curatedTCGAData

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("curatedTCGAData")

Load packages:

library(curatedTCGAData)
library(MultiAssayExperiment)
library(TCGAutils)

Citing curatedTCGAData

Your citations are important to the project and help us secure funding. Please refer to the References section to see the Ramos et al. (2020) and Ramos et al. (2017) citations. For the BibTeX entries, run the citation function (after installation):

citation("curatedTCGAData")
citation("MultiAssayExperiment")

curatedTCGAData uses MultiAssayExperiment to coordinate and represent the data. Please cite the MultiAssayExperiment Cancer Research publication. You can see the PDF of our public data publication on JCO Clinical Cancer Informatics.

Data versions

curatedTCGAData now has a version 2.0.1 set of data with a number of improvements and bug fixes. To access the previous data release, please use version 1.1.38. This can be added to the curatedTCGAData function as:

head(
    curatedTCGAData(
        diseaseCode = "COAD", assays = "*", version = "1.1.38"
    )
)
## See '?curatedTCGAData' for 'diseaseCode' and 'assays' inputs
## Querying EH with: COAD_CNASeq-20160128
## Querying EH with: COAD_CNASNP-20160128
## Querying EH with: COAD_CNVSNP-20160128
## Querying EH with: COAD_GISTIC_AllByGene-20160128
## Querying EH with: COAD_GISTIC_Peaks-20160128
## Querying EH with: COAD_GISTIC_ThresholdedByGene-20160128
## Querying EH with: COAD_Methylation_methyl27-20160128_assays
## Querying EH with: COAD_Methylation_methyl27-20160128_se
## Querying EH with: COAD_Methylation_methyl450-20160128_assays
## Querying EH with: COAD_Methylation_methyl450-20160128_se
## Querying EH with: COAD_miRNASeqGene-20160128
## Querying EH with: COAD_mRNAArray-20160128
## Querying EH with: COAD_Mutation-20160128
## Querying EH with: COAD_RNASeq2GeneNorm-20160128
## Querying EH with: COAD_RNASeqGene-20160128
## Querying EH with: COAD_RPPAArray-20160128
##    ah_id                                  title file_size
## 1  EH625                   COAD_CNASeq-20160128    0.3 Mb
## 2  EH626                   COAD_CNASNP-20160128    3.9 Mb
## 3  EH627                   COAD_CNVSNP-20160128    0.9 Mb
## 4  EH629         COAD_GISTIC_AllByGene-20160128    0.5 Mb
## 5 EH2132             COAD_GISTIC_Peaks-20160128      0 Mb
## 6  EH630 COAD_GISTIC_ThresholdedByGene-20160128    0.3 Mb
##                   rdataclass rdatadateadded rdatadateremoved
## 1           RaggedExperiment     2017-10-10             <NA>
## 2           RaggedExperiment     2017-10-10             <NA>
## 3           RaggedExperiment     2017-10-10             <NA>
## 4       SummarizedExperiment     2017-10-10             <NA>
## 5 RangedSummarizedExperiment     2019-01-09             <NA>
## 6       SummarizedExperiment     2017-10-10             <NA>

Version 2.0.1

Here is a list of changes to the data provided in version 2.0.1

  • provides RNASeq2Gene assays with RSEM gene expression values
  • Genomic information is present in RaggedExperiment objects as GRCh37 rather than 37
  • Assays coming from the same platform are now merged and provided as one (e.g., in OV and GBM)
  • mRNAArray data now returns matrix data instead of DataFrame

Data source

These data were processed by Broad Firehose pipelines and accessed using RTCGAToolbox. For details on the preprocessing methods used, see the Broad GDAC documentation.

