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#' Generates mx_dataset
#'
#' Takes in data from data.frame of cell-level multiplexed data to create a mx_dataset S3 object.
#'
#' @param data multiplexed data to normalize. Data assumed to be a data.frame with cell-level data.
#' @param slide_id String slide identifier of input `data`. This must be a column in the `data` data.frame.
#' @param image_id String image identifier of input `data`. This must be a column in the `data` data.frame.
#' @param marker_cols vector of column name(s) in `data` corresponding to marker values.
#' @param metadata_cols other identifiers of the input `data` (default=NULL). This must be a vector of column name(s) in the `data` data.frame.
#'
#' @return data.frame object in the mx_dataset format with attribute for input type
#' @export
#'
#' @examples
#' mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
#' c("marker1_vals","marker2_vals","marker3_vals"),
#' c("metadata1_vals"))
mx_dataset = function(data,
slide_id,
image_id,
marker_cols,
metadata_cols = NULL){
## trim `data` to remove any columns not given by parameters
data = data.frame(data)
data = trim_dataset(data,slide_id,image_id,marker_cols,metadata_cols)
## use `data` as base of S3 object
## add attributes for slide_id and image_id (column name),
## incl. column names of markers, column names of metadata
mx_obj = new_mx_dataset(data,
slide_id = slide_id,
image_id = image_id,
marker_cols = marker_cols,
metadata_cols = metadata_cols)
## validate object
mx_obj = validate_mx_dataset(mx_obj)
mx_obj
}
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