#' @rdname cleanMetadata

#' @details

#'    GSE31312:\cr

#'    This function makes the samples GSM776068, GSM776149, and GSM776462 to be

#'    left out of the downstream preprocessing due to bad array quality.

#' @export

cleanMetadata.GSE31312 <- function(meta_data) {

  message("Cleaning GSE31312 (IDRC)!")

  # Generic clean

  suppressMessages(meta_data <- cleanMetadata.data.frame(meta_data))

  # Added factor describing the batches and CEL files

  exclude <- c("GSM776068", "GSM776149", "GSM776462")

  meta_data$Batch <-

    factor(ifelse(rownames(meta_data) %in% exclude, NA, "Batch1"))

  meta_data$CEL <- rownames(meta_data)

  meta_data$GSM <- as.character(meta_data$geo_accession)

  return(meta_data)

}

# meta_data$Array.Data.File <-

#   as.character(

#     meta_data_old[as.character(meta_data$GEO.Depository..31312),

#                      "Array.Data.File" ])

#

# meta_data <- meta_data[!is.na(meta_data$Array.Data.File),]

#

# meta_data$WrightClass2 <-

#   meta_data$GEP.Classification

# meta_data$WrightClass <- as.character(meta_data$WrightClass2)

# meta_data$WrightClass <-

#   as.factor(gsub("UC", "Unclassified", meta_data$WrightClass))

#

#   # Creating survival objects

#

#   meta_data$OS  <- meta_data$OS  / 12

#   meta_data$PFS <- meta_data$PFS / 12

#

# meta_data$OScensor <- ifelse(meta_data$OScensor == 1, 0, 1)

# meta_data$PFScensor <- ifelse(meta_data$PFScensor == 1, 0, 1)

#

# meta_data$OS<- Surv(as.numeric(meta_data$OS), meta_data$OScensor)

#

# os5  <- ifelse(meta_data$OS[,1] > 5, 5, meta_data$OS[,1])

# ios5 <- pmin(ifelse(meta_data$OS[,1] > 5, 0, 1), meta_data$OS[,2])

#

# meta_data$OS5 <- Surv(as.numeric(os5), ios5)

#

# meta_data$PFS<- Surv(as.numeric(meta_data$PFS), meta_data$PFScensor)

#

# PFS5  <- ifelse(meta_data$PFS[,1] > 5, 5, meta_data$PFS[,1])

# iPFS5 <- pmin(ifelse(meta_data$PFS[,1] > 5, 0, 1), meta_data$PFS[,2])

#

# meta_data$PFS5 <- Surv(as.numeric(PFS5), iPFS5)

# @

#

#   <<>>=

#   meta_data$ipi.hl <- NA

# meta_data$ipi.hl[meta_data$IPI.score %in% c(0,1)] <- "0-1"

# meta_data$ipi.hl[meta_data$IPI.score %in% c(2,3)] <- "2-3"

# meta_data$ipi.hl[meta_data$IPI.score %in% c(4,5)] <- "4-5"

# meta_data$ipi.hl <- as.factor(meta_data$ipi.hl)

# table(meta_data$ipi.hl, meta_data$IPI.score)

#

# meta_data$age <- meta_data$Age