Downloading datasets

To get a neat table of cancer data available in curatedTCGAData, see the diseaseCodes dataset from TCGAutils. Availability is indicated by the Available column in the dataset.

data('diseaseCodes', package = "TCGAutils")
head(diseaseCodes)
##   Study.Abbreviation Available SubtypeData
## 1                ACC       Yes         Yes
## 2               BLCA       Yes         Yes
## 3               BRCA       Yes         Yes
## 4               CESC       Yes          No
## 5               CHOL       Yes          No
## 6               CNTL        No          No
##                                                         Study.Name
## 1                                         Adrenocortical carcinoma
## 2                                     Bladder Urothelial Carcinoma
## 3                                        Breast invasive carcinoma
## 4 Cervical squamous cell carcinoma and endocervical adenocarcinoma
## 5                                               Cholangiocarcinoma
## 6                                                         Controls

Alternatively, you can get the full table of data available using wildcards '*' in the diseaseCode argument of the main function:

curatedTCGAData(
    diseaseCode = "*", assays = "*", version = "2.0.1"
)

To see what assays are available for a particular TCGA disease code, leave the assays argument as a wildcard ('*'):

head(
    curatedTCGAData(
        diseaseCode = "COAD", assays = "*", version = "2.0.1"
    )
)
## See '?curatedTCGAData' for 'diseaseCode' and 'assays' inputs
## Querying EH with: COAD_CNASeq-20160128
## Querying EH with: COAD_CNASNP-20160128
## Querying EH with: COAD_CNVSNP-20160128
## Querying EH with: COAD_GISTIC_AllByGene-20160128
## Querying EH with: COAD_GISTIC_Peaks-20160128
## Querying EH with: COAD_GISTIC_ThresholdedByGene-20160128
## Querying EH with: COAD_Methylation_methyl27-20160128_assays
## Querying EH with: COAD_Methylation_methyl27-20160128_se
## Querying EH with: COAD_Methylation_methyl450-20160128_assays
## Querying EH with: COAD_Methylation_methyl450-20160128_se
## Querying EH with: COAD_miRNASeqGene-20160128
## Querying EH with: COAD_mRNAArray-20160128
## Querying EH with: COAD_Mutation-20160128
## Querying EH with: COAD_RNASeq2Gene-20160128
## Querying EH with: COAD_RNASeq2GeneNorm_illuminaga-20160128
## Querying EH with: COAD_RNASeq2GeneNorm_illuminahiseq-20160128
## Querying EH with: COAD_RNASeqGene-20160128
## Querying EH with: COAD_RPPAArray-20160128
##    ah_id                                  title file_size
## 1 EH4820                   COAD_CNASeq-20160128    0.3 Mb
## 2 EH4821                   COAD_CNASNP-20160128    3.9 Mb
## 3 EH4822                   COAD_CNVSNP-20160128    0.9 Mb
## 4 EH4824         COAD_GISTIC_AllByGene-20160128    0.4 Mb
## 5 EH4825             COAD_GISTIC_Peaks-20160128      0 Mb
## 6 EH4826 COAD_GISTIC_ThresholdedByGene-20160128    0.2 Mb
##                   rdataclass rdatadateadded rdatadateremoved
## 1           RaggedExperiment     2021-01-27             <NA>
## 2           RaggedExperiment     2021-01-27             <NA>
## 3           RaggedExperiment     2021-01-27             <NA>
## 4       SummarizedExperiment     2021-01-27             <NA>
## 5 RangedSummarizedExperiment     2021-01-27             <NA>
## 6       SummarizedExperiment     2021-01-27             <NA>

Caveats for working with TCGA data

Not all TCGA samples are cancer, there are a mix of samples in each of the 33 cancer types. Use sampleTables on the MultiAssayExperiment object along with data(sampleTypes, package = "TCGAutils") to see what samples are present in the data. There may be tumors that were used to create multiple contributions leading to technical replicates. These should be resolved using the appropriate helper functions such as mergeReplicates. Primary tumors should be selected using TCGAutils::TCGAsampleSelect and used as input to the subsetting mechanisms. See the “Samples in Assays” section of this vignette.

ACC dataset example

(accmae <- curatedTCGAData(
    "ACC", c("CN*", "Mutation"), version = "2.0.1", dry.run = FALSE
))
## A MultiAssayExperiment object of 3 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 3:
##  [1] ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 180 columns
##  [2] ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 180 columns
##  [3] ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 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

Note. For more on how to use a MultiAssayExperiment please see the MultiAssayExperiment vignette.

