Contents

1 Overview

The TCGAutils package completes a suite of Bioconductor packages for convenient access, integration, and analysis of The Cancer Genome Atlas. It includes: 0. helpers for working with TCGA through the Bioconductor packages MultiAssayExperiment (for coordinated representation and manipulation of multi-omits experiments) and curatedTCGAData, which provides unrestricted TCGA data as MultiAssayExperiment objects, 0. helpers for importing TCGA data as from flat data structures such as data.frame or DataFrame read from delimited data structures provided by the Broad Institute’s Firehose, GenomicDataCommons, and 0. functions for interpreting TCGA barcodes and for mapping between barcodes and Universally Unique Identifiers (UUIDs).

2 Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("TCGAutils")

Required packages for this vignette:

library(TCGAutils)
library(curatedTCGAData)
library(MultiAssayExperiment)
library(RTCGAToolbox)
library(rtracklayer)
library(R.utils)

3 curatedTCGAData utility functions

Functions such as getSubtypeMap and getClinicalNames provide information on data inside a MultiAssayExperiment object downloaded from curatedTCGAData. sampleTables and TCGAsplitAssays support useful operations on these MultiAssayExperiment objects.

3.1 obtaining TCGA as MultiAssayExperiment objects from curatedTCGAData

For a list of all available data types, use dry.run = TRUE and an asterisk * as the assay input value:

curatedTCGAData("COAD", "*", dry.run = TRUE, 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
## 7  EH2133  COAD_Methylation_methyl27-20160128_assays   37.2 Mb
## 8  EH2134      COAD_Methylation_methyl27-20160128_se    0.4 Mb
## 9  EH2135 COAD_Methylation_methyl450-20160128_assays  983.8 Mb
## 10 EH2136     COAD_Methylation_methyl450-20160128_se    6.1 Mb
## 11  EH634                 COAD_miRNASeqGene-20160128    0.2 Mb
## 12  EH635                    COAD_mRNAArray-20160128    8.1 Mb
## 13  EH636                     COAD_Mutation-20160128    1.2 Mb
## 14  EH637              COAD_RNASeq2GeneNorm-20160128    8.8 Mb
## 15  EH638                   COAD_RNASeqGene-20160128    0.4 Mb
## 16  EH639                    COAD_RPPAArray-20160128    0.6 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>
## 7        SummarizedExperiment     2019-01-09             <NA>
## 8        SummarizedExperiment     2019-01-09             <NA>
## 9            RaggedExperiment     2019-01-09             <NA>
## 10       SummarizedExperiment     2019-01-09             <NA>
## 11       SummarizedExperiment     2017-10-10             <NA>
## 12       SummarizedExperiment     2017-10-10             <NA>
## 13           RaggedExperiment     2017-10-10             <NA>
## 14       SummarizedExperiment     2017-10-10             <NA>
## 15       SummarizedExperiment     2017-10-10             <NA>
## 16       SummarizedExperiment     2017-10-10             <NA>

In this example, we download part of the Colon Adenocarcinoma (COAD) dataset using curatedTCGAData via ExperimentHub. This command will download data types such as CNASeq, Mutation, etc.:

coad <- curatedTCGAData(
    diseaseCode = "COAD", assays = c("CNASeq", "Mutation", "miRNA*",
    "RNASeq2*", "mRNAArray", "Methyl*"), version = "1.1.38", dry.run = FALSE
)

3.2 sampleTables: what sample types are present in the data?

The sampleTables function gives a tally of available samples in the dataset based on the TCGA barcode information.

sampleTables(coad)
## $`COAD_CNASeq-20160128`
## 
## 01 10 11 
## 68 55 13 
## 
## $`COAD_miRNASeqGene-20160128`
## 
##  01  02 
## 220   1 
## 
## $`COAD_mRNAArray-20160128`
## 
##  01  11 
## 153  19 
## 
## $`COAD_Mutation-20160128`
## 
##  01 
## 154 
## 
## $`COAD_RNASeq2GeneNorm-20160128`
## 
##  01 
## 191 
## 
## $`COAD_Methylation_methyl27-20160128`
## 
##  01  11 
## 165  37 
## 
## $`COAD_Methylation_methyl450-20160128`
## 
##  01  02  06  11 
## 293   1   1  38

For reference in interpreting the sample type codes, see the sampleTypes table:

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

3.3 TCGAsplitAssays: separate the data from different tissue types

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

(tnmae <- TCGAsplitAssays(coad, c("01", "11")))
## Warning: Some 'sampleCodes' not found in assays
## A MultiAssayExperiment object of 11 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 11:
##  [1] 01_COAD_CNASeq-20160128: RaggedExperiment with 40530 rows and 68 columns
##  [2] 11_COAD_CNASeq-20160128: RaggedExperiment with 40530 rows and 13 columns
##  [3] 01_COAD_miRNASeqGene-20160128: SummarizedExperiment with 705 rows and 220 columns
##  [4] 01_COAD_mRNAArray-20160128: SummarizedExperiment with 17814 rows and 153 columns
##  [5] 11_COAD_mRNAArray-20160128: SummarizedExperiment with 17814 rows and 19 columns
##  [6] 01_COAD_Mutation-20160128: RaggedExperiment with 62530 rows and 154 columns
##  [7] 01_COAD_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 191 columns
##  [8] 01_COAD_Methylation_methyl27-20160128: SummarizedExperiment with 27578 rows and 165 columns
##  [9] 11_COAD_Methylation_methyl27-20160128: SummarizedExperiment with 27578 rows and 37 columns
##  [10] 01_COAD_Methylation_methyl450-20160128: SummarizedExperiment with 485577 rows and 293 columns
##  [11] 11_COAD_Methylation_methyl450-20160128: SummarizedExperiment with 485577 rows and 38 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

The MultiAssayExperiment package then provides functionality to merge replicate profiles for a single patient (mergeReplicates()), which would now be appropriate but would not have been appropriate before splitting different tissue types into different assays, because that would average measurements from tumors and normal tissues.

