The cBioPortal is an open-access web platform that provides intuitive access to large-scale cancer genomics datasets Gao et al. 2013 cbioPortal community. Originally developed to make complex molecular profiling data more accessible to the broader research community, cBioPortal hosts data from thousands of cancer studies and tens of thousands of tumor samples.

In this tutorial, we will work with data from the METABRIC consortium metabric community, one of the landmark breast cancer genomics studies available through cBioPortal. This dataset contains comprehensive molecular and clinical data from over 2,000 breast cancer patients, including gene expression profiles, copy number alterations, mutation data, and clinical outcomes.

Agenda

In this tutorial, we will cover:

1. Import data from CbioPortal
2. Data cleanup
   1. Clinical data
   2. Omics data
3. Split data to train and test
4. Conclusion

Import data from CbioPortal

Hands On: Import data from cBioPortal

1. Flexynesis cBioPortal import ( Galaxy version 0.2.20+galaxy3) with the following parameters:
   • “I certify that I am not using this tool for commercial purposes.”: Yes
   • “cBioPortal study ID”: brca_metabric

Question

1. What modalities are imported to Galaxy?

Solution

1. • Clinical data
   • Copy number alteration
   • Methylation
   • Gene expression
   • Mutation

Data cleanup

Now we need to clean up our data. This means removing comment lines in the matrix, removing duplicate samples and so on.

Clinical data

Hands On: Prepare clinical data

Here we’ll First extract the clinical data from the collection, then we remove the comment lines, and finally we remove the extra index column.

1. Extract dataset with the following parameters:
   • param-file “Input List”: datasets (output of Flexynesis cBioPortal import tool)
   • “How should a dataset be selected?”: Select by element identifier
     • “Element identifier:”: data_clinical_patient
2. Table Compute ( Galaxy version 1.2.4+galaxy2) with the following parameters:
   • “Input Single or Multiple Tables”: Single Table
     • param-file “Table”: data_clinical_patient (output of Extract dataset tool)
     • In “Advanced File Options “:
       • “Header begins at line N”: 4
     • “Type of table operation”: No operation (just reformat on output)
3. Advanced Cut ( Galaxy version 9.5+galaxy2) with the following parameters:
   • param-file “File to cut”: table (output of Table Compute tool)
   • “Operation”: Discard
   • “Cut by”: fields
     • “Is there a header for the data’s columns ?”: Yes
       • “List of Fields”: c1:
4. Rename the data clinical data - cleaned

Question

1. How many samples are there in the clinical data?

Solution

1. Click on the data, you will see the data has 2510 lines, so 2509 samples.

Omics data

Time to prepare our omics data. We are interested in mutation and gene expression.

Hands On: Prepare gene expression data

Here we’ll First extract the clinical data from the collection, then we remove duplicate genes from the matrix, and finally we remove the ENTREZ Ids.

1. Extract dataset with the following parameters:
   • param-file “Input List”: data (output of Flexynesis cBioPortal import tool)
   • “How should a dataset be selected?”: Select by element identifier
     • “Element identifier:”: data_mrna_illumina_microarray
2. Sort ( Galaxy version 9.5+galaxy2) with the following parameters:
   • param-file “Sort Query”: data_mrna_illumina_microarray (output of Extract dataset tool)
   • “Number of header lines”: 1
   • In “Column selections”:
     • param-repeat “Insert Column selections”
       • “on column”: c1
   • “Output unique values”: Yes
3. Advanced Cut ( Galaxy version 9.5+galaxy2) with the following parameters:
   • param-file “File to cut”: output (output of Sort tool)
   • “Operation”: Discard
   • “Cut by”: fields
     • “Is there a header for the data’s columns ?”: Yes
       • “List of Fields”: c2: Entrez_Gene_Id
4. Rename the data expression data - cleaned

Question

1. How many samples and genes are there in the gene expression data?

Solution

1. Click on the data, you will see the data has 20386 lines and 1981 columns, so 20385 genes and 1980 samples.

Hands On: Prepare mutation data

Here we’ll First extract the clinical data from the collection, then we remove comment lines from the matrix, and remove the extra index column, and finally, we create a binarized matrix which indicates the number of mutations per genes.