Subtype information

Some cancer datasets contain associated subtype information within the clinical datasets provided. This subtype information is included in the metadata of colData of the MultiAssayExperiment object. To obtain these variable names, use the getSubtypeMap function from TCGA utils:

head(getSubtypeMap(accmae))
##         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

Typical clinical variables

Another helper function provided by TCGAutils allows users to obtain a set of consistent clinical variable names across several cancer types. Use the getClinicalNames function to obtain a character vector of common clinical variables such as vital status, years to birth, days to death, etc.

head(getClinicalNames("ACC"))
## [1] "years_to_birth"        "vital_status"          "days_to_death"        
## [4] "days_to_last_followup" "tumor_tissue_site"     "pathologic_stage"
colData(accmae)[, getClinicalNames("ACC")][1:5, 1:5]
## DataFrame with 5 rows and 5 columns
##              years_to_birth vital_status days_to_death days_to_last_followup
##                   <integer>    <integer>     <integer>             <integer>
## TCGA-OR-A5J1             58            1          1355                    NA
## TCGA-OR-A5J2             44            1          1677                    NA
## TCGA-OR-A5J3             23            0            NA                  2091
## TCGA-OR-A5J4             23            1           423                    NA
## TCGA-OR-A5J5             30            1           365                    NA
##              tumor_tissue_site
##                    <character>
## TCGA-OR-A5J1           adrenal
## TCGA-OR-A5J2           adrenal
## TCGA-OR-A5J3           adrenal
## TCGA-OR-A5J4           adrenal
## TCGA-OR-A5J5           adrenal

Identifying samples in Assays

The sampleTables function gives an overview of sample types / codes present in the data:

sampleTables(accmae)
## $`ACC_CNASNP-20160128`
## 
## 01 10 11 
## 90 85  5 
## 
## $`ACC_CNVSNP-20160128`
## 
## 01 10 11 
## 90 85  5 
## 
## $`ACC_Mutation-20160128`
## 
## 01 
## 90

You can use the reference dataset (sampleTypes) from the TCGAutils package to interpret the TCGA sample codes above. The dataset provides clinically meaningful descriptions:

data(sampleTypes, package = "TCGAutils")
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

Separating samples

Often, an analysis is performed comparing two groups of samples to each other. To facilitate the separation of samples, the TCGAsplitAssays function from TCGAutils identifies all sample types in the assays and moves each into its own assay. By default, all discoverable sample types are separated into a separate experiment. In this case we requested only solid tumors and blood derived normal samples as seen in the sampleTypes reference dataset:

sampleTypes[sampleTypes[["Code"]] %in% c("01", "10"), ]
##    Code           Definition Short.Letter.Code
## 1    01  Primary Solid Tumor                TP
## 10   10 Blood Derived Normal                NB
TCGAsplitAssays(accmae, c("01", "10"))
## Warning: Some 'sampleCodes' not found in assays
## A MultiAssayExperiment object of 5 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 5:
##  [1] 01_ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 90 columns
##  [2] 10_ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 85 columns
##  [3] 01_ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 90 columns
##  [4] 10_ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 85 columns
##  [5] 01_ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 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

To obtain a logical vector that could be used for subsetting a MultiAsssayExperiment, refer to TCGAsampleSelect. To select only primary tumors, use the function on the colnames of the MultiAssayExperiment:

tums <- TCGAsampleSelect(colnames(accmae), "01")

TCGAprimaryTumors convenience

If interested in only the primary tumor samples, TCGAutils provides a convenient operation to extract primary tumors from the MultiAssayExperiment representation. The TCGAprimaryTumors function will return only samples with primary tumor (either solid tissue or blood) samples using the above operations in the background:

(primaryTumors <- TCGAprimaryTumors(accmae))
## harmonizing input:
##   removing 180 sampleMap rows with 'colname' not in colnames of experiments
## A MultiAssayExperiment object of 3 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 3:
##  [1] ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 90 columns
##  [2] ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 90 columns
##  [3] ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 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