MultiAssayExperiment also defines the MatchedAssayExperiment class, which eliminates any profiles not present across all assays and ensures identical ordering of profiles (columns) in each assay. In this example, it will match tumors to adjacent normals in subsequent assays:

(matchmae <- as(tnmae[, , c(4, 6, 7)], "MatchedAssayExperiment"))
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 853 sampleMap rows not in names(experiments)
##   removing 260 colData rownames not in sampleMap 'primary'
## A MatchedAssayExperiment object of 3 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 3:
##  [1] 01_COAD_mRNAArray-20160128: SummarizedExperiment with 17814 rows and 138 columns
##  [2] 01_COAD_Mutation-20160128: RaggedExperiment with 62530 rows and 138 columns
##  [3] 01_COAD_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 138 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

Only about 12 participants have both a matched tumor and solid normal sample.

3.4 getSubtypeMap: manually curated molecular subtypes

Per-tumor subtypes are saved in the metadata of the colData slot of MultiAssayExperiment objects downloaded from curatedTCGAData. These subtypes were manually curated from the supplemental tables of all primary TCGA publications:

getSubtypeMap(coad)
##        COAD_annotations        COAD_subtype
## 1            Patient_ID           patientID
## 2                   msi          MSI_status
## 3  methylation_subtypes methylation_subtype
## 4         mrna_subtypes  expression_subtype
## 5 histological_subtypes   histological_type

3.5 getClinicalNames: key “level 4” clinical & pathological data

The curatedTCGAData colData contain hundreds of columns, obtained from merging all unrestricted levels of clinical, pathological, and biospecimen data. This function provides the names of “level 4” clinical/pathological variables, which are the only ones provided by most other TCGA analysis tools. Users may then use these variable names for subsetting or analysis, and may even want to subset the colData to only these commonly used variables.

getClinicalNames("COAD")
##  [1] "years_to_birth"                      
##  [2] "vital_status"                        
##  [3] "days_to_death"                       
##  [4] "days_to_last_followup"               
##  [5] "tumor_tissue_site"                   
##  [6] "pathologic_stage"                    
##  [7] "pathology_T_stage"                   
##  [8] "pathology_N_stage"                   
##  [9] "pathology_M_stage"                   
## [10] "gender"                              
## [11] "date_of_initial_pathologic_diagnosis"
## [12] "days_to_last_known_alive"            
## [13] "radiation_therapy"                   
## [14] "histological_type"                   
## [15] "residual_tumor"                      
## [16] "number_of_lymph_nodes"               
## [17] "race"                                
## [18] "ethnicity"

Warning: some names may not exactly match the colData names in the object due to differences in variable types. These variables are kept separate and differentiated with x and y. For example, vital_status in this case corresponds to two different variables obtained from the pipeline. One variable is interger type and the other character:

class(colData(coad)[["vital_status.x"]])
## [1] "integer"
class(colData(coad)[["vital_status.y"]])
## [1] "character"
table(colData(coad)[["vital_status.x"]])
## 
##   0   1 
## 355 102
table(colData(coad)[["vital_status.y"]])
## 
## DECEASED   LIVING 
##       22      179

Such conflicts should be inspected in this manner, and conflicts resolved by choosing the more complete variable, or by treating any conflicting values as unknown (“NA”).

4 Converting Assays to SummarizedExperiment

This section gives an overview of the operations that can be performed on a given set of metadata obtained particularly from data-rich objects such as those obtained from curatedTCGAData. There are several operations that work with microRNA, methylation, mutation, and assays that have gene symbol annotations.

4.1 CpGtoRanges

Using the methylation annotations in IlluminaHumanMethylation450kanno.ilmn12.hg19 and the minfi package, we look up CpG probes and convert to genomic coordinates with CpGtoRanges. The function provides two assays, one with mapped probes and the other with unmapped probes. Excluding unmapped probes can be done by setting the unmapped argument to FALSE. This will run for both types of methylation data (27k and 450k).

methcoad <- CpGtoRanges(coad)
## Setting options('download.file.method.GEOquery'='auto')
## Setting options('GEOquery.inmemory.gpl'=FALSE)
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 535 sampleMap rows not in names(experiments)

4.2 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.

mircoad <- mirToRanges(coad)
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 221 sampleMap rows not in names(experiments)

4.3 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.

It uses ‘hg19’ transcript database (‘TxDb’) package internally to summarize regions using qreduceAssay. The current genome build (‘hg18’) in the data must be translated to ‘hg19’.

