R/TCGAWorkflowExampleData.R

In BioinformaticsFMRP/TCGAWorkflowData: Data for TCGA Workflow

#' A SummarizedExperiment containing
#' TCGA data: DNA methylation platform 450K chromossome 9
#' for 10 LGG samples and 10 GBM samples
#' @docType data
#' @keywords internal
#' @name commonCNV
#' @format A data frame with 7454 rows and 6 columns
#' @examples
#' data("CNV.hg19.bypos.111213")
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#' A SummarizedExperiment containing
#' TCGA data: DNA methylation platform 450K chromossome 9
#' for 10 LGG samples and 10 GBM samples
#' @docType data
#' @keywords internal
#' @name met
#' @import SummarizedExperiment
#' @format A SumarrizedExperiment with 9861 rows and 20 samples
#' @examples
#' data("elmerExample")
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#' A GRanges object with gene information (hg19)
#' @docType data
#' @keywords internal
#' @name genes_GR
#' @format A GRanges object
#' @examples
#' data("genes_GR")
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#' A data frame object with gene information (hg19)
#' @docType data
#' @keywords internal
#' @name genes
#' @format A dataframe object
#' @examples
#' data("genes_GR")
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#' A gene expression matrix for 10 GBM and 10 LGG samples prepared for
#' the creation of an ELMER object.
#' @docType data
#' @keywords internal
#' @name exp
#' @format A matrix with 21022 rows and 20 samples
#' @examples
#' data("elmerExample")
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#' Identifiers for the 10 LGG samples in the ELMER objects
#' @docType data
#' @keywords internal
#' @name lgg.samples
#' @format A vector of 10 barcodes
#' @examples
#' data("elmerExample")
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#' Identifiers for the 10 GBM samples in the ELMER objects
#' @docType data
#' @keywords internal
#' @name gbm.samples
#' @format A vector of 10 barcodes
#' @examples
#' data("elmerExample")
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#' A gene expression matrix wih 20 LGG samples
#' @docType data
#' @keywords internal
#' @name lgg.exp
#' @format Gene expression: A SummariedExperiment object
#' with 21022 rows and 20 columns
#' @examples
#' data("LGGIllumina_HiSeq")
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#' A gene expression matrix wih 20 GBM samples
#' @docType data
#' @keywords internal
#' @name gbm.exp
#' @format Gene expression: A SummariedExperiment object
#'  with 21022 rows and 20 columns
#' @examples
#' data("GBMIllumina_HiSeq")
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#' CNV data for 20 TCGA-GBM samples
#' @docType data
#' @keywords internal
#' @name cnvMatrix
#' @format A matrix with 3252 rows and 6 columns
#' @examples
#' data("GBMnocnvhg19")
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#' Merged LGG and GBM GDC maf files for  Somatic Mutation Calling Workflow mutect2
#' @docType data
#' @keywords internal
#' @name mut
#' @format A matrix with 101942 rows and 114 columns
#' @examples
#' data("mafMutect2LGGGBM")
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#'  LGG  GDC maf files for  Somatic Mutation Calling Workflow mutect2
#' @docType data
#' @keywords internal
#' @name LGGmut
#' @format A matrix with 29840 rows and 114 columns
#' @examples
#' data("mafMutect2LGGGBM")
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#'  GBM GDC maf files for  Somatic Mutation Calling Workflow mutect2
#' @docType data
#' @keywords internal
#' @name GBMmut
#' @format A matrix with 72102 rows and 114 columns
#' @examples
#' data("mafMutect2LGGGBM")
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#'  histone marks specific for brain tissue from the Roadmap database.
