R/hetset_class.R

In ZytoHMGU/hetset: Identification of Heterogeneous Subsets in Data

# construct hetset object from matrix or data.frame ---------------------------
hetset <- function(D,S = NULL){
  if(!class(D) %in% c("data.frame","matrix","SummarizedExperiment")){
    stop("class of data should be one of data.frame, matrix or SE")
  }
  if(!class(S) %in% c("data.frame","matrix","logical","NULL")){
    stop("class of sample information should be data.frame, matrix or logical")
  }
  if (class(D) != "SummarizedExperiment"){
    if (is.null(S)){
      dec.needed <- ceiling(log10(1+ncol(D)))
      ID.all <- 10^dec.needed
      ID.needed <- paste0("ID",substr(as.character((ID.all+1):(ID.all+ncol(D))),
                                      start = 2,stop = dec.needed+1))
      S <- data.frame("ID" = as.factor(ID.needed))

      if (is.null(colnames(D))){
        colnames(D) <- as.factor(ID.needed)
      }
      if (is.null(rownames(D))){
        rownames(D) <- paste0("F",1:nrow(D))
      }
    }
    sample_names <- colnames(D)
    if(class(S) %in% c("data.frame","matrix")){
      colData <- S
      colData$prt <- NA
      colData$col.names <- sample_names
    } else {
      colData <- DataFrame("prt" = NA,col.names = sample_names)
    }

    feature_names <- rownames(D)
    rowData <- DataFrame(row.names = feature_names)

    H <- SummarizedExperiment(assays = SimpleList("X" = as.matrix(D)),
                              rowData = rowData,colData = colData)
  } else {
    H <- D
  }
  H@metadata$slf <- NA
  H@metadata$prm.full <- list("mean" = NA,"cov" = NA)
  H@metadata$prm.A <- list("mean" = NA,"cov" = NA)
  H@metadata$prm.B <- list("mean" = NA,"cov" = NA)
  H@metadata$prp.A <- 0.5
  H@metadata$color_type <- "raw"
  H$prt <- NA
  H
}