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

Workshop participation

See the instruction Google slides
Go to the conference instance registration webpage and use these credentials:
- username: rstudio
- password: welcome-to-bioc2020
Browse to the workshop website
(Alternatively) Docker users can run this workshop locally via:

docker run -e PASSWORD=bioc -p 8787:8787 mr148/multiassayworkshop:latest

Requirements: R/Bioconductor packages

The workshop uses a Docker container with Bioconductor devel version 3.12. 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:

library(MultiAssayExperiment)
library(curatedTCGAData)
library(cBioPortalData)
library(TCGAutils)
library(GenomicDataCommons)
library(UpSetR)

Citing MultiAssayExperiment

Please use this citation (Ramos et al. 2017) when using MultiAssayExperiment, your citations are appreciated!

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

`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)

`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)

`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:

conversion / summarization of row annotations to genomic ranges
identification and separation of samples
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

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.

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 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.

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`

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!

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.
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.
RangedSummarizedExperiment: described above, could be used for gene expression, methylation, or other data types referring to genomic positions.
ExpressionSet: Another rich representation for ID-based datasets, supported only for legacy reasons
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.
RangedVcfStack: For VCF archives broken up by chromosome (see VcfStack class defined in the GenomicFiles package)
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.

Note. many data classes that support row and column naming and subsetting can 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 then 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:

RNASeq2GeneNorm: gene mRNA abundance by RNA-seq
gistict: GISTIC genomic copy number by gene
RPPAArray: protein abundance by Reverse Phase Protein Array
Mutations: non-silent somatic mutations by gene
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 all data to 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.

MultiAssayExperiment construction and concatenation

constructor function

The MultiAssayExperiment constructor function can take three arguments:

experiments - An ExperimentList or list of rectangular data
colData - A DataFrame describing the patients (or cell lines, or other biological units)
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 all data to files

`prepMultiAssay` - Constructor function helper

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

`c` - concatenate to MultiAssayExperiment

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

Key points about the colData:

Each row maps to zero or more observations in each experiment in the ExperimentList, below.
One row per biological unit
- MultiAssayExperiment supports both missing observations and replicate observations, ie one row of colData can map to 0, 1, or more columns of any of the experimental data matrices.
- therefore you could treat replicate observations as one or multiple rows of colData, and this will result in different behaviors of functions you will learn later like subsetting, duplicated(), and wideFormat().
- multiple time points, or distinct biological replicates, should probably be separate rows of the colData.

ExperimentList - experiment data

A base list or ExperimentList object containing the experimental datasets for the set of samples collected. This gets converted into a class ExperimentList during construction.

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

Key points:

One matrix-like dataset per list element (although they do not even need to be matrix-like, see for example the RaggedExperiment package)
One matrix column per assayed specimen. Each matrix column must correspond to exactly one row of colData: in other words, you must know which patient or cell line the observation came from. However, multiple columns can come from the same patient, or there can be no data for that patient.
Matrix rows correspond to variables, e.g. genes or genomic ranges
ExperimentList elements can be genomic range-based (e.g. SummarizedExperiment::RangedSummarizedExperiment-class or RaggedExperiment::RaggedExperiment-class) or ID-based data (e.g. SummarizedExperiment::SummarizedExperiment-class, Biobase::eSet-class base::matrix-class, DelayedArray::DelayedArray-class, and derived classes)
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:

assay column: the name of the assay, and found in the names of ExperimentList list names
primary column: identifiers of patients or biological units, and found in the row names of colData
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

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 are available as MultiAssayExperiment objects from the curatedTCGAData package. This represents a lot of harmonization!

library(curatedTCGAData)
curatedTCGAData("ACC", version = "1.1.38", dry.run = TRUE)