#' @docType data
#' @keywords internal
#' @name histone.marks
#' @format A matrix with 72102 rows and 114 columns
#' @examples
#' data("histoneMarks")
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#' Probes meta file from broadinstitute website for Copy Number
#' Variation Analysis (CNV) analysis
#' From: ftp://ftp.broadinstitute.org/pub/GISTIC2.0/hg19_support/genome.info.6.0_hg19.na31_minus_frequent_nan_probes_sorted_2.1.txt
#' @docType data
#' @keywords internal
#' @name markersMatrix
#' @format A matrix with 1831228 rows and 3 columns
#' @examples
#' data("markersMatrix")
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#' A subset of GBM GISTIC2 results, which is used to identify genes
#' targeted by somatic copy-number alterations (SCNAs)
#' From: GDAC firehose, downloaded with RTCGAtoolbox
#' @docType data
#' @keywords internal
#' @name gistic.allbygene
#' @format A matrix with 24776 rows and 580 columns
#' @examples
#' data("GBMGistic")
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#' A subset of GBM GISTIC2 results, which is used to identify genes
#' targeted by somatic copy-number alterations (SCNAs)
#' From: GDAC firehose, downloaded with RTCGAtoolbox
#' @docType data
#' @keywords internal
#' @name gistic.thresholedbygene
#' @format A matrix with 24776 rows and 580 columns
#' @examples
#' data("GBMGistic")
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#' Biogrid information
#' @docType data
#' @keywords internal
#' @name tmp.biogrid
#' @format Two matrices with 24776 rows and 580 columns
#' @examples
#' data("biogrid")
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#' @title Data for TCGA Workflow
#' @description
#' This experimental data package has the data necessary to follow the
#' TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages.
#' It contains the following files:
#' \itemize{
#'   \item GBMnocnvhg19: segmented CNV from SNP array (Affymetrix Genome-Wide
#'   Human SNP Array 6.0) for 20  Glioblastoma multiforme (GBM)  samples.
#'   \item GBMIllumina_HiSeq: a matrix with  raw expression signal for
#'   expression of a gene for 20 GBM (Glioblastoma multiforme) samples
#'   \item LGGIllumina_HiSeq: a matrix with  raw expression signal
#'   for expression of a gene for 20 LGG (Lower grade glioma) samples
#'   \item met20SamplesGBMLGGchr9:  DNA methylation matrix from Infinium
#'    HumanMethylation450 platform
#'     for 10 LGG (Lower grade glioma) and 10 GBM (Glioblastoma multiforme).
#'     It has only probes in chromossome 9  in order to make the example
#'     of the workflow faster
#'   \item elmerExample: Contains a DNA methylation matrix
#'     (only probes in chromossome 9) and a gene expression matrix for
#'     10  LGG (Lower grade glioma) and 10 GBM (Glioblastoma multiforme) in the required format
#'     for to execute the R/Bioconductor ELMER package analysis and a vector
#'     identifying which sample belongs to each tumor type.
#'    \item GBMGistic: a subset of GBM GISTIC results which is used to
#'    identify genes targeted by somatic copy-number  alterations (SCNAs)
#'    \item biogrid: biogrid information
#'   \item mafMutect2LGGGBM: Mutation annotation files for LGG
#'     (Lower grade glioma) and GBM (Glioblastoma multiforme)
#'     samples merged into a single matrix. The GDC Somatic Mutation Calling
#'     Workflow mutect2 was used to create this MAF files.
#'   \item markersMatrix: Probes meta file from broadinstitute website for
#'   Copy Number Variation Analysis (CNV) analysis
#'   \item  histoneMarks: histone marks specific for brain tissue using
#'   from Roadmap database.
#'   \item  genes_GR: A GRanges Object and a dataframe  with gene information
#'   (hg19) downloaded from ENSEMBLE database using
#'   biomart via TCGAbiolinks
#'}
#'     For more information how to create these objects please read the vignette of this package with the
#'     follwing command: \code{browseVignettes("TCGAWorkflowData")}
#'
#' @docType package
#' @name TCGAWorkflowData
#' @keywords utilities
#' @examples
#' data("elmerExample")
#' data("GBMnocnvhg19")
#' data("GBMIllumina_HiSeq")
#' data("LGGIllumina_HiSeq")
#' data("met20SamplesGBMLGGchr9")
#' data("mafMutect2LGGGBM")
#' data("markersMatrix")
#' data("biogrid")
#' data("GBMGistic")
#' data("histoneMarks")
#' data("genes_GR")
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