papaa@0.1.9_PI3K_OG_model_tutorial

Run Workflow in Galaxy

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

flowchart TD
  0["ℹ️ Input Dataset\npancan_rnaseq_freeze.tsv"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\npancan_mutation_freeze.tsv"];
  style 1 stroke:#2c3143,stroke-width:4px;
  10["ℹ️ Input Dataset\nGSE69822_pi3k_sign.txt"];
  style 10 stroke:#2c3143,stroke-width:4px;
  11["ℹ️ Input Dataset\nGSE69822_pi3k_trans.csv"];
  style 11 stroke:#2c3143,stroke-width:4px;
  12["ℹ️ Input Dataset\nGDSC_CCLE_common_mut_cnv_binary.tsv.gz"];
  style 12 stroke:#2c3143,stroke-width:4px;
  13["ℹ️ Input Dataset\nccle_rnaseq_genes_rpkm_20180929_mod.tsv.gz"];
  style 13 stroke:#2c3143,stroke-width:4px;
  14["ℹ️ Input Dataset\nGDSC_EXP_CCLE_converted_name.tsv.gz"];
  style 14 stroke:#2c3143,stroke-width:4px;
  15["ℹ️ Input Dataset\nCCLE_MUT_CNA_AMP_DEL_binary_Revealer.tsv"];
  style 15 stroke:#2c3143,stroke-width:4px;
  16["ℹ️ Input Dataset\ncompounds_of_interest.txt"];
  style 16 stroke:#2c3143,stroke-width:4px;
  17["ℹ️ Input Dataset\ncosmic_cancer_classification.tsv"];
  style 17 stroke:#2c3143,stroke-width:4px;
  18["ℹ️ Input Dataset\npath_rtk_ras_pi3k_genes.txt"];
  style 18 stroke:#2c3143,stroke-width:4px;
  19["PAPAA: PanCancer classifier"];
  17 -->|output| 19;
  3 -->|output| 19;
  2 -->|output| 19;
  1 -->|output| 19;
  4 -->|output| 19;
  5 -->|output| 19;
  0 -->|output| 19;
  2["ℹ️ Input Dataset\ncopy_number_loss_status.tsv"];
  style 2 stroke:#2c3143,stroke-width:4px;
  20["PAPAA: PanCancer within disease analysis"];
  17 -->|output| 20;
  3 -->|output| 20;
  2 -->|output| 20;
  1 -->|output| 20;
  4 -->|output| 20;
  5 -->|output| 20;
  0 -->|output| 20;
  21["PAPAA: PanCancer apply weights"];
  17 -->|output| 21;
  3 -->|output| 21;
  2 -->|output| 21;
  1 -->|output| 21;
  4 -->|output| 21;
  5 -->|output| 21;
  19 -->|classifier_coefficients| 21;
  19 -->|classifier_summary| 21;
  0 -->|output| 21;
  22["PAPAA: PanCancer external sample status prediction"];
  19 -->|classifier_summary| 22;
  11 -->|output| 22;
  19 -->|classifier_coefficients| 22;
  10 -->|output| 22;
  23["PAPAA: PanCancer compare within models"];
  19 -->|classifier_coefficients| 23;
  19 -->|classifier_summary| 23;
  20 -->|classifier_coefficients| 23;
  20 -->|classifier_summary| 23;
  24["PAPAA: PanCancer visualize decisions"];
  21 -->|classifier_decisions| 24;
  25["PAPAA: PanCancer alternative genes pathwaymapper"];
  21 -->|classifier_decisions| 25;
  3 -->|output| 25;
  2 -->|output| 25;
  1 -->|output| 25;
  5 -->|output| 25;
  18 -->|output| 25;
  26["PAPAA: PanCancer map mutation class"];
  21 -->|classifier_decisions| 26;
  3 -->|output| 26;
  2 -->|output| 26;
  6 -->|output| 26;
  18 -->|output| 26;
  27["PAPAA: PanCancer pathway count heatmaps"];
  25 -->|all_gene_metric_ranks| 27;
  21 -->|classifier_decisions| 27;
  17 -->|output| 27;
  3 -->|output| 27;
  2 -->|output| 27;
  1 -->|output| 27;
  4 -->|output| 27;
  5 -->|output| 27;
  18 -->|output| 27;
  25 -->|pathway_metrics_pathwaymapper| 27;
  0 -->|output| 27;
  28["PAPAA: PanCancer targene summary figures"];
  25 -->|all_gene_metric_ranks| 28;
  19 -->|classifier_summary| 28;
  26 -->|mutation_classification_scores| 28;
  19 -->|classifier_coefficients| 28;
  27 -->|path_events_per_sample| 28;
  19 -->|summary_counts| 28;
  29["PAPAA: PanCancer targene cell line predictions"];
  28 -->|amino_acid_mutation_scores| 29;
  7 -->|output| 29;
  15 -->|output| 29;
  13 -->|output| 29;
  19 -->|classifier_summary| 29;
  8 -->|output| 29;
  9 -->|output| 29;
  12 -->|output| 29;
  14 -->|output| 29;
  28 -->|nucleotide_mutation_scores| 29;
  19 -->|classifier_coefficients| 29;
  18 -->|output| 29;
  3["ℹ️ Input Dataset\ncopy_number_gain_status.tsv"];
  style 3 stroke:#2c3143,stroke-width:4px;
  30["PAPAA: PanCancer targene pharmacology"];
  16 -->|output| 30;
  29 -->|gdsc1_ccle_targene_pharmacology_predictions| 30;
  29 -->|gdsc1_targene_pharmacology_predictions| 30;
  29 -->|gdsc2_ccle_targene_pharmacology_predictions| 30;
  29 -->|gdsc2_targene_pharmacology_predictions| 30;
  4["ℹ️ Input Dataset\nmutation_burden_freeze.tsv"];
  style 4 stroke:#2c3143,stroke-width:4px;
  5["ℹ️ Input Dataset\nsample_freeze.tsv"];
  style 5 stroke:#2c3143,stroke-width:4px;
  6["ℹ️ Input Dataset\nmc3.v0.2.8.PUBLIC.maf"];
  style 6 stroke:#2c3143,stroke-width:4px;
  7["ℹ️ Input Dataset\nCCLE_DepMap_18Q1_maf_20180207.txt"];
  style 7 stroke:#2c3143,stroke-width:4px;
  8["ℹ️ Input Dataset\ngdsc1_ccle_pharm_fitted_dose_data.txt"];
  style 8 stroke:#2c3143,stroke-width:4px;
  9["ℹ️ Input Dataset\ngdsc2_ccle_pharm_fitted_dose_data.txt"];
  style 9 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

• 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/statistics/tutorials/aberrant_pi3k_pathway_analysis/workflows/main_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