To view the results, run sampleTables again on the output:

sampleTables(primaryTumors)
## $`ACC_CNASNP-20160128`
## 
## 01 
## 90 
## 
## $`ACC_CNVSNP-20160128`
## 
## 01 
## 90 
## 
## $`ACC_Mutation-20160128`
## 
## 01 
## 90

Keeping colData in an extracted Assay

When extracting a single assay from the MultiAssayExperiment the user can conveniently choose to keep the colData from the MultiAssayExperiment in the extracted assay given that the class of the extracted assay supports colData storage and operations. SummarizedExperiment and its derived data representations support this operation. In this example, we extract the mutation data as represented by a RaggedExperiment which was designed to have colData functionality. The default replacement method is to ‘append’ the MultiAssayExperiment colData to the RaggedExperiment assay. The mode argument can also completely replace the colData when set to ‘replace’.

(accmut <- getWithColData(accmae, "ACC_Mutation-20160128", mode = "append"))
## Warning: 'experiments' dropped; see 'drops()'
## class: RaggedExperiment 
## dim: 20166 90 
## assays(47): Hugo_Symbol Entrez_Gene_Id ... Trna_alt1 Trna_alt2
## rownames: NULL
## colnames(90): TCGA-OR-A5J1-01A-11D-A29I-10 TCGA-OR-A5J2-01A-11D-A29I-10
##   ... TCGA-PK-A5HB-01A-11D-A29I-10 TCGA-PK-A5HC-01A-11D-A30A-10
## colData names(822): patientID years_to_birth ... genome_doublings ADS
head(colData(accmut)[, 1:4])
## DataFrame with 6 rows and 4 columns
##                                 patientID years_to_birth vital_status
##                               <character>      <integer>    <integer>
## TCGA-OR-A5J1-01A-11D-A29I-10 TCGA-OR-A5J1             58            1
## TCGA-OR-A5J2-01A-11D-A29I-10 TCGA-OR-A5J2             44            1
## TCGA-OR-A5J3-01A-11D-A29I-10 TCGA-OR-A5J3             23            0
## TCGA-OR-A5J4-01A-11D-A29I-10 TCGA-OR-A5J4             23            1
## TCGA-OR-A5J5-01A-11D-A29I-10 TCGA-OR-A5J5             30            1
## TCGA-OR-A5J6-01A-31D-A29I-10 TCGA-OR-A5J6             29            0
##                              days_to_death
##                                  <integer>
## TCGA-OR-A5J1-01A-11D-A29I-10          1355
## TCGA-OR-A5J2-01A-11D-A29I-10          1677
## TCGA-OR-A5J3-01A-11D-A29I-10            NA
## TCGA-OR-A5J4-01A-11D-A29I-10           423
## TCGA-OR-A5J5-01A-11D-A29I-10           365
## TCGA-OR-A5J6-01A-31D-A29I-10            NA

Example use of RaggedExperiment

The RaggedExperiment representation provides a matrix view of a GRangesList internal representation. Typical use of a RaggedExperiment involves a number of functions to reshape ‘ragged’ measurements into a matrix-like format. These include sparseAssay, compactAssay, disjoinAssay, and qreduceAssay. See the RaggedExperiment vignette for details. In this example, we convert entrez gene identifiers to numeric in order to show how we can create a sparse matrix representation of any numeric metadata column in the RaggedExperiment.

ragex <- accmae[["ACC_Mutation-20160128"]]
## convert score to numeric
mcols(ragex)$Entrez_Gene_Id <- as.numeric(mcols(ragex)[["Entrez_Gene_Id"]])
sparseAssay(ragex, i = "Entrez_Gene_Id", sparse=TRUE)[1:6, 1:3]
## 6 x 3 sparse Matrix of class "dgCMatrix"
##                         TCGA-OR-A5J1-01A-11D-A29I-10
## 1:11561526:+                                   57540
## 1:12309384:+                                   55187
## 1:33820015:+                                    1912
## 1:152785074-152785097:+                       353132
## 1:152800122:+                                 353131
## 1:152800131:+                                 353131
##                         TCGA-OR-A5J2-01A-11D-A29I-10
## 1:11561526:+                                       .
## 1:12309384:+                                       .
## 1:33820015:+                                       .
## 1:152785074-152785097:+                            .
## 1:152800122:+                                      .
## 1:152800131:+                                      .
##                         TCGA-OR-A5J3-01A-11D-A29I-10
## 1:11561526:+                                       .
## 1:12309384:+                                       .
## 1:33820015:+                                       .
## 1:152785074-152785097:+                            .
## 1:152800122:+                                      .
## 1:152800131:+                                      .

