MultiAssayExperiment: Integrative workflow for public multi-omics data

Marcel Ramos

Roswell Park Comprehensive Cancer Center, Buffalo, NY

Ludwig Geistlinger

CUNY School of Public Health, New York, NY

Levi Waldron

CUNY School of Public Health, New York, NY

July 02, 2025

Source: vignettes/Ramos_MultiAssayExperiment.Rmd

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

Workshop participation 

docker run -e PASSWORD=bioc -p 8787:8787 ghcr.io/waldronlab/multiassayworkshop:latest

Requirements: R/Bioconductor packages

The workshop uses a Docker container with Bioconductor version 3.21. If you would like to install Bioconductor on your computer at a later date, see the Bioconductor installation instructions.

Here is a list of packages that we will be using:

Citing MultiAssayExperiment

Please use these citations (Ramos et al. 2017), (Ramos et al. 2020), and (Ramos et al. 2023) when using MultiAssayExperiment, curatedTCGAData, cBioPortalData, or RaggedExperiment. Your citations are very much appreciated!

Overview of Packages

Package Description
MultiAssayExperiment Infrastructure package to represent multi-omics data
curatedTCGAData Downloads TCGA data from ExperimentHub in MultiAssayExperiment form
terraTCGAdata Obtains TCGA data within Terra cloud service as MultiAssayExperiment
cBioPortalData Access over 300 study datasets from the cBio Genomics Portal
TCGAutils Make use of utility functions for working with TCGA data
SingleCellMultiModal Obtain single cell data from various multi-modality studies
TENxIO Import 10X files as Bioconductor data classes

Key Packages

MultiAssayExperiment
  • provides an integrative representation for multi-omics data
  • modelled after the SummarizedExperiment representation for expression data
  • easy-to-use operations for manipulating multiple sets of data such as copy number alterations, mutations, proteomics, methylation, and more
MultiAssayExperiment object schematic

MultiAssayExperiment object schematic

cBioPortalData
  • R/Bioconductor interface to cBioPortal data
  • makes use of the revamped API with caching
  • queries are handled for the user in the background
  • easy-to-use interface (no knowledge of the cBioPortal data model required)
  • see getStudies() for a list of available studies
curatedTCGAData
  • Many tools exist for accessing and downloading The Cancer Genome Atlas data: RTCGAToolbox, GenomicDataCommons, TCGAbiolinks, cBioPortal website, Broad GDAC Firehose, and more
  • makes it easy to obtain user-friendly and integrative data at very little cognitive overhead
  • conveniently places data in the analysis platform of choice, R/Bioconductor
  • provides 33 different cancer types from the Broad GDAC Firehose
    • On-the-fly construction from ‘flat’ files
    • hg19 data
    • MultiAssayExperiment representations

Reference vignettes:

  • Available Studies – (curatedTCGAData section) A list of available cancer studies from TCGAutils::diseaseCodes.

  • OmicsTypes – A descriptive table of ’omics types in curatedTCGAData (thanks to Ludwig G. @lgeistlinger)

terraTCGAdata
  • Tool for making use of TCGA data within the Terra platform.
  • Go to https://app.terra.bio and sign in with your Google account (credit card required)
  • Select the latest RStudio / Bioconductor instance and install the package.
  • Make sure to authenticate Google Cloud and check AnVIL::gcloud_exists()
  • findTCGAworkspaces to list all the available workspaces with TCGA data
  • terraTCGAworkspace to set the TCGA dataset to download
  • terraTCGAdata - main function to download data
SingleCellMultiModal
  • Serves multi-modal datasets from GEO and other sources as MultiAssayExperiment data representations. Some representations are out of memory using the HDF5 format as well as the MTX format.
  • Available technologies are scNMT, 10X Multiome, seqFISH, (EC)CITEseq, SCoPE2 and others.
TENxIO
  • Allows import of 10X files as Bioconductor objects, e.g., TENxH5 will produce a SingleCellExperiment from an .h5 file.
  • Available for a number of formats such as MTX, TSV, peaks, fragments, and .tar.gz files.
TCGAutils
  • allows additional exploration, and manipulation of samples and metadata
  • User-friendly operations for subsetting, separating, converting, and reshaping of sample and feature TCGA data
  • developed specifically for TCGA data and curatedTCGAData outputs

It provides convenience / helper functions in three major areas:

  1. conversion / summarization of row annotations to genomic ranges
  2. identification and separation of samples
  3. translation and interpretation of TCGA identifiers

For the cheatsheet reference table, see the TCGAutils Cheatsheet.

To better understand how it all fits together, this schematic shows the relationship among all as part of the curatedTCGAData pipeline.

Schematic of curatedTCGAData Pipeline

Schematic of curatedTCGAData Pipeline

Data Classes

This section summarizes three fundamental data classes for the representation of multi-omics experiments.

(Ranged)SummarizedExperiment
A matrix-like container where rows represent features of interest and columns represent samples. The objects contain one or more assays, each represented by a matrix-like object of numeric or other mode.

A matrix-like container where rows represent features of interest and columns represent samples. The objects contain one or more assays, each represented by a matrix-like object of numeric or other mode.

  • matrix-like representation of experimental data including RNA sequencing and microarray experiments.
  • stores multiple experimental data matrices of identical dimensions, with associated metadata on:
    • the rows/genes/transcripts/other measurements (rowData)
    • column/sample phenotype or clinical data (colData)
    • overall experiment (metadata).
  • RangedSummarizedExperiment associates a GRanges or GRangesList vector with the rows

Note. Many other classes for experimental data are actually derived from SummarizedExperiment (e.g., SingleCellExperiment for single-cell RNA sequencing experiments)

library(SingleCellExperiment)
extends("SingleCellExperiment")
## [1] "SingleCellExperiment"       "RangedSummarizedExperiment"
## [3] "SummarizedExperiment"       "RectangularData"           
## [5] "Vector"                     "Annotated"                 
## [7] "vector_OR_Vector"
RaggedExperiment
  • flexible representation for segmented copy number, somatic mutations such as represented in .vcf files, and other ragged array schema for genomic location data.
  • similar to the GRangesList class in GenomicRanges
  • used to represent differing genomic ranges on each of a set of samples
showClass("RaggedExperiment")
## Class "RaggedExperiment" [package "RaggedExperiment"]
## 
## Slots:
##                                                       
## Name:       assays      rowidx      colidx    metadata
## Class: GRangesList     integer     integer        list
## 
## Extends: "Annotated"

RaggedExperiment provides a flexible set of _*Assay_ methods to support transformation of ranged list data to matrix format.

RaggedExperiment object schematic. Rows and columns represent genomic ranges and samples, respectively. Assay operations can be performed with (from left to right) compactAssay, qreduceAssay, and sparseAssay.

RaggedExperiment object schematic. Rows and columns represent genomic ranges and samples, respectively. Assay operations can be performed with (from left to right) compactAssay, qreduceAssay, and sparseAssay.

The Integrative Container

MultiAssayExperiment object schematic. colData provides data about the patients, cell lines, or other biological units, with one row per unit and one column per variable. The experiments are a list of assay datasets of arbitrary class. The sampleMap relates each column (observation) in ExperimentList to exactly one row (biological unit) in colData; however, one row of colData may map to zero, one, or more columns per assay, allowing for missing and replicate assays. sampleMap allows for per-assay sample naming conventions. Metadata can be used to store information in arbitrary format about the MultiAssayExperiment. Green stripes indicate a mapping of one subject to multiple observations across experiments.

