#' #' @title run_clustering
#' #' @description run_clustering
#' #' @author Xiaotao Shen
#' #' \email{shenxt1990@@163.com}
#' #' @param object tidymass-class object.
#' #' @return prcomp object.
#' #' @export
#' #' @examples
#' library(massdataset)
#' library(magrittr)
#' library(dplyr)
#' data("liver_aging_pos")
#'
#' qc_id <-
#' liver_aging_pos %>%
#' activate_mass_dataset(what = "sample_info") %>%
#' dplyr::filter(group == "QC") %>%
#' dplyr::pull(sample_id)
#' object <-
#' mutate_rsd(liver_aging_pos, according_to_samples = qc_id)
#'
#' ###only remain the features with rt > 100, mz > 150 and rsd < 30
#' object <-
#' object %>%
#' activate_mass_dataset(what = "variable_info") %>%
#' dplyr::filter(rt > 100) %>%
#' dplyr::filter(mz > 150) %>%
#' dplyr::filter(rsd < 30)
#'
#' ##only remain the week 24 samples
#' object <-
#' object %>%
#' activate_mass_dataset(what = "sample_info") %>%
#' dplyr::filter(group == "24W")
#'
#' dim(object)
#'
#' object <-
#' object %>%
#' `+`(1) %>%
#' log(10) %>%
#' scale_data(method = "auto")
#'
#'
#'
#'
#'
#'
#' run_clustering <-
#' function(object,
#' path = ".") {
#' dir.create(path, recursive = TRUE, showWarnings = FALSE)
#'
#' expression_data <-
#' object@expression_data
#'
#' time <- 1:ncol(expression_data)
#'
#' expression_data <- rbind(time, expression_data)
#'
#' row.names(expression_data)[1] <- "time"
#'
#' #save it to a temp file
#' dir.create("example")
#' tmp <- tempfile(tmpdir = "example")
#'
#' write.table(
#' expression_data,
#' file = tmp,
#' sep = '\t',
#' quote = F,
#' col.names = NA
#' )
#'
#' #read it back in as an expression set
#' data <- Mfuzz::table2eset(filename = tmp)
#' m1 <- Mfuzz::mestimate(eset = data)
#'
#' Dmin(
#' data,
#' m = m1,
#' crange = seq(2, 22, 1),
#' repeats = 3,
#' visu = TRUE
#' )
#'
#' cluster_number <- 5
#' c <- Mfuzz::mfuzz(eset = data, c = cluster_number, m = m1)
#'
#' centers <- c$centers
#' names(c$cluster) == rownames(c$membership)
#'
#' cluster_info <-
#' data.frame(
#' variable_id = names(c$cluster),
#' c$membership,
#' cluster = c$cluster,
#' stringsAsFactors = FALSE
#' ) %>%
#' dplyr::arrange(cluster)
#'
#' rownames(cluster_info) <- NULL
#' colnames(cluster_info) <-
#' colnames(cluster_info) %>%
#' stringr::str_replace(pattern = "^X", "membership_")
#' }
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