Multi-omic Integration and Analysis of cBioPortal and TCGA data with MultiAssayExperiment

Quick-install

BiocManager::install("waldronlab/MultiAssayWorkshop", dependencies=TRUE, build_vignettes=TRUE)

Instructor names and contact information

Marcel Ramos, Ludwig Geistlinger, and Levi Waldron

CUNY School of Public Health

55 West 125th Street

New York, NY 10027

Workshop Description

This workshop demonstrates the leveraging of public multi-omics databases, such as cBioPortal and The Cancer Genome Atlas (TCGA), through the use of the cBioPortalData and curatedTCGAData experiment data packages. It provides users with the basics of data management, using the MultiAssayExperiment data class and the TCGAutils utility package, and example analyses of multiple assays associated with a single set of biological specimens. In addition to providing a basic overview of key data classes, such as MultiAssayExperiment and RaggedExperiment, this workshop intends to provide an overview of cBioPortalData and curatedTCGAData experiment data packages and TCGAutils functionality aimed at enhancing the ease-of-use of TCGA data.

Pre-requisites

• Basic knowledge of R syntax
• Familiarity with the GRanges and SummarizedExperiment classes
• Familiarity with ’omics data types including copy number and gene expression

Workshop Participation

Students will have a chance to build a MultiAssayExperiment object from scratch, to work with complex objects provided by the curatedTCGAData package and to test functionality provided in the TCGAutils package.

R/Bioconductor packages used

• MultiAssayExperiment
• curatedTCGAData
• cBioPortalData
• TCGAutils
• RaggedExperiment
• SummarizedExperiment

Time outline

1h 45m total

Activity	Time
Overview of key data classes	25m
Building a MultiAssayExperiment from scratch	10m
Creating and importing TCGA multi-assay datasets	10m
Subsetting and reshaping multi-assay data	20m
Plotting, correlation, and other analyses	20m
Introduction to curatedTCGAData, cBioPortalData, and TCGAutils	20m

Workshop goals and objectives

Learning goals

• overview common data classes available in Bioconductor
• understand appropriate data structures for different ’omics data types
• identify advantages of providing integrative data in an analysis-ready platform
• gain familiarity with cBioPortalData and curatedTCGAData
• explore available functionality in TCGAutils for the management and coordination of multi-omic data

Learning objectives

• use curatedTCGAData and cBioPortalData to create on-the-fly multi-omics data objects
• apply knowlege of MultiAssayExperiment to represent other multi’omics data
• understand basic data management techniques with MultiAssayExperiment such as subsetting, reshaping, growing, and extraction
• adapt MultiAssayExperiment data with packages for differential expression, machine learning, and plotting