MultiAssayExperiment object schematic. colData provides data about the patients, cell lines, or other biological units, with one row per unit and one column per variable. The experiments are a list of assay datasets of arbitrary class. The sampleMap relates each column (observation) in ExperimentList to exactly one row (biological unit) in colData; however, one row of colData may map to zero, one, or more columns per assay, allowing for missing and replicate assays. sampleMap allows for per-assay sample naming conventions. Metadata can be used to store information in arbitrary format about the MultiAssayExperiment. Green stripes indicate a mapping of one subject to multiple observations across experiments.

MultiAssayExperiment

  • coordinates multi-omics experiment data on a set of biological specimens
  • can contain any number of assays with different representations and dimensions
  • assays can be ID-based, where measurements are indexed identifiers of genes, microRNA, proteins, microbes, etc.
  • assays may be range-based, where measurements correspond to genomic ranges that can be represented as GRanges objects, such as gene expression or copy number.
Click on the fold to see what data classes are supported!
  1. matrix: the most basic class for ID-based datasets, could be used for example for gene expression summarized per-gene, microRNA, metabolomics, or microbiome data.
  2. SummarizedExperiment and derived methods: described above, could be used for miRNA, gene expression, proteomics, or any matrix-like data where measurements are represented by IDs.
  3. RangedSummarizedExperiment: described above, could be used for gene expression, methylation, or other data types referring to genomic positions.
  4. ExpressionSet: Another rich representation for ID-based datasets, supported only for legacy reasons
  5. RaggedExperiment: described above, for non-rectangular (ragged) ranged-based datasets such as segmented copy number, where segmentation of copy number alterations occurs and different genomic locations in each sample.
  6. RangedVcfStack: For VCF archives broken up by chromosome (see VcfStack class defined in the GenomicFiles package)
  7. DelayedMatrix: An on-disk representation of matrix-like objects for large datasets. It reduces memory usage and optimizes performance with delayed operations. This class is part of the DelayedArray package.
  8. SingleCellExperiment, SpatialExperiment, etc.

Note. Data classes that support row and column naming and subsetting may be used in a MultiAssayExperiment.

MatchedAssayExperiment

  • uniform subclass of MultiAssayExperiment
  • “all patients have a sample in each assay”
# coercion
as(x, "MatchedAssayExperiment")

# construction from MAE
MatchedAssayExperiment(mae)

Note. The MultiAssayExperiment package provides functionality to merge replicate profiles for a single patient (mergeReplicates()).

Key points

  • MultiAssayExperiment coordinates different Bioconductor classes into one unified object
  • MultiAssayExperiment is an infrastructure package while curatedTCGAData and cBioPortalData provide data on cancer studies including TCGA

Building from Scratch: MultiAssayExperiment

miniACC Demo

Get started by trying out MultiAssayExperiment using a subset of the TCGA adrenocortical carcinoma (ACC) dataset provided with the package. This dataset provides five assays on 92 patients, although all five assays were not performed for every patient:

  1. RNASeq2GeneNorm: gene mRNA abundance by RNA-seq
  2. gistict: GISTIC genomic copy number by gene
  3. RPPAArray: protein abundance by Reverse Phase Protein Array
  4. Mutations: non-silent somatic mutations by gene
  5. miRNASeqGene: microRNA abundance by microRNA-seq.
data("miniACC")
miniACC
## A MultiAssayExperiment object of 5 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 5:
##  [1] RNASeq2GeneNorm: SummarizedExperiment with 198 rows and 79 columns
##  [2] gistict: SummarizedExperiment with 198 rows and 90 columns
##  [3] RPPAArray: SummarizedExperiment with 33 rows and 46 columns
##  [4] Mutations: matrix with 97 rows and 90 columns
##  [5] miRNASeqGene: SummarizedExperiment with 471 rows and 80 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files

shiny Demo

Click Here to open the shiny tutorial.

Key points

  • Extractor functions allow users to take components from the MultiAssayExperiment object
  • They’re usually the same name as the component except for experiments which extracts the ExperimentList

Notes on Working with MultiAssayExperiment

API cheat sheet

The MultiAssayExperiment API for construction, access, subsetting, management, and reshaping to formats for application of R/Bioconductor graphics and analysis packages.

The MultiAssayExperiment API for construction, access, subsetting, management, and reshaping to formats for application of R/Bioconductor graphics and analysis packages.

MultiAssayExperiment construction and concatenation

constructor function

The MultiAssayExperiment constructor function accepts three arguments:

  1. experiments - An ExperimentList or list of rectangular data
  2. colData - A DataFrame describing the patients (or cell lines, or other biological units)
  3. sampleMap - A DataFrame of assay, primary, and colname identifiers

The miniACC object can be reconstructed as follows:

MultiAssayExperiment(
    experiments = experiments(miniACC),
    colData = colData(miniACC),
    sampleMap = sampleMap(miniACC),
    metadata = metadata(miniACC)
)
## A MultiAssayExperiment object of 5 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 5:
##  [1] RNASeq2GeneNorm: SummarizedExperiment with 198 rows and 79 columns
##  [2] gistict: SummarizedExperiment with 198 rows and 90 columns
##  [3] RPPAArray: SummarizedExperiment with 33 rows and 46 columns
##  [4] Mutations: matrix with 97 rows and 90 columns
##  [5] miRNASeqGene: SummarizedExperiment with 471 rows and 80 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files
Getting help with prepMultiAssay

The prepMultiAssay function allows the user to diagnose typical problems when creating a MultiAssayExperiment object. See ?prepMultiAssay for more details.

Combining MultiAssayExperiment object with the c function

The c function allows the user to concatenate an additional experiment to an existing MultiAssayExperiment. The optional sampleMap argument allows concatenating an assay whose column names do not match the row names of colData. For convenience, the mapFrom argument allows the user to map from a particular experiment provided that the order of the colnames is in the same. A warning will be issued to make the user aware of this assumption. For example, to concatenate a matrix of log2-transformed RNA-seq results:

miniACC2 <- c(
    miniACC,
    log2rnaseq = log2(assays(miniACC)$RNASeq2GeneNorm),
    mapFrom=1L
)
## Warning: Assuming column order in the data provided 
##  matches the order in 'mapFrom' experiment(s) colnames
experiments(miniACC2)
## ExperimentList class object of length 6:
##  [1] RNASeq2GeneNorm: SummarizedExperiment with 198 rows and 79 columns
##  [2] gistict: SummarizedExperiment with 198 rows and 90 columns
##  [3] RPPAArray: SummarizedExperiment with 33 rows and 46 columns
##  [4] Mutations: matrix with 97 rows and 90 columns
##  [5] miRNASeqGene: SummarizedExperiment with 471 rows and 80 columns
##  [6] log2rnaseq: matrix with 198 rows and 79 columns

colData - information biological units

This slot is a DataFrame describing the characteristics of biological units, for example clinical data for patients. In the prepared datasets from The Cancer Genome Atlas, each row is one patient and each column is a clinical, pathological, subtype, or other variable. The $ function provides a shortcut for accessing or setting colData columns.

colData(miniACC)[1:4, 1:4]
## DataFrame with 4 rows and 4 columns
##                 patientID years_to_birth vital_status days_to_death
##               <character>      <integer>    <integer>     <integer>
## TCGA-OR-A5J1 TCGA-OR-A5J1             58            1          1355
## TCGA-OR-A5J2 TCGA-OR-A5J2             44            1          1677
## TCGA-OR-A5J3 TCGA-OR-A5J3             23            0            NA
## TCGA-OR-A5J4 TCGA-OR-A5J4             23            1           423
table(miniACC$race)
## 
##                     asian black or african american                     white 
##                         2                         1                        78

Note. MultiAssayExperiment supports both missing observations and replicate observations, i.e., one row of colData can map to 0, 1, or more columns of any of the experimental data matrices. One could therefore treat replicate observations as one or multiple rows of colData. This can result in different subsetting, duplicated(), and wideFormat() behaviors.

