#' use curatedTCGAData and Brennan metadata to build an annotated GBM MAE

#' @import curatedTCGAData

#' @importFrom S4Vectors DataFrame

#' @import BiocFileCache

#' @param cache if not NULL, location of BiocFileCache instance

#' @note Will cache the created SummarizedExperiment instance unless told not to.

#' `rname` is `"brenn_gbm_se"`.

#' @examples

#' gbmse = build_gbm_se()

#'

#' # check effect of MGMT methylation on survival

#'

#' xm = gbmse[, gbmse$mgmt_status !="" & gbmse$vital_status !=""]

#' library(survival)

#' ss = Surv(xm$os_days, 1*(xm$vital_status=="DECEASED"))

#' plot(survfit(ss~xm$mgmt_status), lty=1:2)

#' legend(1100, .95, lty=c(1,2), legend=c("MGMT methylated", "unmethylated"))

#' title("Time-on-study/vital status for 123 GBM patients analyzed in Brennan et al. PMID 24120142")

#' survdiff(ss~xm$mgmt_status)

#' @export

build_gbm_se = function(cache=BiocFileCache::BiocFileCache()) {

 if (!is.null(cache)) {

   chk = bfcquery(cache, "brenn_gbm_se")

   if (nrow(chk)>0) return(readRDS(chk$rpath))

   }

 mae = curatedTCGAData("GBM", assay="RNAseq2GeneNorm", dry.run=FALSE, version="2.0.1")

 se = experiments(mae)[[1]]

 colnames(se) = substr(colnames(se), 1, 12)

 br = get_brennan_clin()

 okids = intersect(colnames(se), rownames(br))

 br = br[okids,]

 se = se[,okids]

 colData(se) = DataFrame(br)

 assayNames(se) = "RNAseq2GeneNorm"

 if (is.null(cache)) return(se)

 tf = tempfile()

 saveRDS(se, tf)

 bfcadd(cache, rname="brenn_gbm_se", fpath=tf, action="copy")

 readRDS(bfcquery(cache, "brenn_gbm_se")$rpath)

}