##     ah_id                                 title file_size
## 1   EH558                   ACC_CNASNP-20160128    0.8 Mb
## 2   EH559                   ACC_CNVSNP-20160128    0.2 Mb
## 3   EH561         ACC_GISTIC_AllByGene-20160128    0.3 Mb
## 4  EH2115             ACC_GISTIC_Peaks-20160128      0 Mb
## 5   EH562 ACC_GISTIC_ThresholdedByGene-20160128    0.2 Mb
## 6  EH2116       ACC_Methylation-20160128_assays  236.4 Mb
## 7  EH2117           ACC_Methylation-20160128_se    6.1 Mb
## 8   EH565             ACC_miRNASeqGene-20160128    0.1 Mb
## 9   EH566                 ACC_Mutation-20160128    0.7 Mb
## 10  EH567          ACC_RNASeq2GeneNorm-20160128      4 Mb
## 11  EH568                ACC_RPPAArray-20160128    0.1 Mb
##                    rdataclass rdatadateadded rdatadateremoved
## 1            RaggedExperiment     2017-10-10             <NA>
## 2            RaggedExperiment     2017-10-10             <NA>
## 3        SummarizedExperiment     2017-10-10             <NA>
## 4  RangedSummarizedExperiment     2019-01-09             <NA>
## 5        SummarizedExperiment     2017-10-10             <NA>
## 6        SummarizedExperiment     2019-01-09             <NA>
## 7            RaggedExperiment     2019-01-09             <NA>
## 8        SummarizedExperiment     2017-10-10             <NA>
## 9            RaggedExperiment     2017-10-10             <NA>
## 10       SummarizedExperiment     2017-10-10             <NA>
## 11       SummarizedExperiment     2017-10-10             <NA>

acc <- curatedTCGAData(
    diseaseCode = "ACC",
    assays = c(
        "miRNASeqGene", "RPPAArray", "Mutation", "RNASeq2GeneNorm", "CNVSNP"
    ),
    version = "1.1.38",
    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 all data to files

These objects contain most unrestricted TCGA assay and clinical / pathological data, as well as material curated 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"

The TCGAutils package provides additional helper functions.

`cBioPortalData`

The cBio Genomics Portal provides access to more than 260 datasets collected and curated from different instutions.

There are two main ways of accessing this data:

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

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

cBioDataPack

library(cBioPortalData)
data("studiesTable")
head(studiesTable[["cancer_study_id"]])

## [1] "paac_jhu_2014"       "mel_tsam_liang_2017" "all_stjude_2015"    
## [4] "all_stjude_2016"     "aml_ohsu_2018"       "laml_tcga"

(uvm <- cBioDataPack("uvm_tcga"))

## A MultiAssayExperiment object of 9 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 9:
##  [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 15191 rows and 80 columns
##  [5] mutations_extended: RaggedExperiment with 2174 rows and 80 columns
##  [6] mutations_mskcc: RaggedExperiment with 2174 rows and 80 columns
##  [7] RNA_Seq_v2_expression_median: SummarizedExperiment with 20531 rows and 80 columns
##  [8] RNA_Seq_v2_mRNA_median_all_sample_Zscores: SummarizedExperiment with 20531 rows and 80 columns
##  [9] RNA_Seq_v2_mRNA_median_Zscores: SummarizedExperiment with 20440 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 all data to files

cBioPortalData

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

cbio <- cBioPortal()
getStudies(cbio)

## # A tibble: 303 x 13
##    name     shortName  description     publicStudy pmid  citation  groups status
##    <chr>    <chr>      <chr>           <lgl>       <chr> <chr>     <chr>   <int>
##  1 Oral Sq… Head & ne… Comprehensive … TRUE        2361… Pickerin… ""          0
##  2 Hepatoc… HCC (Inse… Whole-exome se… TRUE        2582… Schulze … "PUBL…      0
##  3 Uveal M… UM (QIMR)  Whole-genome o… TRUE        2668… Johansso… "PUBL…      0
##  4 Neurobl… NBL (AMC)  Whole genome s… TRUE        2236… Molenaar… "PUBL…      0
##  5 Nasopha… NPC (Sing… Whole exome se… TRUE        2495… Lin et a… "PUBL…      0
##  6 Neurobl… NBL (Colo… Whole-genome s… TRUE        2646… Peifer e… ""          0
##  7 Myelody… MDS (Toky… Whole exome se… TRUE        2190… Yoshida … ""          0
##  8 Insulin… Panet (Sh… Whole exome se… TRUE        2432… Cao et a… ""          0
##  9 Pleural… PLMESO (N… Whole-exome se… TRUE        2548… Guo et a… ""          0
## 10 Pilocyt… PAST (Nat… Whole-genome s… TRUE        2381… Jones et… "PUBL…      0
## # … with 293 more rows, and 5 more variables: importDate <chr>,
## #   allSampleCount <int>, studyId <chr>, cancerTypeId <chr>,
## #   referenceGenome <chr>

