papaa@0.1.9_PI3K_OG_model_tutorial

statistics-aberrant_pi3k_pathway_analysis/main-workflow

Author(s)

version Version
1
last_modification Last updated
May 6, 2021
license License
None Specified, defaults to CC-BY-4.0
galaxy-tags Tags
statistics
classification
ml
cancer

Features

Tutorial
hands_on PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis

Workflow Testing
Tests: ❌
Results: Not yet automated
FAIRness purl PURL
https://gxy.io/GTN:W00217
RO-Crate logo with flask Download Workflow RO-Crate Workflowhub cloud with gears logo View on WorkflowHub
Launch in Tutorial Mode question
galaxy-download Download
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

Input Label
Input dataset pancan_rnaseq_freeze.tsv
Input dataset pancan_mutation_freeze.tsv
Input dataset GSE69822_pi3k_sign.txt
Input dataset GSE69822_pi3k_trans.csv
Input dataset GDSC_CCLE_common_mut_cnv_binary.tsv.gz
Input dataset ccle_rnaseq_genes_rpkm_20180929_mod.tsv.gz
Input dataset GDSC_EXP_CCLE_converted_name.tsv.gz
Input dataset CCLE_MUT_CNA_AMP_DEL_binary_Revealer.tsv
Input dataset compounds_of_interest.txt
Input dataset cosmic_cancer_classification.tsv
Input dataset path_rtk_ras_pi3k_genes.txt
Input dataset copy_number_loss_status.tsv
Input dataset copy_number_gain_status.tsv
Input dataset mutation_burden_freeze.tsv
Input dataset sample_freeze.tsv
Input dataset mc3.v0.2.8.PUBLIC.maf
Input dataset CCLE_DepMap_18Q1_maf_20180207.txt
Input dataset gdsc1_ccle_pharm_fitted_dose_data.txt
Input dataset gdsc2_ccle_pharm_fitted_dose_data.txt

Outputs

From Output Label

Tools

Tool Links
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_alternative_genes_pathwaymapper/pancancer_alternative_genes_pathwaymapper/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_apply_weights/pancancer_apply_weights/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_classifier/pancancer_classifier/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_compare_within_models/pancancer_compare_within_models/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_external_sample_status_prediction/pancancer_external_sample_status_prediction/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_map_mutation_class/pancancer_map_mutation_class/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_pathway_count_heatmaps/pancancer_pathway_count_heatmaps/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_targene_cell_line_predictions/pancancer_targene_cell_line_predictions/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_targene_pharmacology/pancancer_targene_pharmacology/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_targene_summary_figures/pancancer_targene_summary_figures/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_visualize_decisions/pancancer_visualize_decisions/0.1.9 View in ToolShed
toolshed.g2.bx.psu.edu/repos/vijay/pancancer_within_disease_analysis/pancancer_within_disease_analysis/0.1.9 View in ToolShed

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

Below are the instructions for importing these workflows directly into your Galaxy server of choice to start using them!
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:

Video: Importing a workflow from URL

Version History

Version Commit Time Comments
5 7ea290c2b 2020-12-18 19:00:40 aberrant_pi3k_activity_tutorial update-18
4 34bebb50b 2020-12-18 15:16:05 reformat workflow and add tags and annotation
3 b0a2a036f 2020-12-17 15:58:17 aberrant_pi3k_activity_tutorial update-17
2 d49e2046f 2020-12-15 16:12:32 pi3k_aberrant_activity_tutorial update-12
1 5eeb04b88 2020-12-11 22:40:16 pi3k_aberrant activity tutorial edit-8

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