Note. Multiple time points, or distinct biological replicates, can be separate rows of the colData.

Key points

  • Each row maps to zero or more observations in each experiment
  • Usually, organized as one row per biological unit

ExperimentList - experiment data

Experimental datasets can be input as either a base list or ExperimentList object for the set of samples collected. To see the experiments use the experiments getter function.

experiments(miniACC)
## ExperimentList class object of length 5:
##  [1] RNASeq2GeneNorm: SummarizedExperiment with 198 rows and 79 columns
##  [2] gistict: SummarizedExperiment with 198 rows and 90 columns
##  [3] RPPAArray: SummarizedExperiment with 33 rows and 46 columns
##  [4] Mutations: matrix with 97 rows and 90 columns
##  [5] miRNASeqGene: SummarizedExperiment with 471 rows and 80 columns

Note. Each matrix column must correspond to exactly one row in colData. In other words, each patient or cell line must be traceable. However, multiple columns can come from the same patient, or there can be no data for that patient.

Key points:

  • One rectangular dataset per list element
  • One column per assayed specimen.
  • Matrix rows correspond to variables, e.g. genes or genomic ranges
  • ExperimentList elements can be genomic range-based (e.g. SummarizedExperiment or RaggedExperiment) or ID-based data
  • Most rectangular-type data classes are supported

Note. Any data class can be included in the ExperimentList, as long as it supports: single-bracket subsetting ([), dimnames, and dim. Most data classes defined in Bioconductor meet these requirements.

sampleMap - relationship graph

sampleMap is a graph representation of the relationship between biological units and experimental results. In simple cases where the column names of ExperimentList data matrices match the row names of colData, the user won’t need to specify or think about a sample map, it can be created automatically by the MultiAssayExperiment constructor. sampleMap is a simple three-column DataFrame:

  1. assay column: the name of the assay, and found in the names of ExperimentList list names
  2. primary column: identifiers of patients or biological units, and found in the row names of colData
  3. colname column: identifiers of assay results, and found in the column names of ExperimentList elements Helper functions are available for creating a map from a list. See ?listToMap 
sampleMap(miniACC)
## DataFrame with 385 rows and 3 columns
##               assay      primary                colname
##            <factor>  <character>            <character>
## 1   RNASeq2GeneNorm TCGA-OR-A5J1 TCGA-OR-A5J1-01A-11R..
## 2   RNASeq2GeneNorm TCGA-OR-A5J2 TCGA-OR-A5J2-01A-11R..
## 3   RNASeq2GeneNorm TCGA-OR-A5J3 TCGA-OR-A5J3-01A-11R..
## 4   RNASeq2GeneNorm TCGA-OR-A5J5 TCGA-OR-A5J5-01A-11R..
## 5   RNASeq2GeneNorm TCGA-OR-A5J6 TCGA-OR-A5J6-01A-31R..
## ...             ...          ...                    ...
## 381    miRNASeqGene TCGA-PA-A5YG TCGA-PA-A5YG-01A-11R..
## 382    miRNASeqGene TCGA-PK-A5H8 TCGA-PK-A5H8-01A-11R..
## 383    miRNASeqGene TCGA-PK-A5H9 TCGA-PK-A5H9-01A-11R..
## 384    miRNASeqGene TCGA-PK-A5HA TCGA-PK-A5HA-01A-11R..
## 385    miRNASeqGene TCGA-PK-A5HB TCGA-PK-A5HB-01A-11R..

Key points:

  • relates experimental observations (colnames) to colData
  • permits experiment-specific sample naming, missing, and replicate observations

back to top

metadata

Metadata can be used to keep additional information about patients, assays performed on individuals or on the entire cohort, or features such as genes, proteins, and genomic ranges. There are many options available for storing metadata. First, MultiAssayExperiment has its own metadata for describing the entire experiment:

metadata(miniACC)
## $title
## [1] "Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma"
## 
## $PMID
## [1] "27165744"
## 
## $sourceURL
## [1] "http://s3.amazonaws.com/multiassayexperiments/accMAEO.rds"
## 
## $RPPAfeatureDataURL
## [1] "http://genomeportal.stanford.edu/pan-tcga/show_target_selection_file?filename=Allprotein.txt"
## 
## $colDataExtrasURL
## [1] "http://www.cell.com/cms/attachment/2062093088/2063584534/mmc3.xlsx"

Additionally, the DataFrame class used by sampleMap and colData, as well as the ExperimentList class, similarly support metadata. Finally, many experimental data objects that can be used in the ExperimentList support metadata. These provide flexible options to users and to developers of derived classes.

The Cancer Genome Atlas (TCGA) Data from curatedTCGAData

Most unrestricted TCGA data (from 33 cancer types) are available as MultiAssayExperiment objects from the curatedTCGAData package. This represents a lot of harmonization!

Here we list the available data for the Adrenocortical Carcinoma ("ACC") cancer type:

library(curatedTCGAData)
curatedTCGAData("ACC", version = "2.0.1", dry.run = TRUE)
##     ah_id                                 title file_size
## 1  EH4737                   ACC_CNASNP-20160128    0.8 Mb
## 2  EH4738                   ACC_CNVSNP-20160128    0.2 Mb
## 3  EH4740         ACC_GISTIC_AllByGene-20160128    0.2 Mb
## 4  EH4741             ACC_GISTIC_Peaks-20160128      0 Mb
## 5  EH4742 ACC_GISTIC_ThresholdedByGene-20160128    0.2 Mb
## 6  EH4744       ACC_Methylation-20160128_assays  239.2 Mb
## 7  EH4745           ACC_Methylation-20160128_se      6 Mb
## 8  EH4746             ACC_miRNASeqGene-20160128    0.1 Mb
## 9  EH4747                 ACC_Mutation-20160128    0.7 Mb
## 10 EH4748              ACC_RNASeq2Gene-20160128    2.7 Mb
## 11 EH4749          ACC_RNASeq2GeneNorm-20160128      4 Mb
## 12 EH4750                ACC_RPPAArray-20160128    0.1 Mb
##                    rdataclass rdatadateadded rdatadateremoved
## 1            RaggedExperiment     2021-01-27             <NA>
## 2            RaggedExperiment     2021-01-27             <NA>
## 3        SummarizedExperiment     2021-01-27             <NA>
## 4  RangedSummarizedExperiment     2021-01-27             <NA>
## 5        SummarizedExperiment     2021-01-27             <NA>
## 6        SummarizedExperiment     2021-01-27             <NA>
## 7            RaggedExperiment     2021-01-27             <NA>
## 8        SummarizedExperiment     2021-01-27             <NA>
## 9        SummarizedExperiment     2021-01-27             <NA>
## 10       SummarizedExperiment     2021-01-27             <NA>
## 11                     DFrame     2021-01-27             <NA>
## 12       SummarizedExperiment     2021-01-27             <NA>

We then download the data with dry.run set to FALSE.

acc <- curatedTCGAData(
    diseaseCode = "ACC",
    assays = c(
        "miRNASeqGene", "RPPAArray", "Mutation", "RNASeq2GeneNorm", "CNVSNP"
    ),
    version = "2.0.1",
    dry.run = FALSE
)
acc
## A MultiAssayExperiment object of 5 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 5:
##  [1] ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 180 columns
##  [2] ACC_miRNASeqGene-20160128: SummarizedExperiment with 1046 rows and 80 columns
##  [3] ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 columns
##  [4] ACC_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 79 columns
##  [5] ACC_RPPAArray-20160128: SummarizedExperiment with 192 rows and 46 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files

These objects contain most unrestricted TCGA assay and clinical / pathological data, as well as some curated data from the supplements of published TCGA primary papers at the end of the colData columns:

dim(colData(acc))
## [1]  92 822
tail(colnames(colData(acc)), 10)
##  [1] "MethyLevel"       "miRNA.cluster"    "SCNA.cluster"     "protein.cluster" 
##  [5] "COC"              "OncoSign"         "purity"           "ploidy"          
##  [9] "genome_doublings" "ADS"

cBioPortalData

The cBioPortal for Cancer Genomics provides access to hundreds of datasets collected and curated from different institutions. cBioPortalData is an R/Bioconductor client to download these datasets and integrate them using the MultiAssayExperiment data class. There is a more detailed workshop dedicated to cBioPortalData.

