curatedPCaData: Curated Prostate Cancer Data

#' Template for curatedPCaData clinical fields
#'
#' This data frame contains the clinical fields which were
#' the aim for extraction when gathering metadata for each dataset.
#' Variable types, ranges, and description is reported.
#'
#' @return A data.frame of the PCa template
#'
#' @docType data
#' @examples
#' data(template_prad)
#' head(template_prad)
"template_prad"

#' Abida et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex), copy
#' number alteration (cna), mutations (mut) and immune cell estimates for Abida 
#' et al.
#'
#' @format A MAE object spanning 444 castrate resistant prostate cancer samples.
#' \describe{
#'     \item{cna.gistic}{matrix with 20291 rows and 444 columns, from GISTIC 
#'         discretized copy number alteration calls.}
#'     \item{gex.relz}{matrix with 18971 rows and 266 columns, from z-score 
#'         normalized expression in relative to paired normals.}
#'     \item{mut}{RaggedExperiment with 63184 rows and 444 columns, mutation 
#'         data from cbioportal}
#'     \item{cibersort}{matrix with 22 rows and 261 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 261 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 261 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 261 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 261 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 261 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{mmatrix with 11 rows and 261 columns, of mcp-counter based 
#'         deconvolution data}
#'
#' }
#' @details the clinical data refers to a sample of 444 tumors collected in 429 
#' patients. The tissue was collected primarily at metastatic sites rather than 
#' from the prostate.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/31061129/}{PubMed})
#' @source  \url{https://www.cbioportal.org/study/summary?id=prad_su2c_2019}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_abida
#' @examples
#' mae_abida <- getPCa('abida')
#' @name curatedPCaDatasets_abida
#' @keywords datasets
NULL

#' Baca et al. MAE-object
#'
#' MultiAssayExperiment object containing copy number alteration (cna) and 
#' mutations for Baca et al.
#'
#' @format A MAE object spanning 56 prostate cancer samples.
#' \describe{
#'     \item{cna.gistic}{matrix with 20124 rows and 56 columns, from GISTIC 
#'         discretized copy number alteration calls.}
#'     \item{mut}{RaggedExperiment with 2734 rows and 57 columns, mutation data 
#'         from cbioportal}
#' }
#' @source \url{https://www.cbioportal.org/study/summary?id=prad_broad_2013}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_baca
#' @docType data
#' @examples
#' mae_baca <- getPCa('baca')
#' @name curatedPCaDatasets_baca
#' @keywords datasets
NULL

#' Barbieri et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex), copy
#' number alteration (cna), mutations (mut) and immune cell estimates for 
#' Barbieri et al.
#'
#' @format A MAE object spanning prostate adenocarcinomas from Barbieri et. al
#' \describe{
#'     \item{cna.gistic}{matrix with 20844 rows and 109 columns, from GISTIC 
#'         discretized copy number alteration calls}
#'     \item{gex.relz}{matrix with 17917 rows and 31 columns, from z-score 
#'         normalized expression in relative to paired normals.}
#'     \item{mut}{RaggedExperiment with 5737 rows and 112 columns, mutation 
#'         data from cbioportal}
#'     \item{cibersort}{matrix with 22 rows and 31 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 31 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 31 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 31 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 31 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 31 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 31 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details The data comprises of primary localised prostate tumors from two 
#' cohorts, the Weill Cornell Medical College (WCMC; New York, NY), and the 
#' Uropath (Perth, Australia), which commercially provides banked urological 
#' tissues. None of the samples comes from patients who had received prior 
#' treatment for prostate cancer.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/22610119/}{PubMed})
#' @source \url{https://www.cbioportal.org/study/summary?id=prad_broad}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_barbieri
#' @examples
#' mae_barbieri <- getPCa('barbieri')
#' @name curatedPCaDatasets_barbieri
#' @keywords datasets
NULL

#' Barwick et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates for Barwick et al.
#'
#' @format A MAE object spanning 146 prostate cancer samples.
#' \describe{
#'     \item{gex.logq}{matrix with 482 rows and 146 columns, for the 
#'         log-quantile normalized gene expression data}
#'     \item{cibersort}{matrix with 22 rows and 139 columns, of cibersort based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 139 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 139 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 3 rows and 139 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 4 rows and 139 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details Barwick et al. uses an older customized DASL array; therefore its 
#' gene coverage is lower, and many downstream methods fail due to lack of gene 
#' overlap.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/22610119/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18655}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_barwick
#' @examples
#' mae_barwick <- getPCa('barwick')
#' @name curatedPCaDatasets_barwick
#' @keywords datasets
NULL