In this example, we first set the appropriate build name in the mutation dataset COAD_Mutation-20160128 according to the NCBI website and we then use seqlevelsStyle to match the UCSC style in the chain.

rag <- "COAD_Mutation-20160128"
# add the appropriate genome annotation
genome(coad[[rag]]) <- "NCBI36"
# change the style to UCSC
seqlevelsStyle(rowRanges(coad[[rag]])) <- "UCSC"

# inspect changes
seqlevels(rowRanges(coad[[rag]]))
##  [1] "chr1"  "chr2"  "chr3"  "chr4"  "chr5"  "chr6"  "chr7"  "chr8"  "chr9" 
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17" "chr18"
## [19] "chr19" "chr20" "chr21" "chr22" "chrX"  "chrY"
genome(coad[[rag]])
##   chr1   chr2   chr3   chr4   chr5   chr6   chr7   chr8   chr9  chr10  chr11 
## "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" 
##  chr12  chr13  chr14  chr15  chr16  chr17  chr18  chr19  chr20  chr21  chr22 
## "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" "hg18" 
##   chrX   chrY 
## "hg18" "hg18"

Now we use liftOver from rtracklayer to translate ‘hg18’ builds to ‘hg19’ using the chain file obtained via AnnotationHub. We use a query to find the file. You can also query with terms such as c("Homo sapiens", "chain", "hg18", "hg19") in the query function. We are specifically looking for the chain file “hg18ToHg19.over.chain.gz”.

library(AnnotationHub)
ah <- AnnotationHub()
query(ah, "hg18ToHg19.over.chain.gz")
## AnnotationHub with 1 record
## # snapshotDate(): 2024-10-24
## # names(): AH14220
## # $dataprovider: UCSC
## # $species: Homo sapiens
## # $rdataclass: ChainFile
## # $rdatadateadded: 2014-12-15
## # $title: hg18ToHg19.over.chain.gz
## # $description: UCSC liftOver chain file from hg18 to hg19
## # $taxonomyid: 9606
## # $genome: hg18
## # $sourcetype: Chain
## # $sourceurl: http://hgdownload.cse.ucsc.edu/goldenpath/hg18/liftOver/hg18To...
## # $sourcesize: NA
## # $tags: c("liftOver", "chain", "UCSC", "genome", "homology") 
## # retrieve record with 'object[["AH14220"]]'
chain <- ah[["AH14220"]]
## loading from cache
ranges19 <- rtracklayer::liftOver(rowRanges(coad[[rag]]), chain)

Note. The same can be done to convert hg19 to hg38 (the same build that the Genomic Data Commons uses) with the corresponding chain file as obtained above.

This will give us a list of ranges, each element corresponding to a single row in the RaggedExperiment. We remove rows that had no matches in the liftOver process and replace the ranges in the original RaggedExperiment with the replacement method. Finally, we put the RaggedExperiment object back into the MultiAssayExperiment.

re19 <- coad[[rag]][as.logical(lengths(ranges19))]
ranges19 <- unlist(ranges19)
genome(ranges19) <- "hg19"
rowRanges(re19) <- ranges19
# replacement
coad[["COAD_Mutation-20160128"]] <- re19
rowRanges(re19)
## GRanges object with 62523 ranges and 0 metadata columns:
##           seqnames              ranges strand
##              <Rle>           <IRanges>  <Rle>
##       [1]    chr20     1552407-1552408      +
##       [2]     chr1 161736152-161736153      +
##       [3]     chr7           100685895      +
##       [4]     chr7           103824453      +
##       [5]     chr7           104783644      +
##       ...      ...                 ...    ...
##   [62519]     chr9            36369716      +
##   [62520]     chr9            37692640      +
##   [62521]     chr9             6007456      +
##   [62522]     chrX           123785782      +
##   [62523]     chrX            51487184      +
##   -------
##   seqinfo: 24 sequences from hg19 genome; no seqlengths

Now that we have matching builds, we can finally run the qreduceTCGA function.

coad <- qreduceTCGA(coad, keep.assay = TRUE)
## 
##   403 genes were dropped because they have exons located on both strands
##   of the same reference sequence or on more than one reference sequence,
##   so cannot be represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a
##   GRangesList object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
## Warning in .normarg_seqlevelsStyle(value): more than one seqlevels style
## supplied, using the 1st one only
## Warning in .normarg_seqlevelsStyle(value): cannot switch some hg19's seqlevels
## from UCSC to NCBI style

4.4 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(coad)
##   403 genes were dropped because they have exons located on both strands
##   of the same reference sequence or on more than one reference sequence,
##   so cannot be represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a
##   GRangesList object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
##   403 genes were dropped because they have exons located on both strands
##   of the same reference sequence or on more than one reference sequence,
##   so cannot be represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a
##   GRangesList object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
##   removing 363 sampleMap rows not in names(experiments)
## A MultiAssayExperiment object of 11 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 11:
##  [1] COAD_CNASeq-20160128: RaggedExperiment with 40530 rows and 136 columns
##  [2] COAD_miRNASeqGene-20160128: SummarizedExperiment with 705 rows and 221 columns
##  [3] COAD_Mutation-20160128: RaggedExperiment with 62523 rows and 154 columns
##  [4] COAD_Methylation_methyl27-20160128: SummarizedExperiment with 27578 rows and 202 columns
##  [5] COAD_Methylation_methyl450-20160128: SummarizedExperiment with 485577 rows and 333 columns
##  [6] COAD_Mutation-20160128_simplified: RangedSummarizedExperiment with 22913 rows and 154 columns
##  [7] COAD_CNASeq-20160128_simplified: RangedSummarizedExperiment with 22913 rows and 136 columns
##  [8] COAD_mRNAArray-20160128_ranged: RangedSummarizedExperiment with 14121 rows and 172 columns
##  [9] COAD_mRNAArray-20160128_unranged: SummarizedExperiment with 3693 rows and 172 columns
##  [10] COAD_RNASeq2GeneNorm-20160128_ranged: RangedSummarizedExperiment with 16970 rows and 191 columns
##  [11] COAD_RNASeq2GeneNorm-20160128_unranged: SummarizedExperiment with 3531 rows and 191 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

5 Importing TCGA text data files to Bioconductor classes

A few functions in the package accept either files or classes such as data.frame and FirehoseGISTIC as input and return standard Bioconductor classes.