There are two main ways of accessing this data using the cBioPortalData package:

  1. cBioDataPack - tarball (.tar.gz) data files
  2. cBioPortalData - data from the API

Note. pkgdown reference website here: https://waldronlab.io/cBioPortalData/

Listing the studies available

First, we create an API object using the cBioPortal function. This will allow us to subsequently generate queries for the service.

cbio <- cBioPortal()

This function can also access local and private cBioPortal servers; see ?cBioPortal.

Setting the buildReport argument to the getStudies function adds two columns indicating whether the study can be accessed via API (for accessing data only for specific genes, gene panels, or molecular assays), and via data-pack (for accessing all available data).

getStudies(cbio, buildReport = TRUE)
## # A tibble: 494 × 15
##    name          description publicStudy pmid  citation groups status importDate
##    <chr>         <chr>       <lgl>       <chr> <chr>    <chr>   <int> <chr>     
##  1 Acute Lympho… Comprehens… TRUE        2573… Anderss… "PUBL…      0 2024-12-0…
##  2 Hypodiploid … Whole geno… TRUE        2333… Holmfel… ""          0 2024-12-0…
##  3 Adenoid Cyst… Targeted S… TRUE        2441… Ross et… "ACYC…      0 2024-12-0…
##  4 Adenoid Cyst… Whole-geno… TRUE        2686… Rettig … "ACYC…      0 2024-12-0…
##  5 Adenoid Cyst… WGS of 21 … TRUE        2663… Mitani … "ACYC…      0 2024-12-0…
##  6 Adenoid Cyst… Whole-geno… TRUE        2682… Drier e… "ACYC"      0 2024-12-0…
##  7 Adenoid Cyst… Whole exom… TRUE        2377… Stephen… "ACYC…      0 2024-12-0…
##  8 Acute Lympho… Whole-geno… TRUE        2777… Zhang e… "PUBL…      0 2024-12-0…
##  9 Appendiceal … Targeted s… TRUE        3649… Michael… "PUBL…      0 2024-12-0…
## 10 Bladder Canc… Whole exom… TRUE        2690… Al-Ahma… ""          0 2024-12-0…
## # ℹ 484 more rows
## # ℹ 7 more variables: allSampleCount <int>, readPermission <lgl>,
## #   studyId <chr>, cancerTypeId <chr>, referenceGenome <chr>, api_build <lgl>,
## #   pack_build <lgl>

This adds two additional columns to the end of the dataset reporting the status of the builds. Not all studies can be converted to MultiAssayExperiment. Some studies require additional cleaning to be represented with MultiAssayExperiment.

cBioDataPack

The cBioDataPack function downloads complete datasets and integrates them as a MultiAssayExperiment object. The names.field argument allows changing how GenomicRanges are labelled (HUGO, Entrez, Gene Names). There are other arguments but you’re unlikely to use them.

library(cBioPortalData)

(uvm <- cBioDataPack("uvm_tcga"))
## A MultiAssayExperiment object of 8 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 8:
##  [1] cna_hg19.seg: RaggedExperiment with 7618 rows and 80 columns
##  [2] cna: SummarizedExperiment with 24776 rows and 80 columns
##  [3] linear_cna: SummarizedExperiment with 24776 rows and 80 columns
##  [4] methylation_hm450: SummarizedExperiment with 15468 rows and 80 columns
##  [5] mrna_seq_v2_rsem_zscores_ref_all_samples: SummarizedExperiment with 20531 rows and 80 columns
##  [6] mrna_seq_v2_rsem_zscores_ref_diploid_samples: SummarizedExperiment with 20440 rows and 80 columns
##  [7] mrna_seq_v2_rsem: SummarizedExperiment with 20531 rows and 80 columns
##  [8] mutations: RaggedExperiment with 2174 rows and 80 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files

cBioPortalData

This function accesses data from the cBioPortal API. It requires specifying which of the available molecular profiles for a given study to fetch. For example, the urcc_mskcc_2016 study has molecular profiles:

(mp <- molecularProfiles(cbio, studyId = "urcc_mskcc_2016"))
## # A tibble: 2 × 8
##   molecularAlterationType datatype name       description showProfileInAnalysi…¹
##   <chr>                   <chr>    <chr>      <chr>       <lgl>                 
## 1 COPY_NUMBER_ALTERATION  DISCRETE Putative … Putative c… TRUE                  
## 2 MUTATION_EXTENDED       MAF      Mutations  Mutation s… TRUE                  
## # ℹ abbreviated name: ¹​showProfileInAnalysisTab
## # ℹ 3 more variables: patientLevel <lgl>, molecularProfileId <chr>,
## #   studyId <chr>
mp[["molecularProfileId"]]
## [1] "urcc_mskcc_2016_cna"       "urcc_mskcc_2016_mutations"

It also requires specifying a subset of genes, using either ‘genes’ with a vector of Entrez IDs, or ‘genePanelId’ with a gene panel ID:

## # A tibble: 66 × 2
##    description                                                       genePanelId
##    <chr>                                                             <chr>      
##  1 Targeted (27 cancer genes) sequencing of adenoid cystic carcinom… ACYC_FMI_27
##  2 Targeted panel of 232 genes.                                      Agilent    
##  3 Targeted panel of 8 genes.                                        AmpliSeq   
##  4 ARCHER-HEME gene panel (199 genes)                                ARCHER-HEM…
##  5 ARCHER-SOLID Gene Panel (62 genes)                                ARCHER-SOL…
##  6 Targeted sequencing of various tumor types via bait v3.           bait_v3    
##  7 Targeted sequencing of various tumor types via bait v4.           bait_v4    
##  8 Targeted sequencing of various tumor types via bait v5.           bait_v5    
##  9 Targeted panel of 387 cancer-related genes.                       bcc_unige_…
## 10 Research (CMO) IMPACT-Heme gene panel version 3.                  HemePACT_v3
## # ℹ 56 more rows
getGenePanel(cbio, genePanelId= "IMPACT230")
## # A tibble: 230 × 2
##    entrezGeneId hugoGeneSymbol
##           <int> <chr>         
##  1           25 ABL1          
##  2           27 ABL2          
##  3          207 AKT1          
##  4          208 AKT2          
##  5        10000 AKT3          
##  6          238 ALK           
##  7          242 ALOX12B       
##  8       139285 AMER1         
##  9          324 APC           
## 10          367 AR            
## # ℹ 220 more rows

The getGenePanel option only really makes sense for studies that employed targeted sequencing of a gene panel, or that employed whole-exome or whole-genome assays, if you are content with only the subset of genes in the panel. This argument will subset available genes to those in the panel, whether or not that panel was used in the study. If you want all available data, use the cBioDataPack function instead.

