R/data.R

In ORdensity: Identification of Differentially Expressed Genes

#' @name simexpr
#' @title Simulated data with differentially expressed (DE) genes
#' @description Simulated data with 1000 genes measured under two different 
#' experimental conditions 1 and 2. 100 genes among the 1000 were generated as 
#' differentially expressed (DE) genes. The expression levels of all no DE genes 
#' were generated by \eqn{N(0,1)} distribution in both conditions 1 and 2. 
#' The DE genes were generated using the \eqn{N(0,1)} and \eqn{N(\mu_g,1)} distributions
#' for conditions 1 and 2, respectively, with \eqn{|\mu_g|=\Delta}.
#' Parameter \eqn{\Delta} sets the importance of gene \code{g}, where the bigger \eqn{\Delta} is,
#' the more important gene \code{g} is. We considered \eqn{\Delta} in \eqn{\{1.5, 2, 3\}}.
#' Each row \eqn{g} in \code{simexpr} corresponds to a simulated gene.
#'
#' @format A dataframe with 1000 rows and 62 variables:
#' \describe{
#'   \item{DEgen}{It indicates whether gene \code{g} is DE or not.}
#'   \item{gap}{It contains \eqn{\Delta} values.}
#'   \item{A1-A30}{These columns have the expression levels under experimental condition 1.}
#'   \item{B1-B30}{These columns have the expression levels under experimental condition 2.}
#' }
"simexpr"