1. Extract dataset with the following parameters:
   • param-file “Input List”: data (output of Flexynesis cBioPortal import tool)
   • “How should a dataset be selected?”: Select by element identifier
     • “Element identifier:”: data_mutations
2. Table Compute ( Galaxy version 1.2.4+galaxy2) with the following parameters:
   • “Input Single or Multiple Tables”: Single Table
     • param-file “Table”: data_mutations (output of Extract dataset tool)
     • In “Advanced File Options “:
       • “Header begins at line N”: 1
     • “Type of table operation”: No operation (just reformat on output)
3. Advanced Cut ( Galaxy version 9.5+galaxy2) with the following parameters:
   • param-file “File to cut”: table (output of Table Compute tool)
   • “Operation”: Discard
   • “Cut by”: fields
     • “Is there a header for the data’s columns ?”: Yes
       • “List of Fields”: c1:
4. Flexynesis utils ( Galaxy version 0.2.20+galaxy3) with the following parameters:
   • “I certify that I am not using this tool for commercial purposes.”: Yes
   • “Flexynesis utils”: Binarize mutation data
     • param-file “Mutation data”: table (output of Advanced Cut tool)
     • “Column in the mutation file with genes”: Column: 1
     • “Column in the mutation file with samples”: Column: 17
5. Rename the data mutation data - cleaned

Question

1. How many samples and genes are there in the mutation data?

Solution

1. Click on the data, you will see the data has 174 lines and 2370 columns, so 174 genes and 2370 samples.

Split data to train and test

In the last step we split our clinical and omics data into train and test with ratio of 0.7 (70% as training and 30% as test)

Hands On: Task description

1. Flexynesis utils ( Galaxy version 0.2.20+galaxy2) with the following parameters:
   • “I certify that I am not using this tool for commercial purposes.”: Yes
   • “Flexynesis utils”: Split data to train and test
     • param-file “Clinical data”: clinical data - cleaned (output of Advanced Cut tool)
     • param-files “Omics data”: expression data - cleaned (output of Advanced Cut tool), mutation data - cleaned (output of Flexynesis utils tool)

Conclusion

In this tutorial, we showed how to download and prepare multi-modal cancer genomics data from cBioPortal for integration using Flexynesis. Working with the METABRIC breast cancer dataset, we covered the complete data preparation pipeline from raw data access to analysis-ready formats.

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Key points

• cBioportal is a repository for accessible and interpretable cancer genomic data

• Flexynesis comprehensive tool can be used to make data ready for integration.

Frequently Asked Questions

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• Topic FAQs
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• GTN Matrix Chat
• Galaxy Help Forum

References

1. Gao, J., B. A. Aksoy, U. Dogrusoz, G. Dresdner, B. Gross et al., 2013 Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Science Signaling 6: 10.1126/scisignal.2004088
2. community, cbioPortal GcBioPortal for cancer genomics. https://www.cbioportal.org/
3. community, metabric METABRIC. https://ega-archive.org/studies/EGAS00000000083

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Citing this Tutorial

1. Amirhossein Naghsh Nilchi, Polina Polunina, Björn Grüning, Prepare data from CbioPortal for Flexynesis integration (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/statistics/tutorials/flexynesis_cbio_import/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{statistics-flexynesis_cbio_import,
author = "Amirhossein Naghsh Nilchi and Polina Polunina and Björn Grüning",
	title = "Prepare data from CbioPortal for Flexynesis integration (Galaxy Training Materials)",
	year = "",
	month = "",
	day = "",
	url = "\url{https://training.galaxyproject.org/training-material/topics/statistics/tutorials/flexynesis_cbio_import/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol}
}

                   

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/statistics/tutorials/flexynesis_cbio_import/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools: []

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