From the (Chen et al. 2016) publication of the [urcc_mskcc_2016][] study, we know that the study used the IMPACT230 gene panel, so we will specify that.

urcc <- cBioPortalData(
    api = cbio,
    studyId = "urcc_mskcc_2016",
    genePanelId = "IMPACT230",
    molecularProfileIds =
        c("urcc_mskcc_2016_cna", "urcc_mskcc_2016_mutations")
)
## harmonizing input:
##   removing 8 colData rownames not in sampleMap 'primary'
urcc
## A MultiAssayExperiment object of 2 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 2:
##  [1] urcc_mskcc_2016_mutations: RangedSummarizedExperiment with 156 rows and 53 columns
##  [2] urcc_mskcc_2016_cna: SummarizedExperiment with 31 rows and 8 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files

Key points

  • curatedTCGAData provides TCGA data with some curation including tumor subtype information
  • cBioPortalData has two main functions, one for downloading pre-packaged data and another for sending queries through the cBioPortal API

Utilities for TCGA

Aside from the available reshaping functions already included in the MultiAssayExperiment package, the TCGAutils package provides additional helper functions for working with TCGA data.

A number of helper functions are available for managing datasets from curatedTCGAData. These include:

  • Conversions of SummarizedExperiment to RangedSummarizedExperiment based on TxDb.Hsapiens.UCSC.hg19.knownGene for:
    • symbolsToRanges(): gene symbols
    • qreduceTCGA(): convert RaggedExperiment objects to RangedSummarizedExperiment with one row per gene symbol, for:
      • segmented copy number datasets (“CNVSNP” and “CNASNP”)
      • somatic mutation datasets (“Mutation”), with a value of 1 for any non-silent mutation and a value of 0 for no mutation or silent mutation

(1) Conversion of row metadata for curatedTCGAData objects

qreduceTCGA

The qreduceTCGA function converts RaggedExperiment mutation data objects to RangedSummarizedExperiment using org.Hs.eg.db and the qreduceTCGA utility function from RaggedExperiment to summarize ‘silent’ and ‘non-silent’ mutations based on a ‘Variant_Classification’ metadata column in the original object.

## Update build metadata to "hg19"
genome(acc[["ACC_Mutation-20160128"]]) <- "NCBI37"
seqlevelsStyle(acc[["ACC_Mutation-20160128"]]) <- "UCSC"

gnome <- genome(acc[["ACC_Mutation-20160128"]])
gnome <- translateBuild(gnome)
genome(acc[["ACC_Mutation-20160128"]]) <- gnome

qreduceTCGA(acc)
## 
##   403 genes were dropped because they have exons located on both strands of the
##   same reference sequence or on more than one reference sequence, so cannot be
##   represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a GRangesList
##   object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
## Warning in .normarg_seqlevelsStyle(value): more than one seqlevels style
## supplied, using the 1st one only
## Warning in .normarg_seqlevelsStyle(value): cannot switch some hg19's seqlevels
## from UCSC to NCBI style
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 270 sampleMap rows not in names(experiments)
## A MultiAssayExperiment object of 5 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 5:
##  [1] ACC_miRNASeqGene-20160128: SummarizedExperiment with 1046 rows and 80 columns
##  [2] ACC_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 79 columns
##  [3] ACC_RPPAArray-20160128: SummarizedExperiment with 192 rows and 46 columns
##  [4] ACC_Mutation-20160128_simplified: RangedSummarizedExperiment with 22912 rows and 90 columns
##  [5] ACC_CNVSNP-20160128_simplified: RangedSummarizedExperiment with 22912 rows and 180 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files
symbolsToRanges

In the cases where row annotations indicate gene symbols, the symbolsToRanges utility function converts genes to genomic ranges and replaces existing assays with RangedSummarizedExperiment objects. Gene annotations are given as ‘hg19’ genomic regions.

##   403 genes were dropped because they have exons located on both strands of the
##   same reference sequence or on more than one reference sequence, so cannot be
##   represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a GRangesList
##   object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 79 sampleMap rows not in names(experiments)
## A MultiAssayExperiment object of 6 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 6:
##  [1] ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 180 columns
##  [2] ACC_miRNASeqGene-20160128: SummarizedExperiment with 1046 rows and 80 columns
##  [3] ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 columns
##  [4] ACC_RPPAArray-20160128: SummarizedExperiment with 192 rows and 46 columns
##  [5] ACC_RNASeq2GeneNorm-20160128_ranged: RangedSummarizedExperiment with 16954 rows and 79 columns
##  [6] ACC_RNASeq2GeneNorm-20160128_unranged: SummarizedExperiment with 3547 rows and 79 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files
simplifyTCGA

The simplifyTCGA function combines all of the above operations to create a more managable MultiAssayExperiment object and using RangedSummarizedExperiment assays where possible.

TCGAutils::simplifyTCGA(acc)
##   403 genes were dropped because they have exons located on both strands of the
##   same reference sequence or on more than one reference sequence, so cannot be
##   represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a GRangesList
##   object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
## Warning in .normarg_seqlevelsStyle(value): more than one seqlevels style
## supplied, using the 1st one only
## Warning in .normarg_seqlevelsStyle(value): cannot switch some hg19's seqlevels
## from UCSC to NCBI style
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 270 sampleMap rows not in names(experiments)
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## harmonizing input:
##   removing 80 sampleMap rows not in names(experiments)
##   403 genes were dropped because they have exons located on both strands of the
##   same reference sequence or on more than one reference sequence, so cannot be
##   represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a GRangesList
##   object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
## harmonizing input:
##   removing 79 sampleMap rows not in names(experiments)
## A MultiAssayExperiment object of 7 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 7:
##  [1] ACC_RPPAArray-20160128: SummarizedExperiment with 192 rows and 46 columns
##  [2] ACC_Mutation-20160128_simplified: RangedSummarizedExperiment with 22912 rows and 90 columns
##  [3] ACC_CNVSNP-20160128_simplified: RangedSummarizedExperiment with 22912 rows and 180 columns
##  [4] ACC_miRNASeqGene-20160128_ranged: RangedSummarizedExperiment with 1002 rows and 80 columns
##  [5] ACC_miRNASeqGene-20160128_unranged: SummarizedExperiment with 44 rows and 80 columns
##  [6] ACC_RNASeq2GeneNorm-20160128_ranged: RangedSummarizedExperiment with 16954 rows and 79 columns
##  [7] ACC_RNASeq2GeneNorm-20160128_unranged: SummarizedExperiment with 3547 rows and 79 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files

(2) Identification and Separation of Samples

What types of samples are in the data?