5.0.1 Work around for long file names on Windows

Due to file name length, Windows may not be able to read / display all files. The workaround uses the fileNames argument from a character vector of file names and will convert them to TCGA barcodes.

## Load example file found in package
pkgDir <- system.file("extdata", package = "TCGAutils", mustWork = TRUE)
exonFile <- list.files(pkgDir, pattern = "cation\\.txt$", full.names = TRUE)
exonFile
## [1] "/tmp/Rtmp9nmQjv/Rinst1ce37b304ef74a/TCGAutils/extdata/bt.exon_quantification.txt"
## We add the original file prefix to query for the UUID and get the
## TCGAbarcode
filePrefix <- "unc.edu.32741f9a-9fec-441f-96b4-e504e62c5362.1755371."

## Add actual file name manually
makeGRangesListFromExonFiles(exonFile, getBarcodes = FALSE,
    fileNames = paste0(filePrefix, basename(exonFile)))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   exon = col_character(),
##   raw_counts = col_double(),
##   median_length_normalized = col_double(),
##   RPKM = col_double()
## )
## GRangesList object of length 1:
## $`unc.edu.32741f9a-9fec-441f-96b4-e504e62c5362.1755371.bt.exon_quantification.txt`
## GRanges object with 100 ranges and 3 metadata columns:
##         seqnames        ranges strand | raw_counts median_length_normalized
##            <Rle>     <IRanges>  <Rle> |  <numeric>                <numeric>
##     [1]     chr1   11874-12227      + |          4                 0.492918
##     [2]     chr1   12595-12721      + |          2                 0.341270
##     [3]     chr1   12613-12721      + |          2                 0.398148
##     [4]     chr1   12646-12697      + |          2                 0.372549
##     [5]     chr1   13221-14409      + |         39                 0.632997
##     ...      ...           ...    ... .        ...                      ...
##    [96]     chr1 881782-881925      - |        179                        1
##    [97]     chr1 883511-883612      - |        151                        1
##    [98]     chr1 883870-883983      - |        155                        1
##    [99]     chr1 886507-886618      - |        144                        1
##   [100]     chr1 887380-887519      - |        158                        1
##              RPKM
##         <numeric>
##     [1]  0.322477
##     [2]  0.449436
##     [3]  0.523655
##     [4]  1.097661
##     [5]  0.936105
##     ...       ...
##    [96]   35.4758
##    [97]   42.2492
##    [98]   38.8033
##    [99]   36.6933
##   [100]   32.2085
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

NOTE. Querying the API for legacy file names is no longer supported. In this example, we leave the file name in rather than translate the file name to a TCGA barcode, i.e., getBarcodes = FALSE. However, filenameToBarcode is still supported for other file types.

5.1 makeGRangesListFromCopyNumber

Other processed, genomic range-based data from TCGA data can be imported using makeGRangesListFromCopyNumber. This tab-delimited data file of copy number alterations from bladder urothelial carcinoma (BLCA) was obtained from the Genomic Data Commons and is included in TCGAUtils as an example:

grlFile <- system.file("extdata", "blca_cnaseq.txt", package = "TCGAutils")
grl <- read.table(grlFile)
head(grl)
##                          Sample Chromosome     Start       End Num_Probes
## 1  TCGA-BL-A0C8-01A-11D-A10R-02         14  70362113  73912204         NA
## 2  TCGA-BL-A0C8-01A-11D-A10R-02          9 115609546 131133898         NA
## 5  TCGA-BL-A13I-01A-11D-A13U-02         13  19020028  49129100         NA
## 6  TCGA-BL-A13I-01A-11D-A13U-02          1     10208 246409808         NA
## 9  TCGA-BL-A13J-01A-11D-A10R-02         23   3119586   5636448         NA
## 10 TCGA-BL-A13J-01A-11D-A10R-02          7     10127  35776912         NA
##    Segment_Mean
## 1  -0.182879931
## 2   0.039675162
## 5   0.002085552
## 6  -0.014224752
## 9   0.877072555
## 10  0.113873871
makeGRangesListFromCopyNumber(grl, split.field = "Sample")
## GRangesList object of length 116:
## $`TCGA-BL-A0C8-01A-11D-A10R-02`
## GRanges object with 2 ranges and 0 metadata columns:
##       seqnames              ranges strand
##          <Rle>           <IRanges>  <Rle>
##   [1]       14   70362113-73912204      *
##   [2]        9 115609546-131133898      *
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths
## 
## $`TCGA-BL-A13I-01A-11D-A13U-02`
## GRanges object with 2 ranges and 0 metadata columns:
##       seqnames            ranges strand
##          <Rle>         <IRanges>  <Rle>
##   [1]       13 19020028-49129100      *
##   [2]        1   10208-246409808      *
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths
## 
## $`TCGA-BL-A13J-01A-11D-A10R-02`
## GRanges object with 2 ranges and 0 metadata columns:
##       seqnames          ranges strand
##          <Rle>       <IRanges>  <Rle>
##   [1]       23 3119586-5636448      *
##   [2]        7  10127-35776912      *
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths
## 
## ...
## <113 more elements>
makeGRangesListFromCopyNumber(grl, split.field = "Sample",
    keep.extra.columns = TRUE)
## GRangesList object of length 116:
## $`TCGA-BL-A0C8-01A-11D-A10R-02`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames              ranges strand | Num_Probes Segment_Mean
##          <Rle>           <IRanges>  <Rle> |  <logical>    <numeric>
##   [1]       14   70362113-73912204      * |       <NA>   -0.1828799
##   [2]        9 115609546-131133898      * |       <NA>    0.0396752
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths
## 
## $`TCGA-BL-A13I-01A-11D-A13U-02`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand | Num_Probes Segment_Mean
##          <Rle>         <IRanges>  <Rle> |  <logical>    <numeric>
##   [1]       13 19020028-49129100      * |       <NA>   0.00208555
##   [2]        1   10208-246409808      * |       <NA>  -0.01422475
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths
## 
## $`TCGA-BL-A13J-01A-11D-A10R-02`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames          ranges strand | Num_Probes Segment_Mean
##          <Rle>       <IRanges>  <Rle> |  <logical>    <numeric>
##   [1]       23 3119586-5636448      * |       <NA>     0.877073
##   [2]        7  10127-35776912      * |       <NA>     0.113874
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths
## 
## ...
## <113 more elements>