(
    urcc <- cBioPortalData(
        cbio, studyId = "urcc_mskcc_2016", genePanelId = "IMPACT341"
    )
)

## 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_cna: SummarizedExperiment with 213 rows and 62 columns
##  [2] urcc_mskcc_2016_mutations: RangedSummarizedExperiment with 147 rows and 53 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 all data to 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:
- mirToRanges(): microRNA
- 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

`mirToRanges`

microRNA assays obtained from curatedTCGAData have annotated sequences that can be converted to genomic ranges using the mirbase.db package. The function looks up all sequences and converts them to (‘hg19’) ranges. For those rows that cannot be found, an ‘unranged’ assay is introduced in the resulting MultiAssayExperiment object.

mirToRanges(acc)

## Warning: 'experiments' dropped; see 'metadata'

## harmonizing input:
##   removing 80 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_Mutation-20160128: RaggedExperiment with 20166 rows and 90 columns
##  [3] ACC_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 79 columns
##  [4] ACC_RPPAArray-20160128: SummarizedExperiment with 192 rows and 46 columns
##  [5] ACC_miRNASeqGene-20160128_ranged: RangedSummarizedExperiment with 1002 rows and 80 columns
##  [6] ACC_miRNASeqGene-20160128_unranged: SummarizedExperiment with 44 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 all data to files

`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.

## '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: 'experiments' dropped; see 'metadata'

## 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 22918 rows and 90 columns
##  [5] ACC_CNVSNP-20160128_simplified: RangedSummarizedExperiment with 22918 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 all data to 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.

symbolsToRanges(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.

## 'select()' returned 1:1 mapping between keys and columns

## Warning: 'experiments' dropped; see 'metadata'

## 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 17208 rows and 79 columns
##  [6] ACC_RNASeq2GeneNorm-20160128_unranged: SummarizedExperiment with 3293 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 all data to 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.

## '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: 'experiments' dropped; see 'metadata'

## harmonizing input:
##   removing 270 sampleMap rows not in names(experiments)

## Warning: 'experiments' dropped; see 'metadata'

## 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.

## 'select()' returned 1:1 mapping between keys and columns

## Warning: 'experiments' dropped; see 'metadata'

## 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 22918 rows and 90 columns
##  [3] ACC_CNVSNP-20160128_simplified: RangedSummarizedExperiment with 22918 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 17208 rows and 79 columns
##  [7] ACC_RNASeq2GeneNorm-20160128_unranged: SummarizedExperiment with 3293 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 all data to 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:

sampleTables(acc)

## $`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

`splitAssays`: 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. splitAssays 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 <- splitAssays(acc, c("01", "11"))

## Warning: 'splitAssays' is deprecated.
## Use 'TCGAsplitAssays' instead.
## See help("Deprecated")

## 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.

getSubtypeMap(acc)

##          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("0001801b-54b0-4551-8d7a-d66fb59429bf", from_type = "file_id")

##                                file_id associated_entities.entity_submitter_id
## 1 0001801b-54b0-4551-8d7a-d66fb59429bf            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
## 16 TCGA-OR-A5J1-01A-11D-A29H-01 1387b6c7-48fe-4961-86a7-0bdcbd3fef92
## 23 TCGA-OR-A5J1-10A-01D-A29K-01 cb537629-6a01-4d67-84ea-dbf130bd59c7
## 13 TCGA-OR-A5J2-01A-11D-A29H-01 6f0290b0-4cb4-4f72-853e-9ac363bd2c3b
## 2  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 48c342b0-e7a2-4a7b-8556-55bcd8ad9ea0
## 2 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 db8ba5d3-76be-4a67-a575-803ba483b6f9
## 3 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 f580489b-55ea-43c5-9489-b54c13146992
## 4 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 bf72ffef-d8c4-423d-9c5a-7bb5c23b2f31
## 5 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 b36f4e88-89ca-40bf-b543-d0e3c08ad342
## 6 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 4c3a899f-be0f-454f-b5dc-e30e29314c49