Solution

The sampleTables function gives you an overview of samples in each assay:

## $`ACC_CNVSNP-20160128`
## 
## 01 10 11 
## 90 85  5 
## 
## $`ACC_miRNASeqGene-20160128`
## 
## 01 
## 80 
## 
## $`ACC_Mutation-20160128`
## 
## 01 
## 90 
## 
## $`ACC_RNASeq2GeneNorm-20160128`
## 
## 01 
## 79 
## 
## $`ACC_RPPAArray-20160128`
## 
## 01 
## 46

Interpretation of sample codes:

data("sampleTypes")
head(sampleTypes)
##   Code                                      Definition Short.Letter.Code
## 1   01                             Primary Solid Tumor                TP
## 2   02                           Recurrent Solid Tumor                TR
## 3   03 Primary Blood Derived Cancer - Peripheral Blood                TB
## 4   04    Recurrent Blood Derived Cancer - Bone Marrow              TRBM
## 5   05                        Additional - New Primary               TAP
## 6   06                                      Metastatic                TM

TCGAsplitAssays: separate the data from different tissue types

TCGA datasets include multiple -omics for solid tumors, adjacent normal tissues, blood-derived cancers and normals, and other tissue types, which may be mixed together in a single dataset. The MultiAssayExperiment object generated here has one patient per row of its colData, but each patient may have two or more -omics profiles by any assay, whether due to assaying of different types of tissues or to technical replication. TCGAsplitAssays separates profiles from different tissue types (such as tumor and adjacent normal) into different assays of the MultiAssayExperiment by taking a vector of sample codes, and partitioning the current assays into assays with an appended sample code:

split_acc <- TCGAsplitAssays(acc, c("01", "11"))
## Warning: Some 'sampleCodes' not found in assays
## Warning in .checkBarcodes(barcodes): Inconsistent barcode lengths: 28, 27

Only about 43 participants have data across all experiments.

Curated molecular subtypes

Is there subtype data available in the MultiAssayExperiment obtained from curatedTCGAData?

Solution

The getSubtypeMap function will show actual variable names found in colData that contain subtype information. This can only be obtained from MultiAssayExperiment objects provided by curatedTCGAData.

##          ACC_annotations     ACC_subtype
## 1             Patient_ID       patientID
## 2  histological_subtypes       Histology
## 3          mrna_subtypes         C1A/C1B
## 4          mrna_subtypes         mRNA_K4
## 5                   cimp      MethyLevel
## 6      microrna_subtypes   miRNA cluster
## 7          scna_subtypes    SCNA cluster
## 8       protein_subtypes protein cluster
## 9   integrative_subtypes             COC
## 10     mutation_subtypes        OncoSign
head(colData(acc)$Histology)
## [1] "Usual Type" "Usual Type" "Usual Type" "Usual Type" "Usual Type"
## [6] "Usual Type"

(3) Translation and Interpretation of TCGA identifiers

TCGAutils provides a number of ID translation functions. These allow the user to translate from either file or case UUIDs to TCGA barcodes and back. These functions work by querying the Genomic Data Commons API via the GenomicDataCommons package (thanks to Sean Davis). These include:

UUIDtoBarcode() - UUID to TCGA barcode

Here we have a known case UUID that we want to translate into a TCGA barcode.

UUIDtoBarcode("ae55b2d3-62a1-419e-9f9a-5ddfac356db4", from_type = "case_id")
##                                case_id submitter_id
## 1 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 TCGA-B0-5117

In cases where we want to translate a known file UUID to the associated TCGA patient barcode, we can use UUIDtoBarcode.

UUIDtoBarcode("b4bce3ff-7fdc-4849-880b-56f2b348ceac", from_type = "file_id")
##                                file_id associated_entities.entity_submitter_id
## 1 b4bce3ff-7fdc-4849-880b-56f2b348ceac            TCGA-B0-5094-11A-01D-1421-08
barcodeToUUID() - TCGA barcode to UUID

Here we translate the first two TCGA barcodes of the previous copy-number alterations dataset to UUID:

(xbarcode <- head(colnames(acc)[["ACC_CNVSNP-20160128"]], 4L))
## [1] "TCGA-OR-A5J1-01A-11D-A29H-01" "TCGA-OR-A5J1-10A-01D-A29K-01"
## [3] "TCGA-OR-A5J2-01A-11D-A29H-01" "TCGA-OR-A5J2-10A-01D-A29K-01"
barcodeToUUID(xbarcode)
##           submitter_aliquot_ids                          aliquot_ids
## 18 TCGA-OR-A5J1-01A-11D-A29H-01 1387b6c7-48fe-4961-86a7-0bdcbd3fef92
## 16 TCGA-OR-A5J1-10A-01D-A29K-01 cb537629-6a01-4d67-84ea-dbf130bd59c7
## 8  TCGA-OR-A5J2-01A-11D-A29H-01 6f0290b0-4cb4-4f72-853e-9ac363bd2c3b
## 11 TCGA-OR-A5J2-10A-01D-A29K-01 4bf2e4ac-399f-4a00-854b-8e23b561bb4d
UUIDtoUUID() - file and case IDs

We can also translate from file UUIDs to case UUIDs and vice versa as long as we know the input type. We can use the case UUID from the previous example to get the associated file UUIDs using UUIDtoUUID. Note that this translation is a one to many relationship, thus yielding a data.frame of file UUIDs for a single case UUID.

head(UUIDtoUUID("ae55b2d3-62a1-419e-9f9a-5ddfac356db4", to_type = "file_id"))
##                                case_id                        files.file_id
## 1 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 472cecc2-4d28-45c8-be44-d7b1a140c549
## 2 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 78f1f4b2-1ee9-4c8f-92a2-fe437eeba4c5
## 3 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 ba07ad3d-5e92-4091-a853-5f9e49327378
## 4 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 c0669db0-a20c-4836-aca7-0deb6ae8ba0c
## 5 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 0ed8caf1-6bf8-4cf4-bc60-ac5b833418c0
## 6 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 cb0f27bc-8403-414c-84a2-d08f26e3f464

One possible way to verify that file IDs are matching case UUIDS is to browse to the Genomic Data Commons webpage with the specific file UUID. Here we look at the first file UUID entry in the output data.frame:

https://portal.gdc.cancer.gov/files/0b4acc9e-3933-4d74-916a-a53c4a0665e6

In the page we check that the case UUID matches the input.

filenameToBarcode() - Using file names as input 
fquery <- files() |>
    GenomicDataCommons::filter(~ cases.project.project_id == "TCGA-ACC" &
        data_category == "Copy Number Variation" &
        data_type == "Copy Number Segment")

fnames <- head(results(fquery)$file_name)

filenameToBarcode(fnames)
##                                                                      file_name
## 1 GAMED_p_TCGA_B_312_313_314_NSP_GenomeWideSNP_6_E04_1361650.grch38.seg.v2.txt
## 2   6bea41f8-ad23-450e-b7ce-7c098db13640_wgs_gdc_realn.cr.igv.reheader.seg.txt
## 3 GAMED_p_TCGA_B_312_313_314_NSP_GenomeWideSNP_6_E05_1361692.grch38.seg.v2.txt
## 4 GAMED_p_TCGA_B_312_313_314_NSP_GenomeWideSNP_6_E06_1361784.grch38.seg.v2.txt
## 5   6101b5c5-c3d5-4f81-a721-dcdc5afdbffc_wgs_gdc_realn.cr.igv.reheader.seg.txt
## 6 GAMED_p_TCGA_B_312_313_314_NSP_GenomeWideSNP_6_D12_1361734.grch38.seg.v2.txt
##                                file_id
## 1 e88d7597-e2ea-41d3-a419-be70b5b49773
## 2 d2c5eef1-ea06-473b-b570-cce0486e4432
## 3 cf00f0a4-4a71-4b1b-a559-5ba7db28a184
## 4 5dfae645-caa6-4021-8f29-54e3cf043d33
## 5 73a58f81-388b-4723-8862-228b8db91020
## 6 63f7796d-85dc-4f02-85cb-983168afba66
##   samples.portions.analytes.aliquots.submitter_id
## 1                    TCGA-OR-A5KS-01A-11D-A309-01
## 2                    TCGA-OR-A5KS-01A-11D-A75V-36
## 3                    TCGA-OR-A5KS-10A-01D-A309-01
## 4                    TCGA-OR-A5L1-01A-11D-A309-01
## 5                    TCGA-OR-A5L1-10A-01D-A75V-36
## 6                    TCGA-OR-A5KP-10A-01D-A309-01
##   samples.portions.analytes.aliquots.submitter_id
## 1                    TCGA-OR-A5KS-01A-11D-A309-01
## 2                    TCGA-OR-A5KS-10A-01D-A75V-36
## 3                    TCGA-OR-A5KS-10A-01D-A309-01
## 4                    TCGA-OR-A5L1-01A-11D-A309-01
## 5                    TCGA-OR-A5L1-01A-11D-A75V-36
## 6                    TCGA-OR-A5KP-10A-01D-A309-01

See the TCGAutils vignette page for more details.