5.2 makeSummarizedExperimentFromGISTIC

This function is only used for converting the FirehoseGISTIC class of the RTCGAToolbox package. It allows the user to obtain thresholded by gene data, probabilities and peak regions.

tempDIR <- tempdir()
co <- getFirehoseData("COAD", clinical = FALSE, GISTIC = TRUE,
    destdir = tempDIR)

selectType(co, "GISTIC")
## Dataset:COAD
## FirehoseGISTIC object, dim: 24776    454
class(selectType(co, "GISTIC"))
## [1] "FirehoseGISTIC"
## attr(,"package")
## [1] "RTCGAToolbox"
makeSummarizedExperimentFromGISTIC(co, "Peaks")
## class: RangedSummarizedExperiment 
## dim: 66 451 
## metadata(0):
## assays(1): ''
## rownames(66): 23 24 ... 65 66
## rowData names(12): rowRanges Unique.Name ... V461 type
## colnames(451): TCGA-3L-AA1B-01A-11D-A36W-01
##   TCGA-4N-A93T-01A-11D-A36W-01 ... TCGA-T9-A92H-01A-11D-A36W-01
##   TCGA-WS-AB45-01A-11D-A40O-01
## colData names(0):

5.3 mergeColData: expanding the colData of a MultiAssayExperiment

This function merges a data.frame or DataFrame into the colData of an existing MultiAssayExperiment object. It will match column names and row names to do a full merge of both data sets. This convenience function can be used, for example, to add subtype information available for a subset of patients to the colData. Here is a simplified example of adding a column to the colData DataFrame:

race_df <- DataFrame(race_f = factor(colData(coad)[["race"]]),
    row.names = rownames(colData(coad)))
mergeColData(coad, race_df)
## A MultiAssayExperiment object of 9 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 9:
##  [1] COAD_CNASeq-20160128: RaggedExperiment with 40530 rows and 136 columns
##  [2] COAD_miRNASeqGene-20160128: SummarizedExperiment with 705 rows and 221 columns
##  [3] COAD_mRNAArray-20160128: SummarizedExperiment with 17814 rows and 172 columns
##  [4] COAD_Mutation-20160128: RaggedExperiment with 62523 rows and 154 columns
##  [5] COAD_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 191 columns
##  [6] COAD_Methylation_methyl27-20160128: SummarizedExperiment with 27578 rows and 202 columns
##  [7] COAD_Methylation_methyl450-20160128: SummarizedExperiment with 485577 rows and 333 columns
##  [8] COAD_Mutation-20160128_simplified: RangedSummarizedExperiment with 22913 rows and 154 columns
##  [9] COAD_CNASeq-20160128_simplified: RangedSummarizedExperiment with 22913 rows and 136 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

6 Translating and interpreting TCGA identifiers

6.1 GDC Data Updates

Data Release (version 32.0) changed the behavior of the ID translation functions. This is due to the removal of files whose UUIDs were translated. These files have been replaced with newer runs of the pipeline. The new UUIDs can be mapped to the old UUIDs with project specific maps located on GitHub at https://github.com/NCI-GDC/gdc-docs/tree/develop/docs/Data/Release_Notes/GCv36_Manifests as well as with the history lookup API endpoint.

Note. The GDC API version v3.28.0 has deprecated the /legacy/files endpoint; therefore, legacy data is no longer available.

6.1.1 UUID History Lookup

Facilities in the GDC API allow one to look up old UUIDs and obtain new ones. The UUIDhistory function returns a data.frame with old and new UUIDs for a single UUID lookup. This will allow one to map old UUIDs with new ones in order to run a translation query. Old UUIDs have been removed from the API and can no longer be translated by the functions provided in this package.

UUIDhistory("0001801b-54b0-4551-8d7a-d66fb59429bf")
##                                   uuid version file_change release_date
## 1 0001801b-54b0-4551-8d7a-d66fb59429bf       1  superseded   2018-08-23
## 2 b4bce3ff-7fdc-4849-880b-56f2b348ceac       2    released   2022-03-29
##   data_release
## 1         12.0
## 2         32.0

6.2 Translation

The TCGA project has generated massive amounts of data. Some data can be obtained with Universally Unique IDentifiers (UUID) and other data with TCGA barcodes. The Genomic Data Commons provides a JSON API for mapping between UUID and barcode, but it is difficult for many people to understand. TCGAutils makes simple functions available for two-way translation between vectors of these identifiers.

