miniACC {MultiAssayExperiment} | R Documentation |
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).
miniACC
A MultiAssayExperiment
with 5 experiments, providing:
RNA-seq count data: an ExpressionSet
with 198 rows and 79 columns
Reccurent copy number lesions identified by GISTIC2:
a SummarizedExperiment
with 198 rows and 90 columns
Reverse Phase Protein Array: an ExpressionSet
with 33 rows and 46 columns. Rows are indexed by genes,
but protein annotations are available from
featureData(miniACC[["RPPAArray"]])
. The source of these
annotations is noted in abstract(miniACC[["RPPAArray"]])
Somatic mutations: a matrix
with 223 rows and
90 columns. 1 for any kind of non-silent mutation, zero for silent
(synonymous) or no mutation.
microRNA sequencing: an ExpressionSet
with
471 rows and 80 columns. Rows not having at least 5 counts in at least
5 samples were removed.
Levi Waldron lwaldron.research@gmail.com
https://github.com/waldronlab/multiassayexperiment-tcga
Zheng S *et al.*: Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma. Cancer Cell 2016, 29:723-736.
miniACC metadata(miniACC) colnames(colData(miniACC)) table(miniACC$vital_status) longFormat(miniACC["MAPK3", , ], colDataCols = c("vital_status", "days_to_death")) wideFormat(miniACC["MAPK3", , ], colDataCols = c("vital_status", "days_to_death")) ## ## The following is the code used to create this mini dataset from the full ACC dataset. ## The full ACC MultiAssayExperiment was created by the pipeline at ## https://github.com/waldronlab/multiassayexperiment-tcga. ## Not run: ## See www.tinyurl.com/MAEOurls for more pre-built TCGA MultiAssayExperiment objects download.file("http://s3.amazonaws.com/multiassayexperiments/accMAEO.rds", destfile = "accMAEO.rds") library(MultiAssayExperiment) library(RaggedExperiment) #needed for RaggedExperiment objects by updateObject() library(Biobase) acc <- readRDS("accMAEO.rds") acc <- updateObject(acc) protmap <- read.csv(paste0("http://genomeportal.stanford.edu/", "pan-tcga/target_selection_send_data", "?filename=Allprotein.txt"), as.is = TRUE ) RPPAgenes <- Filter(function(x) x != "", protmap$Genes) RPPAgenes <- unlist(strsplit(RPPAgenes, ",")) RPPAgenes <- unique(RPPAgenes) miniACC <- acc[RPPAgenes, , c("RNASeq2GeneNorm", "gistict", "RPPAArray", "Mutations")] mut <- assay(miniACC[["Mutations"]], i = "Variant_Classification") mut <- ifelse(is.na(mut) | mut == "Silent", 0, 1) miniACC[["Mutations"]] <- mut colData(miniACC) <- colData(miniACC)[, c(1:17, 810:822)] rpparowData <- protmap[match(rownames(miniACC[["RPPAArray"]]), protmap$Genes),] rpparowData <- AnnotatedDataFrame(rpparowData) featureData(miniACC[["RPPAArray"]]) <- rpparowData md <- list( title = "Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma", PMID = "27165744", sourceURL = "http://s3.amazonaws.com/multiassayexperiments/accMAEO.rds", RPPAfeatureDataURL = paste0("http://genomeportal.stanford.edu/", "pan-tcga/show_target_selection_file", "?filename=Allprotein.txt"), colDataExtrasURL = "http://www.cell.com/cms/attachment/2062093088/2063584534/mmc3.xlsx" ) metadata(miniACC) <- md mirna <- acc[["miRNASeqGene"]] mirna <- mirna[rowSums(assay(mirna) >= 5) >= 5, ] experimentData(mirna)@abstract <- "Note: Rows not having at least 5 counts in at least 5 samples were removed." miniACC <- c(miniACC, list(miRNASeqGene = mirna), sampleMap = sampleMap(acc)[sampleMap(acc)$assay == "miRNASeqGene",]) miniACC[["RNASeq2GeneNorm"]] <- as(miniACC[["RNASeq2GeneNorm"]], "SummarizedExperiment") miniACC[["RPPAArray"]] <- as(miniACC[["RPPAArray"]], "SummarizedExperiment") miniACC[["miRNASeqGene"]] <- as(miniACC[["miRNASeqGene"]], "SummarizedExperiment") save(miniACC, file = "data/miniACC.RData", compress = "bzip2") ## End(Not run)