#' Chandran et al., Yu et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates for Chandran et al., Yu et al.
#'
#' @format An MAE object spanning 171 men with prostate cancer
#' \describe{
#'     \item{gex.rma}{matrix with 9007 rows and 171 columns, for the gene 
#'         expression data}
#'     \item{cibersort}{matrix with 22 rows and 171 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 171 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 171 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 171 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 171 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 171 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 171 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/17430594/}{PubMed}) 
#' (\href{https://pubmed.ncbi.nlm.nih.gov/15254046/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6919}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_chandran
#' @examples
#' mae_chandran <- getPCa('chandran')
#' @name curatedPCaDatasets_chandran
#' @keywords datasets
NULL

#' Friedrich et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates for Friedrich et al.
#'
#' @format An MAE object spanning 255 men with prostate cancer
#' \describe{
#'     \item{gex.logq}{matrix with 23097 rows and 255 columns, for the 
#'         log-quantile normalized gene expression data}
#'     \item{cibersort}{matrix with 22 rows and 255 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 255 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 255 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 255 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 255 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 255 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 255 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details The data comprises of 255 samples, with 164 primary tumors samples, 
#' 52 adjacent normal samples, and 39 benign prostate hyperplasia samples.  
#' This dataset includes in its totality the 164 samples analysed in Kreutz 
#' et al. 2020.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/32365858/}{PubMed}) 
#' (\href{https://pubmed.ncbi.nlm.nih.gov/32631745/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134051}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_friedrich
#' @examples
#' mae_friedrich <- getPCa('friedrich')
#' @name curatedPCaDatasets_friedrich
#' @keywords datasets
NULL

#' Hieronymus et al. MAE-object
#'
#' MultiAssayExperiment object containing copy number alteration (cna) for 
#' Hieronymus et al.
#'
#' @format A MAE object spanning 104 tumor samples
#' \describe{
#'     \item{cna.gistic}{matrix with 18026 rows and 104 columns, from GISTIC 
#'         discretized copy number alteration calls}
#' }
#' @details The data comprises of 104 samples, for which are available clinical 
#' data and Copy Number Alteration, but no gene expression data -- thus no 
#' deconvolution results are available.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/25024180/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54691}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_hieronymus
#' @examples
#' mae_hieronymus <- getPCa('hieronymus')
#' @name curatedPCaDatasets_hieronymus
#' @keywords datasets
NULL

#' ICGC CA MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates for the ICGC CA (Canadian) cohort.
#'
#' @format An MAE object spanning 213 men with prostate cancer
#' \describe{
#'     \item{gex.rma}{matrix with 17208 rows and 213 columns, of gene expression
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 213 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 213 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 213 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 213 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 213 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 213 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 213 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details The data refers to samples from the ICGC Canadian Prostate Cancer 
#' Genome Network (ICGC-PRAD-CA)
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/28068672/}{PubMed})
#' @source \url{https://dcc.icgc.org/projects/PRAD-CA}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_icgcca
#' @examples
#' mae_icgcca <- getPCa('icgcca')
#' @name curatedPCaDatasets_icgcca
#' @keywords datasets
NULL

#' International Genomics Consortium (IGC) MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates from IGC's Expression Project for Oncology (expO) with a subset for
#' prostate cancer
#'
#' @format An MAE object spanning 83 men with prostate cancer
#' \describe{
#'     \item{gex.rma}{matrix with 12798 rows and 83 columns, of gene expression 
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 83 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 83 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 83 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 83 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 83 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 83 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 83 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#'
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse2109}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_igc
#' @examples
#' mae_igc <- getPCa('igc')
#' @name curatedPCaDatasets_igc
#' @keywords datasets
NULL

#' Kim et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates from Kim et al.
#'
#' @format An MAE object spanning 266 men with prostate cancer
#' \describe{
#'     \item{gex.rma}{matrix with 17638 rows and 266 columns, of gene expression
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 266 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 266 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 266 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 266 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 266 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 2 rows and 266 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 266 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details The dataset consists of 266 NCCN very low/low or 
#' favorable-intermediate risk PCa patients
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/30542054/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119616}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#* omics.
#'
#' @rdname curatedPCaDatasets_kim
#' @examples
#' mae_kim <- getPCa('kim')
#' @name curatedPCaDatasets_kim
#' @keywords datasets
NULL