6.2.1 TCGA barcode to UUID

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

(xbarcode <- head(colnames(coad)[["COAD_CNASeq-20160128_simplified"]], 4L))
## [1] "TCGA-A6-2671-01A-01D-1405-02" "TCGA-A6-2671-10A-01D-1405-02"
## [3] "TCGA-A6-2674-01A-02D-1167-02" "TCGA-A6-2674-10A-01D-1167-02"
barcodeToUUID(xbarcode)
##           submitter_aliquot_ids                          aliquot_ids
## 46 TCGA-A6-2671-01A-01D-1405-02 82e23baf-da11-4175-bee0-81c0c0137d72
## 70 TCGA-A6-2671-10A-01D-1405-02 da65c9d3-62ac-4fb5-b452-1e9c551ba243
## 11 TCGA-A6-2674-01A-02D-1167-02 9fdfc199-b878-4049-994e-b5ca384678fb
## 38 TCGA-A6-2674-10A-01D-1167-02 dd75656d-6df6-4c53-972d-439791c908ac

6.2.2 UUID to TCGA barcode

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

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

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

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

Translating aliquot UUIDs is also possible by providing a known aliquot UUID to the function and giving a from_type, “aliquot_ids”:

UUIDtoBarcode("d85d8a17-8aea-49d3-8a03-8f13141c163b", from_type = "aliquot_ids")
##   portions.analytes.aliquots.aliquot_id portions.analytes.aliquots.submitter_id
## 1  d85d8a17-8aea-49d3-8a03-8f13141c163b            TCGA-CV-5443-01A-01D-1510-01

Additional UUIDs may be supported in future versions.

6.2.3 UUID to UUID

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 e5fc0ead-8403-4494-be9c-43336e5fa7c6
## 2 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 988dbb6d-c444-4387-a15f-95771c3f6cfd
## 3 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 f249a5b5-4172-4250-a5f7-002165e45a53
## 4 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 86d98f72-a320-47d6-9c21-6274097851f0
## 5 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 78f1f4b2-1ee9-4c8f-92a2-fe437eeba4c5
## 6 ae55b2d3-62a1-419e-9f9a-5ddfac356db4 fc754a43-fd64-4dba-bf0c-6c567e292e9b

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/0ff55a5e-6058-4e0b-9641-e3cb375ff214

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

6.3 Parsing TCGA barcodes

Several functions exist for working with TCGA barcodes, the main function being TCGAbarcode. It takes a TCGA barcode and returns information about participant, sample, and/or portion.

## Return participant barcodes
TCGAbarcode(xbarcode, participant = TRUE)
## [1] "TCGA-A6-2671" "TCGA-A6-2671" "TCGA-A6-2674" "TCGA-A6-2674"
## Just return samples
TCGAbarcode(xbarcode, participant = FALSE, sample = TRUE)
## [1] "01A" "10A" "01A" "10A"
## Include sample data as well
TCGAbarcode(xbarcode, participant = TRUE, sample = TRUE)
## [1] "TCGA-A6-2671-01A" "TCGA-A6-2671-10A" "TCGA-A6-2674-01A" "TCGA-A6-2674-10A"
## Include portion and analyte data
TCGAbarcode(xbarcode, participant = TRUE, sample = TRUE, portion = TRUE)
## [1] "TCGA-A6-2671-01A-01D" "TCGA-A6-2671-10A-01D" "TCGA-A6-2674-01A-02D"
## [4] "TCGA-A6-2674-10A-01D"

6.4 Sample selection

Based on lookup table values, the user can select certain sample types from a vector of sample barcodes. Below we select “Primary Solid Tumors” from a vector of barcodes, returning a logical vector identifying the matching samples.

## Select primary solid tumors
TCGAsampleSelect(xbarcode, "01")
##    01    10    01    10 
##  TRUE FALSE  TRUE FALSE
## Select blood derived normals
TCGAsampleSelect(xbarcode, "10")
##    01    10    01    10 
## FALSE  TRUE FALSE  TRUE

6.4.1 Primary tumors

We provide a TCGAprimaryTumors helper function to facilitate the selection of primary tumor samples only:

TCGAprimaryTumors(coad)
## harmonizing input:
##   removing 233 sampleMap rows with 'colname' not in colnames of experiments
## A MultiAssayExperiment object of 9 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 9:
##  [1] COAD_CNASeq-20160128: RaggedExperiment with 40530 rows and 68 columns
##  [2] COAD_miRNASeqGene-20160128: SummarizedExperiment with 705 rows and 220 columns
##  [3] COAD_mRNAArray-20160128: SummarizedExperiment with 17814 rows and 153 columns
##  [4] COAD_Mutation-20160128: RaggedExperiment with 62523 rows and 154 columns
##  [5] COAD_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 191 columns
##  [6] COAD_Methylation_methyl27-20160128: SummarizedExperiment with 27578 rows and 165 columns
##  [7] COAD_Methylation_methyl450-20160128: SummarizedExperiment with 485577 rows and 293 columns
##  [8] COAD_Mutation-20160128_simplified: RangedSummarizedExperiment with 22913 rows and 154 columns
##  [9] COAD_CNASeq-20160128_simplified: RangedSummarizedExperiment with 22913 rows and 68 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

6.5 data.frame representation of barcode

The straightforward TCGAbiospec function will take the information contained in the TCGA barcode and display it in data.frame format with appropriate column names.