Key points

  • TCGAutils provides users additional tools for modifying row and column metadata
  • The package works mainly with TCGA data including barcode identifiers

Data Management with MultiAssayExperiment

Single bracket [ subsetting

In the pseudo code below, the subsetting operations work on the rows of the following indices:

  1. i experimental data rows
  2. j the primary names (vector input) or the column names (list or List inputs)
  3. k assay
multiassayexperiment[i = rownames, j = primary or colnames, k = assay]

Subsetting operations always return another MultiAssayExperiment. For example, the following will return any rows named “MAPK14” or “IGFBP2”, and remove any assays where no rows match:

miniACC[c("MAPK14", "IGFBP2"), , ]

The following will keep only patients of pathological stage IV, and all their associated assays:

stg4 <- miniACC$pathologic_stage == "stage iv"
# remove NA values from vector
miniACC[, stg4 & !is.na(stg4), ]

And the following will keep only the RNA-seq dataset, and only patients for which this assay is available:

miniACC[, , "RNASeq2GeneNorm"]
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 306 sampleMap rows not in names(experiments)
##   removing 13 colData rownames not in sampleMap 'primary'

Subsetting by genomic ranges

If any ExperimentList objects have features represented by genomic ranges (e.g. RangedSummarizedExperiment, RaggedExperiment), then a GRanges object in the first subsetting position will subset these objects as in GenomicRanges::findOverlaps(). Any non-ranged ExperimentList element will be subset to zero rows.

Double bracket [[ subsetting

The “double bracket” method ([[) is a convenience function for extracting a single element of the MultiAssayExperiment ExperimentList. It avoids the use of experiments(mae)[[1L]]. For example, both of the following extract the ExpressionSet object containing RNA-seq data:

miniACC[[1L]]
## class: SummarizedExperiment 
## dim: 198 79 
## metadata(3): experimentData annotation protocolData
## assays(1): exprs
## rownames(198): DIRAS3 MAPK14 ... SQSTM1 KCNJ13
## rowData names(0):
## colnames(79): TCGA-OR-A5J1-01A-11R-A29S-07 TCGA-OR-A5J2-01A-11R-A29S-07
##   ... TCGA-PK-A5HA-01A-11R-A29S-07 TCGA-PK-A5HB-01A-11R-A29S-07
## colData names(0):
## equivalently
miniACC[["RNASeq2GeneNorm"]]
## class: SummarizedExperiment 
## dim: 198 79 
## metadata(3): experimentData annotation protocolData
## assays(1): exprs
## rownames(198): DIRAS3 MAPK14 ... SQSTM1 KCNJ13
## rowData names(0):
## colnames(79): TCGA-OR-A5J1-01A-11R-A29S-07 TCGA-OR-A5J2-01A-11R-A29S-07
##   ... TCGA-PK-A5HA-01A-11R-A29S-07 TCGA-PK-A5HB-01A-11R-A29S-07
## colData names(0):

To preserve the colData during the extraction, see ?getWithColData.

Complete cases

complete.cases() shows which patients have complete data for all assays:

##    Mode   FALSE    TRUE 
## logical      49      43

The above logical vector could be used for patient subsetting. More simply, intersectColumns() will select complete cases and rearrange each ExperimentList element so its columns correspond exactly to rows of colData in the same order:

accmatched <- intersectColumns(miniACC)

Note, the column names of the assays in accmatched are not the same because of assay-specific identifiers, but they have been automatically re-arranged to correspond to the same patients. In these TCGA assays, the first three - delimited positions correspond to patient, ie the first patient is TCGA-OR-A5J2:

colnames(accmatched)
## CharacterList of length 5
## [["RNASeq2GeneNorm"]] TCGA-OR-A5J2-01A-11R-A29S-07 ...
## [["gistict"]] TCGA-OR-A5J2-01A-11D-A29H-01 ... TCGA-PK-A5HA-01A-11D-A29H-01
## [["RPPAArray"]] TCGA-OR-A5J2-01A-21-A39K-20 ... TCGA-PK-A5HA-01A-21-A39K-20
## [["Mutations"]] TCGA-OR-A5J2-01A-11D-A29I-10 ... TCGA-PK-A5HA-01A-11D-A29I-10
## [["miRNASeqGene"]] TCGA-OR-A5J2-01A-11R-A29W-13 ...

Row names that are common across assays

intersectRows() keeps only rows that are common to each assay, and aligns them in identical order. For example, to keep only genes where data are available for RNA-seq, GISTIC copy number, and somatic mutations:

accmatched2 <- intersectRows(miniACC[, ,
    c("RNASeq2GeneNorm", "gistict", "Mutations")])
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 126 sampleMap rows not in names(experiments)
rownames(accmatched2)
## CharacterList of length 3
## [["RNASeq2GeneNorm"]] DIRAS3 G6PD KDR ERBB3 AKT1S1 ... RET CDKN2A MACC1 CHGA
## [["gistict"]] DIRAS3 G6PD KDR ERBB3 AKT1S1 ... PREX1 RET CDKN2A MACC1 CHGA
## [["Mutations"]] DIRAS3 G6PD KDR ERBB3 AKT1S1 ... PREX1 RET CDKN2A MACC1 CHGA

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Extraction

assay and assays

The assay and assays methods follow SummarizedExperiment convention. The assay (singular) method will extract the first element of the ExperimentList and will return a matrix.

class(assay(miniACC))
## [1] "matrix" "array"

The assays (plural) method will return a SimpleList of the data with each element being a matrix.

assays(miniACC)
## List of length 5
## names(5): RNASeq2GeneNorm gistict RPPAArray Mutations miRNASeqGene

Key point:

  • Whereas the [[ returned an experiment in its original class, assay() and assays() convert the assay data to matrix format.

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Summary of slots and accessors

Slot in the MultiAssayExperiment can be accessed or set using the respective accessor functions:

Slot Accessor
ExperimentList experiments()
colData colData() and $ *
sampleMap sampleMap()
metadata metadata()

__*__ The $ operator on a MultiAssayExperiment returns a single column of the colData.

Transformation / reshaping

The longForm or wideFormat functions will “reshape” and combine experiments with each other and with colData into one DataFrame. These functions provide compatibility with most of the common R/Bioconductor functions for regression, machine learning, and visualization.