Users who would like to use the internal GRangesList representation can invoke the coercion method:

as(ragex, "GRangesList")
## GRangesList object of length 90:
## $`TCGA-OR-A5J1-01A-11D-A29I-10`
## GRanges object with 66 ranges and 47 metadata columns:
##        seqnames              ranges strand | Hugo_Symbol Entrez_Gene_Id
##           <Rle>           <IRanges>  <Rle> | <character>      <numeric>
##    [1]        1            11561526      + |      PTCHD2          57540
##    [2]        1            12309384      + |      VPS13D          55187
##    [3]        1            33820015      + |        PHC2           1912
##    [4]        1 152785074-152785097      + |       LCE1B         353132
##    [5]        1           152800122      + |       LCE1A         353131
##    ...      ...                 ...    ... .         ...            ...
##   [62]        9           132395087      + |       NTMT1          28989
##   [63]        9           139272547      + |      SNAPC4           6621
##   [64]        X           101409174      + |        BEX5         340542
##   [65]        X           154157135      + |          F8           2157
##   [66]       17   80274159-80274160      + |         CD7            924
##                        Center  NCBI_Build Variant_Classification Variant_Type
##                   <character> <character>            <character>  <character>
##    [1] hgsc.bcm.edu;broad.m..          37                 Silent          SNP
##    [2] hgsc.bcm.edu;broad.m..          37                 Silent          SNP
##    [3] hgsc.bcm.edu;broad.m..          37                 Silent          SNP
##    [4] hgsc.bcm.edu;broad.m..          37           In_Frame_Del          DEL
##    [5]           hgsc.bcm.edu          37                 Silent          SNP
##    ...                    ...         ...                    ...          ...
##   [62] hgsc.bcm.edu;broad.m..          37                 Silent          SNP
##   [63] broad.mit.edu;bcgsc.ca          37                 Silent          SNP
##   [64] hgsc.bcm.edu;broad.m..          37      Missense_Mutation          SNP
##   [65] hgsc.bcm.edu;broad.m..          37      Missense_Mutation          SNP
##   [66]          broad.mit.edu          37        Frame_Shift_Ins          INS
##              Reference_Allele      Tumor_Seq_Allele1 Tumor_Seq_Allele2
##                   <character>            <character>       <character>
##    [1]                      G                      G                 A
##    [2]                      T                      T                 G
##    [3]                      C                      C                 T
##    [4] GCTGTGGCTCCAGCTCTGGG.. GCTGTGGCTCCAGCTCTGGG..                 -
##    [5]                      C                      C                 T
##    ...                    ...                    ...               ...
##   [62]                      C                      C                 T
##   [63]                      C                      C                 T
##   [64]                      C                      C                 T
##   [65]                      T                      T                 C
##   [66]                      -                      -                 T
##           dbSNP_RS dbSNP_Val_Status Matched_Norm_Sample_Barcode
##        <character>      <character>                 <character>
##    [1]       novel                       TCGA-OR-A5J1-10A-01D..
##    [2]       novel                       TCGA-OR-A5J1-10A-01D..
##    [3]       novel                       TCGA-OR-A5J1-10A-01D..
##    [4]       novel                       TCGA-OR-A5J1-10A-01D..
##    [5] rs148143373      byfrequency      TCGA-OR-A5J1-10A-01D..
##    ...         ...              ...                         ...
##   [62]       novel                       TCGA-OR-A5J1-10A-01D..
##   [63]       novel                       TCGA-OR-A5J1-10A-01D..
##   [64]       novel                       TCGA-OR-A5J1-10A-01D..
##   [65]       novel                       TCGA-OR-A5J1-10A-01D..
##   [66]       novel                       TCGA-OR-A5J1-10A-01D..
##        Match_Norm_Seq_Allele1 Match_Norm_Seq_Allele2 Tumor_Validation_Allele1
##                   <character>            <character>              <character>
##    [1]                      G                      G                        -
##    [2]                      T                      T                        -
##    [3]                      C                      C                        -
##    [4] GCTGTGGCTCCAGCTCTGGG.. GCTGTGGCTCCAGCTCTGGG..                        -
##    [5]                      C                      C                        -
##    ...                    ...                    ...                      ...
##   [62]                      C                      C                        -
##   [63]                                                                      -
##   [64]                      C                      C                        -
##   [65]                      T                      T                        -
##   [66]                                                                      -
##        Tumor_Validation_Allele2 Match_Norm_Validation_Allele1
##                     <character>                   <character>
##    [1]                        -                             -
##    [2]                        -                             -
##    [3]                        -                             -
##    [4]                        -                             -
##    [5]                        -                             -
##    ...                      ...                           ...
##   [62]                        -                             -
##   [63]                        -                             -
##   [64]                        -                             -
##   [65]                        -                             -
##   [66]                        -                             -
##        Match_Norm_Validation_Allele2 Verification_Status Validation_Status
##                          <character>         <character>       <character>
##    [1]                             -             Unknown          Untested
##    [2]                             -             Unknown          Untested
##    [3]                             -             Unknown          Untested
##    [4]                             -             Unknown          Untested
##    [5]                             -             Unknown          Untested
##    ...                           ...                 ...               ...
##   [62]                             -             Unknown          Untested
##   [63]                             -             Unknown          Untested
##   [64]                             -             Unknown          Untested
##   [65]                             -             Unknown          Untested
##   [66]                             -             Unknown          Untested
##        Mutation_Status Sequencing_Phase Sequence_Source Validation_Method
##            <character>      <character>     <character>       <character>
##    [1]         Somatic          Phase_I             WXS              none
##    [2]         Somatic          Phase_I             WXS              none
##    [3]         Somatic          Phase_I             WXS              none
##    [4]         Somatic          Phase_I             WXS              none
##    [5]         Somatic          Phase_I             WXS              none
##    ...             ...              ...             ...               ...
##   [62]         Somatic          Phase_I             WXS              none
##   [63]         Somatic          Phase_I             WXS              none
##   [64]         Somatic          Phase_I             WXS              none
##   [65]         Somatic          Phase_I             WXS              none
##   [66]         Somatic          Phase_I             WXS              none
##              Score    BAM_File      Sequencer      Tumor_Sample_UUID
##        <character> <character>    <character>            <character>
##    [1]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##    [2]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##    [3]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##    [4]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##    [5]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##    ...         ...         ...            ...                    ...
##   [62]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##   [63]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##   [64]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##   [65]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##   [66]           .           . Illumina GAIIx 352062e7-9b06-41cd-8..
##        Matched_Norm_Sample_UUID COSMIC_Codon COSMIC_Gene Transcript_Id
##                     <character>  <character> <character>   <character>
##    [1]   1d288ab9-ab2d-4483-a..            .  PTCHD2-209     NM_020780
##    [2]   1d288ab9-ab2d-4483-a..            .   VPS13D-95     NM_015378
##    [3]   1d288ab9-ab2d-4483-a..            .    PHC2-227     NM_198040
##    [4]   1d288ab9-ab2d-4483-a..            .    LCE1B-68     NM_178349
##    [5]   1d288ab9-ab2d-4483-a..            .    LCE1A-70     NM_178348
##    ...                      ...          ...         ...           ...
##   [62]   1d288ab9-ab2d-4483-a..            .           .     NM_014064
##   [63]   1d288ab9-ab2d-4483-a..            .   SNAPC4-90     NM_003086
##   [64]   1d288ab9-ab2d-4483-a..            .     BEX5-86  NM_001012978
##   [65]   1d288ab9-ab2d-4483-a..            .      F8-182     NM_000132
##   [66]   1d288ab9-ab2d-4483-a..            .      CD7-90     NM_006137
##               Exon   ChromChange    AAChange Genome_Plus_Minus_10_Bp
##        <character>   <character> <character>             <character>
##    [1]       exon2       c.G477A     p.L159L           GCAGCTGCATCTC
##    [2]       exon6       c.T552G     p.A184A           AAATGCTGTGAAT
##    [3]       exon8      c.G1542A     p.P514P           TGGGGACGGCTGG
##    [4]       exon1  c.152_175del  p.51_59del           GAGGCTGCTGTGG
##    [5]       exon1       c.C174T      p.G58G           TGGGGGCGGCTGC
##    ...         ...           ...         ...                     ...
##   [62]       exon2       c.C105T      p.G35G           GTATGGCCACATC
##   [63]      exon21      c.G3732A    p.Q1244Q           CCCAGGCTGGCGC
##   [64]       exon3        c.G64A      p.A22T           CTAAAGCGGGGGC
##   [65]      exon14      c.A4930G    p.T1644A           CCCAGGTGACTTC
##   [66]       exon3 c.524_525insA    p.A175fs           GAGGCTGCTGGCG
##        Drug_Target     TTotCov     TVarCov     NTotCov     NVarCov
##        <character> <character> <character> <character> <character>
##    [1]           .          22           8          38           0
##    [2]           .         127          48          65           0
##    [3]           .         123          55          81           0
##    [4]           .         105          22         105           0
##    [5]           .          73           8          73           0
##    ...         ...         ...         ...         ...         ...
##   [62]           .         175          71         178           0
##   [63]           .          19           5          36           0
##   [64]           .          31          27          52           0
##   [65]           .          55           5          66           0
##   [66]           .          26           8          57           0
##           dbSNPPopFreq    Trna_tot    Trna_ref    Trna_var   Trna_alt1
##            <character> <character> <character> <character> <character>
##    [1]               .           0           0           0           0
##    [2]               .           0           0           0           0
##    [3]               .           1           1           0           0
##    [4]               .           0           0           0           0
##    [5] C|1.000;T|0.000           0           0           0           0
##    ...             ...         ...         ...         ...         ...
##   [62]               .          99          65          34           0
##   [63]               .           9           6           3           0
##   [64]               .           7           7           0           0
##   [65]               .           0           0           0           0
##   [66]               .           0           0           0           0
##          Trna_alt2
##        <character>
##    [1]           0
##    [2]           0
##    [3]           0
##    [4]           0
##    [5]           0
##    ...         ...
##   [62]           0
##   [63]           0
##   [64]           0
##   [65]           0
##   [66]           0
##   -------
##   seqinfo: 24 sequences from GRCh37 genome; no seqlengths
## 
## ...
## <89 more elements>