TCGAbiospec(xbarcode)
##   submitter_id    sample_definition sample vial portion analyte plate center
## 1 TCGA-A6-2671  Primary Solid Tumor     01    A      01       D  1405     02
## 2 TCGA-A6-2671 Blood Derived Normal     10    A      01       D  1405     02
## 3 TCGA-A6-2674  Primary Solid Tumor     01    A      02       D  1167     02
## 4 TCGA-A6-2674 Blood Derived Normal     10    A      01       D  1167     02

7 OncoPrint - oncoPrintTCGA

We provide a convenience function that investigates metadata within curatedTCGAData objects to present a plot of molecular alterations within a paricular cancer. MultiAssayExperiment objects are required to have an identifiable ‘Mutation’ assay (using text search). The variantCol argument identifies the mutation type column within the data.

Note. Functionality streamlined from the ComplexHeatmap package.

oncoPrintTCGA(coad, matchassay = rag)
##   403 genes were dropped because they have exons located on both strands
##   of the same reference sequence or on more than one reference sequence,
##   so cannot be represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a
##   GRangesList object, or use suppressMessages() to suppress this message.
## Warning in (function (seqlevels, genome, new_style) : cannot switch some hg19's
## seqlevels from UCSC to NCBI style
## 'select()' returned 1:1 mapping between keys and columns
## All mutation types: Frame Shift Del, Frame Shift Ins, Intron, Missense
## Mutation, Nonsense Mutation.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.

8 Reference data

The TCGAutils package provides several helper datasets for working with TCGA barcodes.

8.1 sampleTypes

As shown previously, the reference dataset sampleTypes defines sample codes and their sample types (see ?sampleTypes for source url).

## Obtained previously
sampleCodes <- TCGAbarcode(xbarcode, participant = FALSE, sample = TRUE)

## Lookup table
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
## Match codes found in the barcode to the lookup table
sampleTypes[match(unique(substr(sampleCodes, 1L, 2L)), sampleTypes[["Code"]]), ]
##    Code           Definition Short.Letter.Code
## 1    01  Primary Solid Tumor                TP
## 10   10 Blood Derived Normal                NB

Source: https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes

8.2 clinicalNames - Firehose pipeline clinical variables

clinicalNames is a list of the level 4 variable names (the most commonly used clinical and pathological variables, with follow-ups merged) from each colData datasets in curatedTCGAData. Shipped curatedTCGAData MultiAssayExperiment objects merge additional levels 1-3 clinical, pathological, and biospecimen data and contain many more variables than the ones listed here.

data("clinicalNames")

clinicalNames
## CharacterList of length 33
## [["ACC"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["BLCA"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["BRCA"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["CESC"]] years_to_birth vital_status ... age_at_diagnosis clinical_stage
## [["CHOL"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["COAD"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["DLBC"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["ESCA"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["GBM"]] years_to_birth vital_status days_to_death ... race ethnicity
## [["HNSC"]] years_to_birth vital_status days_to_death ... race ethnicity
## ...
## <23 more elements>
lengths(clinicalNames)
##  ACC BLCA BRCA CESC CHOL COAD DLBC ESCA  GBM HNSC KICH KIRC KIRP LAML  LGG LIHC 
##   16   18   17   48   16   18   11   19   12   19   18   19   19    9   12   16 
## LUAD LUSC MESO   OV PAAD PCPG PRAD READ SARC SKCM STAD TGCT THCA THYM UCEC  UCS 
##   20   20   17   12   19   13   18   18   12   17   17   15   21   11    9   11 
##  UVM 
##   14

