Clustering 3k PBMC with Scanpy

Run Workflow in Galaxy

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• Import to another server (≥23.0+ only!)

flowchart TD
  0["ℹ️ Input Dataset\nBarcodes"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nGenes"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Dataset\nMatrix"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["Import Anndata"];
  2 -->|output| 3;
  0 -->|output| 3;
  1 -->|output| 3;
  4["Inspect AnnData"];
  3 -->|anndata| 4;
  5["Scanpy filter"];
  3 -->|anndata| 5;
  6["Inspect AnnData"];
  3 -->|anndata| 6;
  7["Inspect AnnData"];
  3 -->|anndata| 7;
  8["Inspect AnnData"];
  3 -->|anndata| 8;
  9["Manipulate AnnData"];
  5 -->|anndata_out| 9;
  10["Scanpy Inspect and manipulate"];
  9 -->|anndata| 10;
  11["Inspect AnnData"];
  9 -->|anndata| 11;
  12["Scanpy plot"];
  10 -->|anndata_out| 12;
  a13ef439-dade-4aa1-91d3-0e050da961af["Output\npl_scatter_total_counts_vs_n_genes_by_counts"];
  12 --> a13ef439-dade-4aa1-91d3-0e050da961af;
  style a13ef439-dade-4aa1-91d3-0e050da961af stroke:#2c3143,stroke-width:4px;
  13["Scanpy filter"];
  10 -->|anndata_out| 13;
  14["Scanpy plot"];
  10 -->|anndata_out| 14;
  f3ed5faf-e274-4575-91d4-c6a49d1aef6d["Output\npl_scatter_n_genes_by_counts_vs_pct_mito"];
  14 --> f3ed5faf-e274-4575-91d4-c6a49d1aef6d;
  style f3ed5faf-e274-4575-91d4-c6a49d1aef6d stroke:#2c3143,stroke-width:4px;
  15["Scanpy plot"];
  10 -->|anndata_out| 15;
  51634f2e-a033-4ef8-a785-079c415ccfae["Output\npl_violin"];
  15 --> 51634f2e-a033-4ef8-a785-079c415ccfae;
  style 51634f2e-a033-4ef8-a785-079c415ccfae stroke:#2c3143,stroke-width:4px;
  16["Scanpy filter"];
  13 -->|anndata_out| 16;
  17["Manipulate AnnData"];
  16 -->|anndata_out| 17;
  18["Scanpy normalize"];
  17 -->|anndata| 18;
  19["Scanpy Inspect and manipulate"];
  18 -->|anndata_out| 19;
  20["Manipulate AnnData"];
  19 -->|anndata_out| 20;
  21["Scanpy filter"];
  20 -->|anndata| 21;
  22["Manipulate AnnData"];
  21 -->|anndata_out| 22;
  23["Scanpy plot"];
  21 -->|anndata_out| 23;
  7c1818c2-b46f-4274-b309-68b45c7387f9["Output\npl_highly_variable"];
  23 --> 7c1818c2-b46f-4274-b309-68b45c7387f9;
  style 7c1818c2-b46f-4274-b309-68b45c7387f9 stroke:#2c3143,stroke-width:4px;
  24["Scanpy remove confounders"];
  22 -->|anndata| 24;
  25["Scanpy Inspect and manipulate"];
  24 -->|anndata_out| 25;
  26["Scanpy cluster, embed"];
  25 -->|anndata_out| 26;
  27["Scanpy plot"];
  26 -->|anndata_out| 27;
  9fa13332-20f5-4cf3-8b21-f4cf0be0f7d3["Output\npl_pca_overview"];
  27 --> 9fa13332-20f5-4cf3-8b21-f4cf0be0f7d3;
  style 9fa13332-20f5-4cf3-8b21-f4cf0be0f7d3 stroke:#2c3143,stroke-width:4px;
  28["Scanpy plot"];
  26 -->|anndata_out| 28;
  23fad9ca-caed-4cb5-bf9c-77dccd7fff09["Output\npl_pca_overview_genes"];
  28 --> 23fad9ca-caed-4cb5-bf9c-77dccd7fff09;
  style 23fad9ca-caed-4cb5-bf9c-77dccd7fff09 stroke:#2c3143,stroke-width:4px;
  29["Scanpy plot"];
  26 -->|anndata_out| 29;
  d80530df-fb7b-4d72-8c12-ae09d9eccc7c["Output\npl_pca_loadings"];
  29 --> d80530df-fb7b-4d72-8c12-ae09d9eccc7c;
  style d80530df-fb7b-4d72-8c12-ae09d9eccc7c stroke:#2c3143,stroke-width:4px;
  30["Scanpy Inspect and manipulate"];
  26 -->|anndata_out| 30;
  31["Scanpy plot"];
  26 -->|anndata_out| 31;
  2e3480a4-1dfb-4e43-b70c-459d155853e9["Output\npl_pca_variance_ratio"];
  31 --> 2e3480a4-1dfb-4e43-b70c-459d155853e9;
  style 2e3480a4-1dfb-4e43-b70c-459d155853e9 stroke:#2c3143,stroke-width:4px;
  32["Scanpy cluster, embed"];
  30 -->|anndata_out| 32;
  33["Scanpy plot"];
  32 -->|anndata_out| 33;
  504ea6ab-68a6-4c37-ae97-c58a99ff8381["Output\npl_umap_initial"];
  33 --> 504ea6ab-68a6-4c37-ae97-c58a99ff8381;
  style 504ea6ab-68a6-4c37-ae97-c58a99ff8381 stroke:#2c3143,stroke-width:4px;
  34["Scanpy cluster, embed"];
  32 -->|anndata_out| 34;
  35["Scanpy plot"];
  34 -->|anndata_out| 35;