#' Kunderfranco et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates from Kunderfranco et al.
#'
#' @format An MAE object spanning 67 samples of normal prostate samples and 
#' prostate cancer samples
#' \describe{
#'     \item{gex.logr}{matrix with 16546 rows and 67 columns, of gene expression
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 67 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 67 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 67 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 67 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 67 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 2 rows and 67 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 67 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details The data contains 14 disease free benign prostate hyperplasia 
#' samples and 53 prostate cancer samples.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/20479932/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14206}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_kunderfranco
#' @examples
#' mae_kunderfranco <- getPCa('kunderfranco')
#' @name curatedPCaDatasets_kunderfranco
#' @keywords datasets
NULL

#' Ren et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex), mutations and 
#' immune cell estimates from Ren et al.
#'
#' @format An MAE object spanning 65 men with prostate cancer
#' \describe{
#'     \item{gex.relz}{matrix with 21046 rows and 65 columns, of gene expression
#'         data}
#'     \item{mut}{RaggedExperiment with 50625 rows and 65 columns, of mutation 
#'         data from cbioportal}
#'     \item{cibersort}{matrix with 22 rows and 65 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 65 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 65 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 65 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 65 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 65 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 65 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details The data contains 14 disease free benign prostate hyperplasia 
#' samples and 53 prostate cancer samples.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/28927585/}{PubMed})
#' @source \url{https://www.cbioportal.org/study/summary?id=prad_eururol_2017}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_ren
#' @examples
#' mae_ren <- getPCa('ren')
#' @name curatedPCaDatasets_ren
#' @keywords datasets
NULL

#' Sun et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates from Sun et al.
#'
#' @format An MAE object spanning 79 men with prostate cancer
#' \describe{
#'     \item{gex.rma}{matrix with 12798 rows and 79 columns, of gene expression 
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 79 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 79 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 79 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 79 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 79 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 79 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 79 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#'
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/19343730/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25136}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_sun
#' @examples
#' mae_sun <- getPCa('sun')
#' @name curatedPCaDatasets_sun
#' @keywords datasets
NULL

#' Taylor et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex), copy
#' number alteration (cna), mutations and immune cell estimates from Taylor et 
#' al.
#'
#' @format An MAE object spanning 218 men with prostate cancer
#' \describe{
#'     \item{cna.gistic}{matrix with 17832 rows and 194 columns, of gistic 
#'         values for copy number alteration data}
#'     \item{cna.logr}{matrix with 18062 rows and 218 columns, of log-ratios for
#'         copy number alteration data}
#'     \item{gex.rma}{matrix with 17410 rows and 179 columns, of gene expression
#'         data}
#'     \item{mut}{RaggedExperiment with 90 rows and 43 columns, of mutation 
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 179 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 179 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 179 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 179 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 179 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 179 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 179 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#'
#' @details Note that there is lack of overlap between the omics provided for 
#' each sample.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/20579941/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21035}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_taylor
#' @examples
#' mae_taylor <- getPCa('taylor')
#' @name curatedPCaDatasets_taylor
#' @keywords datasets
NULL

#' TCGA MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex), copy
#' number alteration (cna), mutations and immune cell estimates from GDC TCGA.
#'
#' @format An MAE object spanning 369 men with prostate cancer
#' \describe{
#'     \item{cna.gistic}{matrix with 23151 rows and 492 columns, from GISTIC 
#'         discretized copy number alteration calls}
#'     \item{gex.rsem.log}{matrix with 19658 rows and 461 columns, of gene 
#'         expression data}
#'     \item{mut}{RaggedExperiment with 30897 rows and 495 columns, of mutation 
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 550 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 461 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 461 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 461 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{mcp}{matrix with 11 rows and 461 columns, of mcp-counter based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 461 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 416 columns, of risk scores and AR 
#'         scores}
#' }
#'
#' @details TCGA data was obtained from the latest GDC's XenaBrowser release.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/26544944/}{PubMed})
#' @source \url{https://xenabrowser.net/datapages/?cohort=GDC\%20TCGA\%20Prostate\%20Cancer\%20(PRAD)}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_tcga
#' @examples
#' mae_tcga <- getPCa('tcga')
#' @name curatedPCaDatasets_tcga
#' @keywords datasets
NULL