9 sessionInfo

sessionInfo()
## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.21-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] AnnotationHub_3.15.0        BiocFileCache_2.15.0       
##  [3] dbplyr_2.5.0                rhdf5_2.51.0               
##  [5] RaggedExperiment_1.31.0     R.utils_2.12.3             
##  [7] R.oo_1.26.0                 R.methodsS3_1.8.2          
##  [9] rtracklayer_1.67.0          RTCGAToolbox_2.37.0        
## [11] curatedTCGAData_1.27.1      MultiAssayExperiment_1.33.0
## [13] SummarizedExperiment_1.37.0 Biobase_2.67.0             
## [15] GenomicRanges_1.59.0        GenomeInfoDb_1.43.0        
## [17] IRanges_2.41.0              S4Vectors_0.45.0           
## [19] BiocGenerics_0.53.0         MatrixGenerics_1.19.0      
## [21] matrixStats_1.4.1           TCGAutils_1.27.0           
## [23] BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.5.0                                     
##   [2] later_1.3.2                                       
##   [3] BiocIO_1.17.0                                     
##   [4] bitops_1.0-9                                      
##   [5] filelock_1.0.3                                    
##   [6] tibble_3.2.1                                      
##   [7] preprocessCore_1.69.0                             
##   [8] XML_3.99-0.17                                     
##   [9] lifecycle_1.0.4                                   
##  [10] doParallel_1.0.17                                 
##  [11] processx_3.8.4                                    
##  [12] lattice_0.22-6                                    
##  [13] MASS_7.3-61                                       
##  [14] base64_2.0.2                                      
##  [15] scrime_1.3.5                                      
##  [16] magrittr_2.0.3                                    
##  [17] minfi_1.53.0                                      
##  [18] limma_3.63.0                                      
##  [19] sass_0.4.9                                        
##  [20] rmarkdown_2.28                                    
##  [21] jquerylib_0.1.4                                   
##  [22] yaml_2.3.10                                       
##  [23] doRNG_1.8.6                                       
##  [24] askpass_1.2.1                                     
##  [25] chromote_0.3.1                                    
##  [26] DBI_1.2.3                                         
##  [27] RColorBrewer_1.1-3                                
##  [28] abind_1.4-8                                       
##  [29] zlibbioc_1.53.0                                   
##  [30] rvest_1.0.4                                       
##  [31] quadprog_1.5-8                                    
##  [32] purrr_1.0.2                                       
##  [33] RCurl_1.98-1.16                                   
##  [34] rappdirs_0.3.3                                    
##  [35] circlize_0.4.16                                   
##  [36] GenomeInfoDbData_1.2.13                           
##  [37] rentrez_1.2.3                                     
##  [38] genefilter_1.89.0                                 
##  [39] annotate_1.85.0                                   
##  [40] DelayedMatrixStats_1.29.0                         
##  [41] codetools_0.2-20                                  
##  [42] DelayedArray_0.33.0                               
##  [43] xml2_1.3.6                                        
##  [44] shape_1.4.6.1                                     
##  [45] tidyselect_1.2.1                                  
##  [46] UCSC.utils_1.3.0                                  
##  [47] beanplot_1.3.1                                    
##  [48] illuminaio_0.49.0                                 
##  [49] GenomicAlignments_1.43.0                          
##  [50] jsonlite_1.8.9                                    
##  [51] GetoptLong_1.0.5                                  
##  [52] multtest_2.63.0                                   
##  [53] survival_3.7-0                                    
##  [54] iterators_1.0.14                                  
##  [55] foreach_1.5.2                                     
##  [56] tools_4.5.0                                       
##  [57] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2           
##  [58] Rcpp_1.0.13                                       
##  [59] glue_1.8.0                                        
##  [60] GenomicDataCommons_1.31.0                         
##  [61] SparseArray_1.7.0                                 
##  [62] BiocBaseUtils_1.9.0                               
##  [63] xfun_0.48                                         
##  [64] websocket_1.4.2                                   
##  [65] dplyr_1.1.4                                       
##  [66] HDF5Array_1.35.0                                  
##  [67] withr_3.0.2                                       
##  [68] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
##  [69] BiocManager_1.30.25                               
##  [70] fastmap_1.2.0                                     
##  [71] rhdf5filters_1.19.0                               
##  [72] fansi_1.0.6                                       
##  [73] openssl_2.2.2                                     
##  [74] digest_0.6.37                                     
##  [75] R6_2.5.1                                          
##  [76] mime_0.12                                         
##  [77] colorspace_2.1-1                                  
##  [78] Cairo_1.6-2                                       
##  [79] RSQLite_2.3.7                                     
##  [80] utf8_1.2.4                                        
##  [81] tidyr_1.3.1                                       
##  [82] generics_0.1.3                                    
##  [83] data.table_1.16.2                                 
##  [84] httr_1.4.7                                        
##  [85] S4Arrays_1.7.0                                    
##  [86] RJSONIO_1.3-1.9                                   
##  [87] pkgconfig_2.0.3                                   
##  [88] blob_1.2.4                                        
##  [89] ComplexHeatmap_2.23.0                             
##  [90] siggenes_1.81.0                                   
##  [91] XVector_0.47.0                                    
##  [92] htmltools_0.5.8.1                                 
##  [93] bookdown_0.41                                     
##  [94] clue_0.3-65                                       
##  [95] png_0.1-8                                         
##  [96] knitr_1.48                                        
##  [97] tzdb_0.4.0                                        
##  [98] rjson_0.2.23                                      
##  [99] nlme_3.1-166                                      
## [100] curl_5.2.3                                        
## [101] org.Hs.eg.db_3.20.0                               
## [102] bumphunter_1.49.0                                 
## [103] GlobalOptions_0.1.2                               
## [104] cachem_1.1.0                                      
## [105] stringr_1.5.1                                     
## [106] BiocVersion_3.21.1                                
## [107] parallel_4.5.0                                    
## [108] AnnotationDbi_1.69.0                              
## [109] restfulr_0.0.15                                   
## [110] GEOquery_2.75.0                                   
## [111] pillar_1.9.0                                      
## [112] grid_4.5.0                                        
## [113] reshape_0.8.9                                     
## [114] vctrs_0.6.5                                       
## [115] mirbase.db_1.2.0                                  
## [116] promises_1.3.0                                    
## [117] cluster_2.1.6                                     
## [118] xtable_1.8-4                                      
## [119] evaluate_1.0.1                                    
## [120] tinytex_0.53                                      
## [121] magick_2.8.5                                      
## [122] readr_2.1.5                                       
## [123] GenomicFeatures_1.59.0                            
## [124] cli_3.6.3                                         
## [125] locfit_1.5-9.10                                   
## [126] compiler_4.5.0                                    
## [127] Rsamtools_2.23.0                                  
## [128] rlang_1.1.4                                       
## [129] crayon_1.5.3                                      
## [130] rngtools_1.5.2                                    
## [131] nor1mix_1.3-3                                     
## [132] mclust_6.1.1                                      
## [133] ps_1.8.1                                          
## [134] plyr_1.8.9                                        
## [135] stringi_1.8.4                                     
## [136] BiocParallel_1.41.0                               
## [137] Biostrings_2.75.0                                 
## [138] Matrix_1.7-1                                      
## [139] ExperimentHub_2.15.0                              
## [140] hms_1.1.3                                         
## [141] sparseMatrixStats_1.19.0                          
## [142] bit64_4.5.2                                       
## [143] Rhdf5lib_1.29.0                                   
## [144] KEGGREST_1.47.0                                   
## [145] statmod_1.5.0                                     
## [146] highr_0.11                                        
## [147] memoise_2.0.1                                     
## [148] bslib_0.8.0                                       
## [149] bit_4.5.0