  c674ba29-f097-4242-8dc2-b22441233e73["Output\npl_umap_louvain"];
  35 --> c674ba29-f097-4242-8dc2-b22441233e73;
  style c674ba29-f097-4242-8dc2-b22441233e73 stroke:#2c3143,stroke-width:4px;
  36["Scanpy Inspect and manipulate"];
  34 -->|anndata_out| 36;
  37["Scanpy Inspect and manipulate"];
  34 -->|anndata_out| 37;
  38["Inspect clusters"];
  36 -->|anndata_out| 38;
  39["Scanpy plot"];
  36 -->|anndata_out| 39;
  249d7c38-140c-403d-a0ed-effe1a17fdd6["Output\npl_rank_gene_groups_t_test_wilcoxon_test"];
  39 --> 249d7c38-140c-403d-a0ed-effe1a17fdd6;
  style 249d7c38-140c-403d-a0ed-effe1a17fdd6 stroke:#2c3143,stroke-width:4px;
  40["Scanpy plot"];
  36 -->|anndata_out| 40;
  c1395d95-9c53-48c9-972e-cf93116e85db["Output\npl_rank_genes_heatmap"];
  40 --> c1395d95-9c53-48c9-972e-cf93116e85db;
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  41["Scanpy plot"];
  36 -->|anndata_out| 41;
  6b30a9d8-01be-4757-999d-cade6b516df6["Output\npl_stacked_violin_marker_genes"];
  41 --> 6b30a9d8-01be-4757-999d-cade6b516df6;
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  42["Scanpy plot"];
  36 -->|anndata_out| 42;
  30bc8ca7-37b7-4efc-b442-8bc4cb986d7e["Output\npl_umap_marker_genes"];
  42 --> 30bc8ca7-37b7-4efc-b442-8bc4cb986d7e;
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  43["Scanpy Inspect and manipulate"];
  36 -->|anndata_out| 43;
  44["Inspect AnnData"];
  36 -->|anndata_out| 44;
  9092ecc8-cace-45c4-8efa-bd4d27275060["Output\nuns_rank_genes_groups_names_wilcoxon_test"];
  44 --> 9092ecc8-cace-45c4-8efa-bd4d27275060;
  style 9092ecc8-cace-45c4-8efa-bd4d27275060 stroke:#2c3143,stroke-width:4px;
  45["Manipulate AnnData"];
  36 -->|anndata_out| 45;
  545a105c-1202-4209-aff2-201fd953e398["Output\nanndata_out"];
  45 --> 545a105c-1202-4209-aff2-201fd953e398;
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  46["Scanpy plot"];
  36 -->|anndata_out| 46;
  ad91827e-e8cc-45ec-b32f-935cf44a77d1["Output\npl_violin_louvain"];
  46 --> ad91827e-e8cc-45ec-b32f-935cf44a77d1;
  style ad91827e-e8cc-45ec-b32f-935cf44a77d1 stroke:#2c3143,stroke-width:4px;
  47["Scanpy plot"];
  37 -->|anndata_out| 47;
  4011556e-85f9-470e-b902-7ef9df11fef5["Output\npl_rank_gene_groups_t_test"];
  47 --> 4011556e-85f9-470e-b902-7ef9df11fef5;
  style 4011556e-85f9-470e-b902-7ef9df11fef5 stroke:#2c3143,stroke-width:4px;
  48["Inspect AnnData"];
  37 -->|anndata_out| 48;
  55f664c3-b771-4115-890f-3eb4637813b0["Output\nuns_rank_genes_groups_names_t_test"];
  48 --> 55f664c3-b771-4115-890f-3eb4637813b0;
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  49["Datamash"];
  38 -->|obs| 49;
  10693ba5-026d-443b-aa80-b4dedbf4f940["Output\ncells_per_cluster"];
  49 --> 10693ba5-026d-443b-aa80-b4dedbf4f940;
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  50["Scanpy plot"];
  43 -->|anndata_out| 50;
  51878bf2-d055-4acc-b365-cc7f7a673f66["Output\npl_rank_gene_groups_0_vs_1"];
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  51["Scanpy plot"];
  43 -->|anndata_out| 51;
  10a46aa1-5685-4480-a6eb-362f32b91fa1["Output\npl_rank_gene_groups_violin_0_vs_1"];
  51 --> 10a46aa1-5685-4480-a6eb-362f32b91fa1;
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  52["Scanpy plot"];
  45 -->|anndata| 52;
  c8ae2d77-361a-41e0-b238-0f494d12b6d7["Output\npl_umap_celltypes"];
  52 --> c8ae2d77-361a-41e0-b238-0f494d12b6d7;
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  53["Scanpy plot"];
  45 -->|anndata| 53;
  a3ceb616-64b8-4c79-ba94-4272a5bd747f["Output\npl_dotplot_marker_genes"];
  53 --> a3ceb616-64b8-4c79-ba94-4272a5bd747f;
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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

• 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-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy.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