7b4b-413e-b9c5-1be6ca079a5d;
  style 85a4008b-7b4b-413e-b9c5-1be6ca079a5d stroke:#2c3143,stroke-width:4px;
  4["Log-normalised AnnData"];
  3 -->|anndata_out| 4;
  3687cbcd-b5f7-4700-b60b-4b498f1139ae["Output\nLog-normalised AnnData"];
  4 --> 3687cbcd-b5f7-4700-b60b-4b498f1139ae;
  style 3687cbcd-b5f7-4700-b60b-4b498f1139ae stroke:#2c3143,stroke-width:4px;
  5["AnnData with HVGs"];
  4 -->|anndata_out| 5;
  46c333ea-f037-4581-bc48-2607fd0b3f40["Output\nAnnData with HVGs"];
  5 --> 46c333ea-f037-4581-bc48-2607fd0b3f40;
  style 46c333ea-f037-4581-bc48-2607fd0b3f40 stroke:#2c3143,stroke-width:4px;
  6["Plot HVGs"];
  5 -->|anndata_out| 6;
  1c93b4c7-ce8f-4b23-896e-6c5bb5c4ed83["Output\nPlot HVGs"];
  6 --> 1c93b4c7-ce8f-4b23-896e-6c5bb5c4ed83;
  style 1c93b4c7-ce8f-4b23-896e-6c5bb5c4ed83 stroke:#2c3143,stroke-width:4px;
  7["AnnData with PCA"];
  5 -->|anndata_out| 7;
  a1e03d97-abfe-4dba-bbc8-73c09f758aea["Output\nAnnData with PCA"];
  7 --> a1e03d97-abfe-4dba-bbc8-73c09f758aea;
  style a1e03d97-abfe-4dba-bbc8-73c09f758aea stroke:#2c3143,stroke-width:4px;
  8["Plot PCA"];
  7 -->|anndata_out| 8;
  8e21189e-a7b6-4b75-8bca-3f95bb87beb7["Output\nPlot PCA"];
  8 --> 8e21189e-a7b6-4b75-8bca-3f95bb87beb7;
  style 8e21189e-a7b6-4b75-8bca-3f95bb87beb7 stroke:#2c3143,stroke-width:4px;
  9["Compute a neighborhood graph of observations"];
  7 -->|anndata_out| 9;
  0b38fe1d-aa0a-4f69-a5d1-6b4429f28d53["Output\nCompute a neighborhood graph of observations"];
  9 --> 0b38fe1d-aa0a-4f69-a5d1-6b4429f28d53;
  style 0b38fe1d-aa0a-4f69-a5d1-6b4429f28d53 stroke:#2c3143,stroke-width:4px;
  10["AnnData with UMAP"];
  9 -->|anndata_out| 10;
  0522e651-8ed4-4ccb-8aac-9181c01c7dd7["Output\nAnnData with UMAP"];
  10 --> 0522e651-8ed4-4ccb-8aac-9181c01c7dd7;
  style 0522e651-8ed4-4ccb-8aac-9181c01c7dd7 stroke:#2c3143,stroke-width:4px;
  11["Plot  UMAP"];
  10 -->|anndata_out| 11;
  1192456b-eeb1-42af-826e-8810f6c0fc70["Output\nPlot UMAP"];
  11 --> 1192456b-eeb1-42af-826e-8810f6c0fc70;
  style 1192456b-eeb1-42af-826e-8810f6c0fc70 stroke:#2c3143,stroke-width:4px;
  12["Anndata with leiden_res_0.2"];
  10 -->|anndata_out| 12;
  ffa51c91-4cf9-485b-847b-939661288e77["Output\nAnndata with leiden_res_0.2"];
  12 --> ffa51c91-4cf9-485b-847b-939661288e77;
  style ffa51c91-4cf9-485b-847b-939661288e77 stroke:#2c3143,stroke-width:4px;
  13["AnnData with Leiden comparison 0.2 and 0.4"];
  12 -->|anndata_out| 13;
  7aff6b81-ef43-46ea-a6b4-4f8bc14b9255["Output\nAnnData with Leiden comparison 0.2 and 0.4"];
  13 --> 7aff6b81-ef43-46ea-a6b4-4f8bc14b9255;
  style 7aff6b81-ef43-46ea-a6b4-4f8bc14b9255 stroke:#2c3143,stroke-width:4px;
  14["AnnData with Leiden comparison 0.2 and 0.4 and 0.6"];
  13 -->|anndata_out| 14;
  023bc215-b02a-45d5-974f-1cb06edef0bc["Output\nAnnData with Leiden comparison 0.2 and 0.4 and 0.6"];
  14 --> 023bc215-b02a-45d5-974f-1cb06edef0bc;
  style 023bc215-b02a-45d5-974f-1cb06edef0bc stroke:#2c3143,stroke-width:4px;
  15["Plot Leiden comparison"];
  14 -->|anndata_out| 15;
  b807cbdf-bc5e-438e-8af7-98752ef2ad86["Output\nPlot Leiden comparison"];
  15 --> b807cbdf-bc5e-438e-8af7-98752ef2ad86;
  style b807cbdf-bc5e-438e-8af7-98752ef2ad86 stroke:#2c3143,stroke-width:4px;
  16["Rank genes for characterizing groups"];
  14 -->|anndata_out| 16;
  abb57524-2c86-45f4-85cd-f2fb932f2976["Output\nRank genes for characterizing groups"];
  16 --> abb57524-2c86-45f4-85cd-f2fb932f2976;
  style abb57524-2c86-45f4-85cd-f2fb932f2976 stroke:#2c3143,stroke-width:4px;
  61a640ca-598d-44ab-a8d5-b86fe7b65f85["Output\nAnnData with markers"];
  16 --> 61a640ca-598d-44ab-a8d5-b86fe7b65f85;