Note. It is best to remove technical replicates before performing these operations to minimize the number of NA values, esp. to wideFormat.

longForm

In long format a single column provides all assay results, with additional optional colData columns whose values are repeated as necessary. Here assay is the name of the ExperimentList element, primary is the patient identifier (rowname of colData), rowname is the assay rowname (in this case genes), colname is the assay-specific identifier (column name), value is the numeric measurement (gene expression, copy number, presence of a non-silent mutation, etc), and following these are the vital_status and days_to_death colData columns that have been added:

longForm(miniACC[c("TP53", "CTNNB1"), , ],
    colDataCols = c("vital_status", "days_to_death"))
## harmonizing input:
##   removing 126 sampleMap rows not in names(experiments)
## DataFrame with 518 rows and 7 columns
##               assay      primary     rowname                      colname
##         <character>  <character> <character>                     <factor>
## 1   RNASeq2GeneNorm TCGA-OR-A5J1        TP53 TCGA-OR-A5J1-01A-11R-A29S-07
## 2   RNASeq2GeneNorm TCGA-OR-A5J1      CTNNB1 TCGA-OR-A5J1-01A-11R-A29S-07
## 3   RNASeq2GeneNorm TCGA-OR-A5J2        TP53 TCGA-OR-A5J2-01A-11R-A29S-07
## 4   RNASeq2GeneNorm TCGA-OR-A5J2      CTNNB1 TCGA-OR-A5J2-01A-11R-A29S-07
## 5   RNASeq2GeneNorm TCGA-OR-A5J3        TP53 TCGA-OR-A5J3-01A-11R-A29S-07
## ...             ...          ...         ...                          ...
## 514       Mutations TCGA-PK-A5HA      CTNNB1 TCGA-PK-A5HA-01A-11D-A29I-10
## 515       Mutations TCGA-PK-A5HB        TP53 TCGA-PK-A5HB-01A-11D-A29I-10
## 516       Mutations TCGA-PK-A5HB      CTNNB1 TCGA-PK-A5HB-01A-11D-A29I-10
## 517       Mutations TCGA-PK-A5HC        TP53 TCGA-PK-A5HC-01A-11D-A30A-10
## 518       Mutations TCGA-PK-A5HC      CTNNB1 TCGA-PK-A5HC-01A-11D-A30A-10
##         value vital_status days_to_death
##     <numeric>    <integer>     <integer>
## 1     563.401            1          1355
## 2    5634.467            1          1355
## 3     165.481            1          1677
## 4   62658.391            1          1677
## 5     956.303            0            NA
## ...       ...          ...           ...
## 514         0            0            NA
## 515         0            0            NA
## 516         0            0            NA
## 517         0            0            NA
## 518         0            0            NA
wideFormat

In wide format, each feature from each assay goes in a separate column, with one row per primary identifier (patient). Here, each variable becomes a new column:

wideFormat(miniACC[c("TP53", "CTNNB1"), , ],
    colDataCols = c("vital_status", "days_to_death"))
## harmonizing input:
##   removing 126 sampleMap rows not in names(experiments)
## DataFrame with 92 rows and 9 columns
##          primary vital_status days_to_death gistict_TP53 gistict_CTNNB1
##      <character>    <integer>     <integer>    <numeric>      <numeric>
## 1   TCGA-OR-A5J1            1          1355            0              0
## 2   TCGA-OR-A5J2            1          1677            0              1
## 3   TCGA-OR-A5J3            0            NA            0              0
## 4   TCGA-OR-A5J4            1           423            1              0
## 5   TCGA-OR-A5J5            1           365            0              0
## ...          ...          ...           ...          ...            ...
## 88  TCGA-PK-A5H9            0            NA            0              0
## 89  TCGA-PK-A5HA            0            NA           -1              0
## 90  TCGA-PK-A5HB            0            NA           -1             -1
## 91  TCGA-PK-A5HC            0            NA            1              1
## 92  TCGA-P6-A5OG            1           383           -1              1
##     Mutations_TP53 Mutations_CTNNB1 RNASeq2GeneNorm_TP53 RNASeq2GeneNorm_CTNNB1
##          <numeric>        <numeric>            <numeric>              <numeric>
## 1                0                0              563.401                5634.47
## 2                1                1              165.481               62658.39
## 3                0                0              956.303                6337.43
## 4                0                0                   NA                     NA
## 5                0                0             1169.636                5979.06
## ...            ...              ...                  ...                    ...
## 88               0                0              890.866                5258.99
## 89               0                0              683.572                8120.17
## 90               0                0              237.370                5257.81
## 91               0                0                   NA                     NA
## 92              NA               NA              815.345                6390.10

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Key points

  • Knowing how to subset a MultiAssayExperiment object is important to be able to restrict observations and measurements to particular phenotypes or sample types
  • Functions such as longForm and wideFormat are helpful for downstream analysis functions that require a certain type of input format

SingleCellMultiModal

SingleCellMultiModal is an ExperimentHub package that serves multiple datasets obtained from GEO and other sources and represents them as MultiAssayExperiment objects. We provide several multi-modal datasets including scNMT, 10X Multiome, seqFISH, CITEseq, SCoPE2, and others. The scope of the package is is to provide data for benchmarking and analysis.

Users can access the data for a particular technology with the appropriate function. For example, to obtain a small data.frame of what data is available for scNMT, the user would enter:

scNMT("mouse_gastrulation", dry.run = TRUE, version = '2.0.0')
##     ah_id         mode file_size rdataclass rdatadateadded rdatadateremoved
## 1  EH3753      acc_cgi   21.1 Mb     matrix     2020-09-03             <NA>
## 2  EH3754     acc_CTCF    1.2 Mb     matrix     2020-09-03             <NA>
## 3  EH3755      acc_DHS   16.2 Mb     matrix     2020-09-03             <NA>
## 4  EH3756 acc_genebody   60.1 Mb     matrix     2020-09-03             <NA>
## 5  EH3757     acc_p300    0.2 Mb     matrix     2020-09-03             <NA>
## 6  EH3758 acc_promoter   33.8 Mb     matrix     2020-09-03             <NA>
## 7  EH3760      met_cgi   12.1 Mb     matrix     2020-09-03             <NA>
## 8  EH3761     met_CTCF    0.1 Mb     matrix     2020-09-03             <NA>
## 9  EH3762      met_DHS    3.9 Mb     matrix     2020-09-03             <NA>
## 10 EH3763 met_genebody   33.9 Mb     matrix     2020-09-03             <NA>
## 11 EH3764     met_p300    0.1 Mb     matrix     2020-09-03             <NA>
## 12 EH3765 met_promoter   18.7 Mb     matrix     2020-09-03             <NA>
## 13 EH3766          rna   43.5 Mb     matrix     2020-09-03             <NA>

We can see that there are a few assays for each modality.

See the SingleCellMultiModal tutorial above for more information.

References

Chen, Ying-Bei, Jianing Xu, Anders Jacobsen Skanderup, Yiyu Dong, A Rose Brannon, Lu Wang, Helen H Won, et al. 2016. “Molecular Analysis of Aggressive Renal Cell Carcinoma with Unclassified Histology Reveals Distinct Subsets.” Nat. Commun. 7 (October): 13131. https://doi.org/10.1038/ncomms13131.
Ramos, Marcel, Ludwig Geistlinger, Sehyun Oh, Lucas Schiffer, Rimsha Azhar, Hanish Kodali, Ino de Bruijn, et al. 2020. “Multiomic Integration of Public Oncology Databases in Bioconductor.” JCO Clin Cancer Inform 4 (October): 958–71. https://doi.org/10.1200/CCI.19.00119.
Ramos, Marcel, Martin Morgan, Ludwig Geistlinger, Vincent J Carey, and Levi Waldron. 2023. “RaggedExperiment: The Missing Link Between Genomic Ranges and Matrices in Bioconductor.” Bioinformatics, May. https://doi.org/10.1093/bioinformatics/btad330.
Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez, Tiffany Chan, et al. 2017. “Software for the Integration of Multiomics Experiments in Bioconductor.” Cancer Res. 77 (21): e39–42. https://doi.org/10.1158/0008-5472.CAN-17-0344.