A MultiAssayExperiment object providing a reduced version of
the TCGA ACC dataset for all 92 patients. RNA-seq, copy number, and somatic
mutations are included only for genes whose proteins are included in the
reverse-phase protein array. The MicroRNA-seq dataset is also included,
with infrequently expressed microRNA removed. Clinical, pathological, and
subtype information are provided by colData(miniACC), and some
additional details are provided by metadata(miniACC).
Usage
data("miniACC")Format
A MultiAssayExperiment with 5 experiments, providing:
- RNASeq2GeneNorm
RNA-seq count data: an
ExpressionSetwith 198 rows and 79 columns- gistict
Reccurent copy number lesions identified by GISTIC2: a
SummarizedExperimentwith 198 rows and 90 columns- RPPAArray
Reverse Phase Protein Array: an
ExpressionSetwith 33 rows and 46 columns. Rows are indexed by genes, but protein annotations are available fromfeatureData(miniACC[["RPPAArray"]]). The source of these annotations is noted inabstract(miniACC[["RPPAArray"]])- Mutations
Somatic mutations: a
matrixwith 223 rows and 90 columns. 1 for any kind of non-silent mutation, zero for silent (synonymous) or no mutation.- miRNASeqGene
microRNA sequencing: an
ExpressionSetwith 471 rows and 80 columns. Rows not having at least 5 counts in at least 5 samples were removed.
References
Zheng S et al.: Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma. Cancer Cell 2016, 29:723-736.
Author
Levi Waldron lwaldron.research@gmail.com
Examples
data("miniACC")
metadata(miniACC)
#> $title
#> [1] "Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma"
#>
#> $PMID
#> [1] "27165744"
#>
#> $sourceURL
#> [1] "http://s3.amazonaws.com/multiassayexperiments/accMAEO.rds"
#>
#> $RPPAfeatureDataURL
#> [1] "http://genomeportal.stanford.edu/pan-tcga/show_target_selection_file?filename=Allprotein.txt"
#>
#> $colDataExtrasURL
#> [1] "http://www.cell.com/cms/attachment/2062093088/2063584534/mmc3.xlsx"
#>
colnames(colData(miniACC))
#> [1] "patientID"
#> [2] "years_to_birth"
#> [3] "vital_status"
#> [4] "days_to_death"
#> [5] "days_to_last_followup"
#> [6] "tumor_tissue_site"
#> [7] "pathologic_stage"
#> [8] "pathology_T_stage"
#> [9] "pathology_N_stage"
#> [10] "gender"
#> [11] "date_of_initial_pathologic_diagnosis"
#> [12] "radiation_therapy"
#> [13] "histological_type"
#> [14] "residual_tumor"
#> [15] "number_of_lymph_nodes"
#> [16] "race"
#> [17] "ethnicity"
#> [18] "Histology"
#> [19] "C1A.C1B"
#> [20] "mRNA_K4"
#> [21] "MethyLevel"
#> [22] "miRNA.cluster"
#> [23] "SCNA.cluster"
#> [24] "protein.cluster"
#> [25] "COC"
#> [26] "OncoSign"
#> [27] "purity"
#> [28] "ploidy"
#> [29] "genome_doublings"
#> [30] "ADS"
table(miniACC$vital_status)
#>
#> 0 1
#> 58 34
longFormat(
miniACC["MAPK3", , ],
colDataCols = c("vital_status", "days_to_death")
)
#> harmonizing input:
#> removing 216 sampleMap rows not in names(experiments)
#> DataFrame with 169 rows and 7 columns
#> assay primary rowname colname
#> <character> <character> <character> <factor>
#> 1 RNASeq2GeneNorm TCGA-OR-A5J1 MAPK3 TCGA-OR-A5J1-01A-11R-A29S-07
#> 2 RNASeq2GeneNorm TCGA-OR-A5J2 MAPK3 TCGA-OR-A5J2-01A-11R-A29S-07
#> 3 RNASeq2GeneNorm TCGA-OR-A5J3 MAPK3 TCGA-OR-A5J3-01A-11R-A29S-07
#> 4 RNASeq2GeneNorm TCGA-OR-A5J5 MAPK3 TCGA-OR-A5J5-01A-11R-A29S-07
#> 5 RNASeq2GeneNorm TCGA-OR-A5J6 MAPK3 TCGA-OR-A5J6-01A-31R-A29S-07
#> ... ... ... ... ...
#> 165 gistict TCGA-PA-A5YG MAPK3 TCGA-PA-A5YG-01A-11D-A29H-01
#> 166 gistict TCGA-PK-A5H9 MAPK3 TCGA-PK-A5H9-01A-11D-A29H-01
#> 167 gistict TCGA-PK-A5HA MAPK3 TCGA-PK-A5HA-01A-11D-A29H-01
#> 168 gistict TCGA-PK-A5HB MAPK3 TCGA-PK-A5HB-01A-11D-A29H-01
#> 169 gistict TCGA-PK-A5HC MAPK3 TCGA-PK-A5HC-01A-11D-A309-01
#> value vital_status days_to_death
#> <numeric> <integer> <integer>
#> 1 946.681 1 1355
#> 2 1699.382 1 1677
#> 3 2347.243 0 NA
#> 4 1299.156 1 365
#> 5 2543.424 0 NA
#> ... ... ... ...
#> 165 1 0 NA
#> 166 0 0 NA
#> 167 0 0 NA
#> 168 0 0 NA
#> 169 1 0 NA
wideFormat(
miniACC["MAPK3", , ],
colDataCols = c("vital_status", "days_to_death")
)
#> harmonizing input:
#> removing 216 sampleMap rows not in names(experiments)
#> DataFrame with 92 rows and 5 columns
#> primary vital_status days_to_death RNASeq2GeneNorm_MAPK3 gistict_MAPK3
#> <character> <integer> <integer> <numeric> <numeric>
#> 1 TCGA-OR-A5J1 1 1355 946.681 0
#> 2 TCGA-OR-A5J2 1 1677 1699.382 0
#> 3 TCGA-OR-A5J3 0 NA 2347.243 1
#> 4 TCGA-OR-A5J4 1 423 NA 0
#> 5 TCGA-OR-A5J5 1 365 1299.156 1
#> ... ... ... ... ... ...
#> 88 TCGA-PK-A5H9 0 NA 1013.47 0
#> 89 TCGA-PK-A5HA 0 NA 2446.12 0
#> 90 TCGA-PK-A5HB 0 NA 1463.31 0
#> 91 TCGA-PK-A5HC 0 NA NA 1
#> 92 TCGA-P6-A5OG 1 383 1310.88 -1