#' An S4 class to represent co-expressed gene
#'
#' @slot mat Differentially expressed gene expression profilings. Either a
#' numeric matrix, a data.frame, or an ExpressionSet object. Data frames must
#' contain all numeric columns. In all cases, the rows are the items to be
#' clustered (e.g., genes), and the columns are the samples.
#' @slot clusterObjs a list contains clustering results.
#' @slot Distmat the distance matrix.
#' @slot clMethods clustering method.
#' @slot labels the label of genes
#' @slot nClust A numeric vector giving the numbers of clusters to be
#' evaluated. e.g., 2:6 would evaluate the number of clusters ranging from
#' 2 to 6.
#' @slot metric the distance measure to be used. It must be one of
#' "euclidean","maximum", "manhattan", "canberra", "binary",
#' "pearson", "abspearson", "correlation", "abscorrelation",
#' "spearman" or "kendall". Any unambiguous substring can be
#' given. In detail, please reference the parameter method in
#' amap::Dist. Some of the cluster methods could use only part
#' of the metric. Please reference the manual of cogena.
#' @slot method For hierarchical clustering (hclust and agnes), the
#' agglomeration method used. The default is "complete". Available
#' choices are "ward", "single", "complete", and "average".
#' @slot ncore the number of cores used.
#' @slot call the called function
#' @rdname genecl_class
#' @exportClass genecl
#' @import class
setClass("genecl", slots=list(mat="matrix",
clusterObjs="list",
Distmat="dist",
clMethods="character",
labels="character",
nClust="numeric",
metric="character",
method="character",
ncore="numeric",
call="call"))
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