Exporting Data

MultiAssayExperiment provides users with an integrative representation of multi-omic TCGA data at the convenience of the user. For those users who wish to use alternative environments, we have provided an export function to extract all the data from a MultiAssayExperiment instance and write them to a series of files:

td <- tempdir()
tempd <- file.path(td, "ACCMAE")
if (!dir.exists(tempd))
    dir.create(tempd)

exportClass(accmae, dir = tempd, fmt = "csv", ext = ".csv")
## Writing about 6 files to disk...
## [1] "/tmp/RtmpGHLl3g/ACCMAE/accmae_META_1.csv"               
## [2] "/tmp/RtmpGHLl3g/ACCMAE/accmae_ACC_CNASNP-20160128.csv"  
## [3] "/tmp/RtmpGHLl3g/ACCMAE/accmae_ACC_CNVSNP-20160128.csv"  
## [4] "/tmp/RtmpGHLl3g/ACCMAE/accmae_ACC_Mutation-20160128.csv"
## [5] "/tmp/RtmpGHLl3g/ACCMAE/accmae_colData.csv"              
## [6] "/tmp/RtmpGHLl3g/ACCMAE/accmae_sampleMap.csv"

This works for all data classes stored (e.g., RaggedExperiment, HDF5Matrix, SummarizedExperiment) in the MultiAssayExperiment via the assays method which converts classes to matrix format (using individual assay methods).

