curatedTCGAData 1.28.0
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("curatedTCGAData")Load packages:
library(curatedTCGAData)
library(MultiAssayExperiment)
library(TCGAutils)curatedTCGADataYour 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.
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>Here is a list of changes to the data provided in version 2.0.1
RNASeq2Gene assays with RSEM gene expression valuesRaggedExperiment objects as GRCh37
rather than 37OV and GBM)mRNAArray data now returns matrix data instead of DataFrameThese data were processed by Broad Firehose pipelines and accessed using RTCGAToolbox. For details on the preprocessing methods used, see the Broad GDAC documentation.
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 ControlsAlternatively, 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>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.
(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 filesNote. For more on how to use a MultiAssayExperiment please see the
MultiAssayExperiment vignette.
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 clusterAnother 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 adrenalThe 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
##
## $`ACC_Mutation-20160128`
##
## 01
## 90You can use the reference dataset (sampleTypes) from the TCGAutils package
to interpret the TCGA sample codes above. The dataset provides clinically
meaningful descriptions:
data(sampleTypes, package = "TCGAutils")
head(sampleTypes)## Code Definition Short.Letter.Code
## 1 01 Primary Solid Tumor TP
## 2 02 Recurrent Solid Tumor TR
## 3 03 Primary Blood Derived Cancer - Peripheral Blood TB
## 4 04 Recurrent Blood Derived Cancer - Bone Marrow TRBM
## 5 05 Additional - New Primary TAP
## 6 06 Metastatic TMOften, an analysis is performed comparing two groups of samples to each other.
To facilitate the separation of samples, the TCGAsplitAssays function from
TCGAutils identifies all sample types in the assays and moves each into its
own assay. By default, all discoverable sample types are separated into a
separate experiment. In this case we requested only solid tumors and blood
derived normal samples as seen in the sampleTypes reference dataset:
sampleTypes[sampleTypes[["Code"]] %in% c("01", "10"), ]## Code Definition Short.Letter.Code
## 1 01 Primary Solid Tumor TP
## 10 10 Blood Derived Normal NBTCGAsplitAssays(accmae, c("01", "10"))## Warning: Some 'sampleCodes' not found in assays## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] 01_ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 90 columns
## [2] 10_ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 85 columns
## [3] 01_ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 90 columns
## [4] 10_ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 85 columns
## [5] 01_ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - 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 filesTo 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")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 filesTo 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
## 90When 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 ADShead(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 NAThe 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>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] "/home/biocbuild/bbs-3.20-data-experiment/tmpdir/RtmpBlNdwn/ACCMAE/accmae_META_1.csv"
## [2] "/home/biocbuild/bbs-3.20-data-experiment/tmpdir/RtmpBlNdwn/ACCMAE/accmae_ACC_CNASNP-20160128.csv"
## [3] "/home/biocbuild/bbs-3.20-data-experiment/tmpdir/RtmpBlNdwn/ACCMAE/accmae_ACC_CNVSNP-20160128.csv"
## [4] "/home/biocbuild/bbs-3.20-data-experiment/tmpdir/RtmpBlNdwn/ACCMAE/accmae_ACC_Mutation-20160128.csv"
## [5] "/home/biocbuild/bbs-3.20-data-experiment/tmpdir/RtmpBlNdwn/ACCMAE/accmae_colData.csv"
## [6] "/home/biocbuild/bbs-3.20-data-experiment/tmpdir/RtmpBlNdwn/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).
Click here to expand
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] RaggedExperiment_1.30.0 TCGAutils_1.26.0
## [3] curatedTCGAData_1.28.0 MultiAssayExperiment_1.32.0
## [5] SummarizedExperiment_1.36.0 Biobase_2.66.0
## [7] GenomicRanges_1.58.0 GenomeInfoDb_1.42.0
## [9] IRanges_2.40.0 S4Vectors_0.44.0
## [11] BiocGenerics_0.52.0 MatrixGenerics_1.18.0
## [13] matrixStats_1.4.1 BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9
## [3] rlang_1.1.4 magrittr_2.0.3
## [5] compiler_4.4.1 RSQLite_2.3.7
## [7] GenomicFeatures_1.58.0 png_0.1-8
## [9] vctrs_0.6.5 rvest_1.0.4
## [11] stringr_1.5.1 pkgconfig_2.0.3
## [13] crayon_1.5.3 fastmap_1.2.0
## [15] dbplyr_2.5.0 XVector_0.46.0
## [17] utf8_1.2.4 Rsamtools_2.22.0
## [19] promises_1.3.0 rmarkdown_2.28
## [21] tzdb_0.4.0 UCSC.utils_1.2.0
## [23] ps_1.8.1 purrr_1.0.2
## [25] bit_4.5.0 xfun_0.48
## [27] zlibbioc_1.52.0 cachem_1.1.0
## [29] jsonlite_1.8.9 blob_1.2.4
## [31] later_1.3.2 DelayedArray_0.32.0
## [33] BiocParallel_1.40.0 parallel_4.4.1
## [35] R6_2.5.1 bslib_0.8.0
## [37] stringi_1.8.4 rtracklayer_1.66.0
## [39] jquerylib_0.1.4 Rcpp_1.0.13
## [41] bookdown_0.41 knitr_1.48
## [43] BiocBaseUtils_1.8.0 readr_2.1.5
## [45] Matrix_1.7-1 tidyselect_1.2.1
## [47] abind_1.4-8 yaml_2.3.10
## [49] codetools_0.2-20 websocket_1.4.2
## [51] curl_5.2.3 processx_3.8.4
## [53] lattice_0.22-6 tibble_3.2.1
## [55] withr_3.0.2 KEGGREST_1.46.0
## [57] evaluate_1.0.1 BiocFileCache_2.14.0
## [59] xml2_1.3.6 ExperimentHub_2.14.0
## [61] Biostrings_2.74.0 pillar_1.9.0
## [63] BiocManager_1.30.25 filelock_1.0.3
## [65] generics_0.1.3 RCurl_1.98-1.16
## [67] chromote_0.3.1 BiocVersion_3.20.0
## [69] hms_1.1.3 glue_1.8.0
## [71] tools_4.4.1 AnnotationHub_3.14.0
## [73] BiocIO_1.16.0 GenomicAlignments_1.42.0
## [75] XML_3.99-0.17 grid_4.4.1
## [77] AnnotationDbi_1.68.0 GenomeInfoDbData_1.2.13
## [79] restfulr_0.0.15 cli_3.6.3
## [81] rappdirs_0.3.3 fansi_1.0.6
## [83] GenomicDataCommons_1.30.0 S4Arrays_1.6.0
## [85] dplyr_1.1.4 sass_0.4.9
## [87] digest_0.6.37 SparseArray_1.6.0
## [89] rjson_0.2.23 memoise_2.0.1
## [91] htmltools_0.5.8.1 lifecycle_1.0.4
## [93] httr_1.4.7 mime_0.12
## [95] bit64_4.5.2Ramos, 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–e42. https://doi.org/10.1158/0008-5472.CAN-17-0344.