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 BLAIN_p_TCGA_282_304_b2_N_GenomeWideSNP_6_E11_1348556.grch38.seg.v2.txt
## 2 AQUAE_p_TCGA_112_304_b2_N_GenomeWideSNP_6_E01_1348422.grch38.seg.v2.txt
## 3 AQUAE_p_TCGA_112_304_b2_N_GenomeWideSNP_6_A10_1348266.grch38.seg.v2.txt
## 4 BLAIN_p_TCGA_282_304_b2_N_GenomeWideSNP_6_C11_1348522.grch38.seg.v2.txt
## 5 BLAIN_p_TCGA_282_304_b2_N_GenomeWideSNP_6_C03_1348442.grch38.seg.v2.txt
## 6 AQUAE_p_TCGA_112_304_b2_N_GenomeWideSNP_6_F07_1348352.grch38.seg.v2.txt
##                                file_id        aliquots.submitter_id
## 1 8f1630cb-f6f9-44df-84bd-d4fca168aa70 TCGA-OR-A5K4-01A-11D-A29H-01
## 2 eec6400b-611d-4f3d-8406-87ae81d4de70 TCGA-OR-A5L9-01A-11D-A29H-01
## 3 6738b6fb-16bc-448e-9a28-c306ebe98615 TCGA-OR-A5LC-10A-01D-A29K-01
## 4 0c0f5938-bb3c-424a-8d25-f80ee281e4ed TCGA-OR-A5JT-10A-01D-A29K-01
## 5 b517c681-5a71-4e17-b12c-0dd2cc41e4ca TCGA-OR-A5JC-10A-01D-A29K-01
## 6 2b059b73-ef28-407a-9efb-1b5ba5c37f66 TCGA-OR-A5LM-01A-11D-A29H-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

MultiAssayExperiment Subsetting

Single bracket `[`

In pseudo code below, the subsetting operations work on the rows of the following indices: 1. i experimental data rows 2. j the primary names or the column names (entered as a list or List) 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:

miniACC[, miniACC$pathologic_stage == "stage iv", ]

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 'metadata'

## 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 `[[`

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):

Complete cases

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

summary(complete.cases(miniACC))

##    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 'metadata'

## 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

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 assay as its original class, assay() and assays() convert the assay data to matrix form.

Summary of slots and accessors

Slot in the MultiAssayExperiment can be accessed or set using their 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 longFormat 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.

`longFormat`

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:

longFormat(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     value
##         <character>  <character> <character>            <character> <numeric>
## 1   RNASeq2GeneNorm TCGA-OR-A5J1        TP53 TCGA-OR-A5J1-01A-11R..   563.401
## 2   RNASeq2GeneNorm TCGA-OR-A5J1      CTNNB1 TCGA-OR-A5J1-01A-11R..  5634.467
## 3   RNASeq2GeneNorm TCGA-OR-A5J2        TP53 TCGA-OR-A5J2-01A-11R..   165.481
## 4   RNASeq2GeneNorm TCGA-OR-A5J2      CTNNB1 TCGA-OR-A5J2-01A-11R.. 62658.391
## 5   RNASeq2GeneNorm TCGA-OR-A5J3        TP53 TCGA-OR-A5J3-01A-11R..   956.303
## ...             ...          ...         ...                    ...       ...
## 514       Mutations TCGA-PK-A5HA      CTNNB1 TCGA-PK-A5HA-01A-11D..         0
## 515       Mutations TCGA-PK-A5HB        TP53 TCGA-PK-A5HB-01A-11D..         0
## 516       Mutations TCGA-PK-A5HB      CTNNB1 TCGA-PK-A5HB-01A-11D..         0
## 517       Mutations TCGA-PK-A5HC        TP53 TCGA-PK-A5HC-01A-11D..         0
## 518       Mutations TCGA-PK-A5HC      CTNNB1 TCGA-PK-A5HC-01A-11D..         0
##     vital_status days_to_death
##        <integer>     <integer>
## 1              1          1355
## 2              1          1355
## 3              1          1677
## 4              1          1677
## 5              0            NA
## ...          ...           ...
## 514            0            NA
## 515            0            NA
## 516            0            NA
## 517            0            NA
## 518            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 RNASeq2GeneNorm_TP53
##      <character>    <integer>     <integer>            <numeric>
## 1   TCGA-OR-A5J1            1          1355              563.401
## 2   TCGA-OR-A5J2            1          1677              165.481
## 3   TCGA-OR-A5J3            0            NA              956.303
## 4   TCGA-OR-A5J4            1           423                   NA
## 5   TCGA-OR-A5J5            1           365             1169.636
## ...          ...          ...           ...                  ...
## 88  TCGA-PK-A5H9            0            NA              890.866
## 89  TCGA-PK-A5HA            0            NA              683.572
## 90  TCGA-PK-A5HB            0            NA              237.370
## 91  TCGA-PK-A5HC            0            NA                   NA
## 92  TCGA-P6-A5OG            1           383              815.345
##     RNASeq2GeneNorm_CTNNB1 gistict_TP53 gistict_CTNNB1 Mutations_TP53
##                  <numeric>    <numeric>      <numeric>      <numeric>
## 1                  5634.47            0              0              0
## 2                 62658.39            0              1              1
## 3                  6337.43            0              0              0
## 4                       NA            1              0              0
## 5                  5979.06            0              0              0
## ...                    ...          ...            ...            ...
## 88                 5258.99            0              0              0
## 89                 8120.17           -1              0              0
## 90                 5257.81           -1             -1              0
## 91                      NA            1              1              0
## 92                 6390.10           -1              1             NA
##     Mutations_CTNNB1
##            <numeric>
## 1                  0
## 2                  1
## 3                  0
## 4                  0
## 5                  0
## ...              ...
## 88                 0
## 89                 0
## 90                 0
## 91                 0
## 92                NA