Session Information

Click here to expand 
sessionInfo()
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] RaggedExperiment_1.31.0     TCGAutils_1.27.0           
##  [3] curatedTCGAData_1.29.1      MultiAssayExperiment_1.33.0
##  [5] SummarizedExperiment_1.37.0 Biobase_2.67.0             
##  [7] GenomicRanges_1.59.0        GenomeInfoDb_1.43.0        
##  [9] IRanges_2.41.0              S4Vectors_0.45.1           
## [11] BiocGenerics_0.53.2         generics_0.1.3             
## [13] MatrixGenerics_1.19.0       matrixStats_1.4.1          
## [15] BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1          dplyr_1.1.4              
##  [3] blob_1.2.4                filelock_1.0.3           
##  [5] Biostrings_2.75.1         bitops_1.0-9             
##  [7] fastmap_1.2.0             RCurl_1.98-1.16          
##  [9] BiocFileCache_2.15.0      GenomicAlignments_1.43.0 
## [11] XML_3.99-0.17             digest_0.6.37            
## [13] mime_0.12                 lifecycle_1.0.4          
## [15] KEGGREST_1.47.0           RSQLite_2.3.7            
## [17] magrittr_2.0.3            compiler_4.4.2           
## [19] rlang_1.1.4               sass_0.4.9               
## [21] tools_4.4.2               utf8_1.2.4               
## [23] yaml_2.3.10               rtracklayer_1.67.0       
## [25] knitr_1.49                S4Arrays_1.7.1           
## [27] bit_4.5.0                 curl_6.0.0               
## [29] DelayedArray_0.33.1       xml2_1.3.6               
## [31] BiocParallel_1.41.0       abind_1.4-8              
## [33] withr_3.0.2               purrr_1.0.2              
## [35] sys_3.4.3                 grid_4.4.2               
## [37] fansi_1.0.6               ExperimentHub_2.15.0     
## [39] cli_3.6.3                 rmarkdown_2.29           
## [41] crayon_1.5.3              rjson_0.2.23             
## [43] httr_1.4.7                tzdb_0.4.0               
## [45] BiocBaseUtils_1.9.0       GenomicDataCommons_1.31.0
## [47] DBI_1.2.3                 cachem_1.1.0             
## [49] stringr_1.5.1             zlibbioc_1.52.0          
## [51] rvest_1.0.4               parallel_4.4.2           
## [53] AnnotationDbi_1.69.0      BiocManager_1.30.25      
## [55] XVector_0.47.0            restfulr_0.0.15          
## [57] vctrs_0.6.5               Matrix_1.7-1             
## [59] jsonlite_1.8.9            hms_1.1.3                
## [61] bit64_4.5.2               GenomicFeatures_1.59.1   
## [63] maketools_1.3.1           jquerylib_0.1.4          
## [65] glue_1.8.0                codetools_0.2-20         
## [67] stringi_1.8.4             BiocVersion_3.21.1       
## [69] BiocIO_1.17.0             UCSC.utils_1.3.0         
## [71] tibble_3.2.1              pillar_1.9.0             
## [73] rappdirs_0.3.3            htmltools_0.5.8.1        
## [75] GenomeInfoDbData_1.2.13   R6_2.5.1                 
## [77] dbplyr_2.5.0              evaluate_1.0.1           
## [79] lattice_0.22-6            readr_2.1.5              
## [81] AnnotationHub_3.15.0      Rsamtools_2.23.0         
## [83] png_0.1-8                 memoise_2.0.1            
## [85] bslib_0.8.0               SparseArray_1.7.1        
## [87] xfun_0.49                 buildtools_1.0.0         
## [89] pkgconfig_2.0.3

References

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 Clinical Cancer Informatics 1 (4): 958–71. https://doi.org/10.1200/CCI.19.00119.
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 Research 77 (21): e39–42. https://doi.org/10.1158/0008-5472.CAN-17-0344.