  style 61a640ca-598d-44ab-a8d5-b86fe7b65f85 stroke:#2c3143,stroke-width:4px;
  17["Plot ranking of genes"];
  16 -->|anndata_out| 17;
  0d51b081-2a29-4162-93df-d9b1a0f16827["Output\nPlot ranking of genes"];
  17 --> 0d51b081-2a29-4162-93df-d9b1a0f16827;
  style 0d51b081-2a29-4162-93df-d9b1a0f16827 stroke:#2c3143,stroke-width:4px;
  18["AnnData with spatial neighbours"];
  16 -->|anndata_out| 18;
  2737e8c2-dd94-4d93-8b10-9367b7e63822["Output\nAnnData with spatial neighbours"];
  18 --> 2737e8c2-dd94-4d93-8b10-9367b7e63822;
  style 2737e8c2-dd94-4d93-8b10-9367b7e63822 stroke:#2c3143,stroke-width:4px;
  19["Squidpy"];
  18 -->|spatialdata_output_h5ad| 19;
  20["Squidpy"];
  19 -->|spatialdata_output_h5ad| 20;
  21["Squidpy Plot"];
  19 -->|spatialdata_output_h5ad| 21;
  ddbab838-226c-48cc-9a8c-d3674d0aec9e["Output\nPlot Centrality Scores"];
  21 --> ddbab838-226c-48cc-9a8c-d3674d0aec9e;
  style ddbab838-226c-48cc-9a8c-d3674d0aec9e stroke:#2c3143,stroke-width:4px;
  22["Squidpy Plot"];
  20 -->|spatialdata_output_h5ad| 22;
  2989733b-f744-4bb4-87dd-ae2a32fb2aad["Output\nPlot Neighborhood Enrichment"];
  22 --> 2989733b-f744-4bb4-87dd-ae2a32fb2aad;
  style 2989733b-f744-4bb4-87dd-ae2a32fb2aad stroke:#2c3143,stroke-width:4px;
  23["AnnData with Squidpy results"];
  20 -->|spatialdata_output_h5ad| 23;
  d920c2c9-4094-4206-9170-8595e505ace6["Output\nAnnData with Squidpy results"];
  23 --> d920c2c9-4094-4206-9170-8595e505ace6;
  style d920c2c9-4094-4206-9170-8595e505ace6 stroke:#2c3143,stroke-width:4px;
  24["CellTypist-annotated AnnData"];
  23 -->|spatialdata_output_h5ad| 24;
  bee75103-051f-4393-bbee-4992e5f70aba["Output\nCellTypist-annotated AnnData"];
  24 --> bee75103-051f-4393-bbee-4992e5f70aba;
  style bee75103-051f-4393-bbee-4992e5f70aba stroke:#2c3143,stroke-width:4px;
  4473a4e8-a9ce-4087-9b60-754f39e7e0ad["Output\nPlot CellTypist"];
  24 --> 4473a4e8-a9ce-4087-9b60-754f39e7e0ad;
  style 4473a4e8-a9ce-4087-9b60-754f39e7e0ad stroke:#2c3143,stroke-width:4px;
  25["SpatialData with Squidpy results"];
  1 -->|output| 25;
  23 -->|spatialdata_output_h5ad| 25;
  26["Final Lana Anndata"];
  24 -->|anndata_out| 26;
  54eace5b-62b0-46a3-9a11-32ad263291e0["Output\nFinal Liana Anndata"];
  26 --> 54eace5b-62b0-46a3-9a11-32ad263291e0;
  style 54eace5b-62b0-46a3-9a11-32ad263291e0 stroke:#2c3143,stroke-width:4px;
  27["Spatial_Plot_on_VGF_morons_HVG"];
  25 -->|spatialdata_output| 27;
  c0cd1651-e840-4386-bedf-176334e0db04["Output\nSpatial_Plot_on_VGF_morons_HVG"];
  27 --> c0cd1651-e840-4386-bedf-176334e0db04;
  style c0cd1651-e840-4386-bedf-176334e0db04 stroke:#2c3143,stroke-width:4px;
  28["SpatialData with Final metrics"];
  1 -->|output| 28;
  26 -->|anndata_out| 28;
  29["SpatialData Plot"];
  28 -->|spatialdata_output| 29;
  bf087a7d-2bb5-47e3-a94c-667bbabe894f["Output\nPlot spatial clusters"];
  29 --> bf087a7d-2bb5-47e3-a94c-667bbabe894f;
  style bf087a7d-2bb5-47e3-a94c-667bbabe894f stroke:#2c3143,stroke-width:4px;

Inputs

Outputs

Tools

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

1. Click on galaxy-workflows-activity Workflows in the Galaxy activity bar (on the left side of the screen, or in the top menu bar of older Galaxy instances). You will see a list of all your workflows
2. Click on galaxy-upload Import at the top-right of the screen
3. 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”
4. 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/spatial-melanoma-SPICA/workflows/Normalising-clustring-and-spatial-melanoma-without-scaling-xenium-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