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 longFormat and wideFormat are helpful for downstream analysis functions that require a certain type of input format

Bonus: Visualization of Sample Sets

Here we use the built-in visualization functionality to what extent the samples in each assay overlap:

upsetSamples(miniACC)

References

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.

MultiAssayExperiment: Integrative workflow for public multi-omics data

Marcel Ramos

Ludwig Geistlinger

Levi Waldron

March 10, 2021

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

Workshop participation

Requirements: R/Bioconductor packages

Citing MultiAssayExperiment

Key Packages

MultiAssayExperiment

cBioPortalData

curatedTCGAData

TCGAutils

Data Classes

(Ranged)SummarizedExperiment

RaggedExperiment

The Integrative Container

MultiAssayExperiment

MatchedAssayExperiment

Building from Scratch: MultiAssayExperiment

miniACC Demo

shiny Demo

Notes on Working with MultiAssayExperiment

API cheat sheet

MultiAssayExperiment construction and concatenation

constructor function

prepMultiAssay - Constructor function helper

c - concatenate to MultiAssayExperiment

colData - information biological units

ExperimentList - experiment data

sampleMap - relationship graph

metadata

The Cancer Genome Atlas (TCGA) Data from curatedTCGAData

cBioPortalData

cBioDataPack

cBioPortalData

Utilities for TCGA

(1) Conversion of row metadata for curatedTCGAData objects

mirToRanges

qreduceTCGA

symbolsToRanges

simplifyTCGA

(2) Identification and Separation of Samples

What types of samples are in the data?

splitAssays: separate the data from different tissue types

Curated molecular subtypes

(3) Translation and Interpretation of TCGA identifiers

UUIDtoBarcode() - UUID to TCGA barcode

barcodeToUUID() - TCGA barcode to UUID

UUIDtoUUID() - file and case IDs

filenameToBarcode() - Using file names as input

MultiAssayExperiment Subsetting

Single bracket [

Subsetting by genomic ranges

Double bracket [[

Complete cases

Row names that are common across assays

Extraction

assay and assays

Summary of slots and accessors

Transformation / reshaping

longFormat

wideFormat

Bonus: Visualization of Sample Sets

References

`MultiAssayExperiment`

`cBioPortalData`

`curatedTCGAData`

`TCGAutils`

`(Ranged)SummarizedExperiment`

`RaggedExperiment`

`MultiAssayExperiment`

`MatchedAssayExperiment`

Building from Scratch: `MultiAssayExperiment`

`miniACC` Demo

`shiny` Demo

`prepMultiAssay` - Constructor function helper

`c` - concatenate to MultiAssayExperiment

The Cancer Genome Atlas (TCGA) Data from `curatedTCGAData`

`cBioPortalData`

(1) Conversion of row metadata for `curatedTCGAData` objects

`mirToRanges`

`qreduceTCGA`

`symbolsToRanges`

`simplifyTCGA`

`splitAssays`: separate the data from different tissue types

`UUIDtoBarcode()` - UUID to TCGA barcode

`barcodeToUUID()` - TCGA barcode to UUID

`UUIDtoUUID()` - file and case IDs

`filenameToBarcode()` - Using file names as input

Single bracket `[`

Double bracket `[[`

`longFormat`

`wideFormat`