#' True et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates from True et al.
#' @format An MAE object spanning 32 men with prostate cancer
#' \describe{
#'     \item{gex.logr}{matrix with 3615 rows and 32 columns, of gene expression 
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 32 columns, of cibersort based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 32 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 32 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{mcp}{matrix with 7 rows and 32 columns, of mcp-counter based 
#'         deconvolution data}
#'     \item{scores}{matrix with 2 rows and 32 columns, of risk scores and AR 
#'         scores}
#'     \item{estimate}{data.frame with 4 rows and 32 columns, of cell types 
#'         based on ESTIMATE method}
#' }
#' @details The expression data for this dataset was produced by an older two 
#' colour chip that provides the relative expression levels for between tumor 
#' and normal.  Due to the chip type, there was a lack of gene overlap with 
#' some of the downstream methods, thus some downstream methods are missing.
# @references (\href{https://pubmed.ncbi.nlm.nih.gov/16829574/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5132}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_true
#' @examples
#' mae_true <- getPCa('true')
#' @name curatedPCaDatasets_true
#' @keywords datasets
NULL

#' Wallace et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates from Wallace et al.
#' @format An MAE object spanning 83 men with prostate cancer
#' \describe{
#'     \item{gex.rma}{matrix with 12783 rows and 89 columns, of gene expression 
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 83 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 89 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 89 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 89 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 89 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 89 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 89 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#'
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/18245496/}{PubMed})
#'
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6956}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_wallace
#' @examples
#' mae_wallace <- getPCa('wallace')
#' @name curatedPCaDatasets_wallace
#' @keywords datasets
NULL

#' Wang et al. MAE-object
#'
#' MultiAssayExperiment object containing gene expression (gex) and immune cell 
#' estimates from Wang et al.
#' @format An MAE object spanning 148 men with prostate cancer
#' \describe{
#'     \item{gex.rma}{matrix with 12798 rows and 148 columns, of gene expression
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 148 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 148 columns, of xcell based 
#'         deconvolution data}
#'     \item{epic}{matrix with 8 rows and 148 columns, of epic based 
#'         deconvolution data}
#'     \item{quantiseq}{matrix with 11 rows and 148 columns, of quantiseq based 
#'         deconvolution data}
#'     \item{estimate}{data.frame with 4 rows and 148 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 148 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 148 columns, of mcp-counter based 
#'         deconvolution data}
#' }
#' @details 148 prostate samples, with various amounts of tumor, stroma, BPH and
#' atrophic gland, were used for this study.
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/20663908/}{PubMed})
#'
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8218}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_wang
#' @examples
#' mae_wang <- getPCa('wang')
#' @name curatedPCaDatasets_wang
#' @keywords datasets
NULL

#' Weiner et al. MAE-object
#'
#' A MultiAssayExperiment object containing gene expression (gex) and immune 
#' cell estimates for Weiner et al.
#' @format An MAE spanning 838 prostate cancer samples of two cohorts
#' \describe{
#'     \item{gex.rma}{matrix of 17410 rows and 838 columns of gene expression 
#'         data}
#'     \item{cibersort}{matrix with 22 rows and 838 columns, of cibersort based 
#'         deconvolution data}
#'     \item{xcell}{matrix with 39 rows and 838 columns, the xcell deconvolution
#'         of the expression data}
#'     \item{epic}{matrix with 8 rows and 838 columns, the epic deconvolution of
#'         the expression data}
#'     \item{quantiseq}{matrix with 11 rows and 838 columns, the quantiseq 
#'         deconvolution of the expression data}
#'     \item{estimate}{data.frame with 4 rows and 838 columns, of cell types 
#'         based on ESTIMATE method}
#'     \item{scores}{matrix with 4 rows and 838 columns, of risk scores and AR 
#'         scores}
#'     \item{mcp}{matrix with 11 rows and 838 columns, the mcp-counter 
#'         deconvolution of the expression data}
#' }
#'
#' @references (\href{https://pubmed.ncbi.nlm.nih.gov/33568675/}{PubMed})
#' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157548}
#' @return A MultiAssayExperiment corresponding to the study and its available 
#' omics.
#'
#' @rdname curatedPCaDatasets_weiner
#' @examples
#' mae_weiner <- getPCa('weiner')
#' @name curatedPCaDatasets_weiner
#' @keywords datasets
NULL