#' GSEAparams S4 class implementation in R
#'
#' This S4 class contains the parameters to provide for GSEA.
#'
#' @slot perm_number number of permutations for p-value calculation
#' (default: 200).
#' @slot min_sz minimum size of gene sets (number of genes in a gene set)
#' to be included in the analysis (default: 5).
#' @slot pv estimate of the variance associated with each observation
#' (default: 0).
#' @slot w1 weight 1, parameter used to calculate the prior variance
#' obtained with class size var.constant. This penalizes especially small
#' classes and small subsets. Values around 0.1 - 0.5 are expected to be
#' reasonable. (default: 0.2).
#' @slot w2 weight 2, parameter used to calculate the prior variance
#' obtained with the same class size as that of the analyzed class. This
#' penalizes small subsets from the gene list. Values around 0.3 and 0.5 are
#' expected to be reasonable (default: 0.5).
#' @slot vc size of the reference class used with wgt1. (default: 10).
#'
#' @docType methods
#' @name GSEAparams-class
#' @rdname GSEAparams-class
#' @seealso \code{\link{SEAparams-class}}
#' @seealso \code{\link{summary}}
#' @export GSEAparams
#' @examples
#' ## Lets create the default GSEAparams object.
#' myGseaParams <- GSEAparams()
#' ## Lets create another GSEAparams object with 500 permutations.
#' myGseaParams500Perms <- GSEAparams(perm_number = 500)
GSEAparams <- setClass(
Class = "GSEAparams",
slots = c(
perm_number = "numeric",
min_sz = "numeric",
pv = "numeric",
w1 = "numeric",
w2 = "numeric",
vc = "numeric"
),
prototype = list(
perm_number = 200,
min_sz = 5,
pv = 0,
w1 = 0.2,
w2 = 0.5,
vc = 10
),
validity = function(object) {
perm_number_ok <- object@perm_number > 1
min_sz_ok <- object@min_sz >= 0
pv_ok <- object@pv >= 0
w1_ok <- object@w1 >= 0
w2_ok <- object@w2 >= 0
vc_ok <- object@vc >= 0
return(perm_number_ok && min_sz_ok && pv_ok && w1_ok && w2_ok &&
vc_ok)
}
)
setClassUnion("GSEAparamsOrNULL", c("GSEAparams", "NULL"))
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